Genetic associations of brain structural networks in schizophrenia: a preliminary study

Identification and validation of candidate clinical signatures of apolipoprotein L isoforms in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a lethal malignancy worldwide with an increasing number of new cases each year. Apolipoprotein (APOL) isoforms have been explored for their associations with HCC.The GSE14520 cohort was used for training data; The Cancer Genome Atlas (TCGA) database was used for validated data. Diagnostic, prognostic significance and mechanisms were explored using these cohorts. Risk score models and nomograms were constructed using prognosis-related isoforms and clinical factors for survival prediction. Oncomine and HCCDB databases were further used for validation of diagnostic, prognostic significance. APOL1, 3, and 6 were differentially expressed in two cohorts (all P ≤ 0.05). APOL1 and APOL6 had diagnostic capacity whereas APOL3 and APOL6 had prognostic capacity in two cohorts (areas under curves [AUCs] > 0.7, P ≤ 0.05). Mechanism studies demonstrated that APOL3 and APOL6 might be involved in humoral chemokine signaling pathways (all P ≤ 0.05). Risk score models and nomograms were constructed and validated for survival prediction of HCC. Moreover, diagnostic values of APOL1 and weak APOL6 were validated in Oncomine database (AUC > 0.700, 0.694); prognostic values of APOL3 and APOL6 were validated in HCCDB database (all P < 0.05). Differentially expressed APOL1 and APOL6 might be diagnostic biomarkers; APOL3 and APOL6 might be prognostic biomarkers of RFS and OS for HCC via chemokine signaling pathways.

MBNL1‑AS1 attenuates tumor cell proliferation by regulating the miR‑29c‑3p/BVES signal in colorectal cancer

Dysregulation of long non‑coding RNAs (lncRNAs) is involved in the development of colorectal cancer (CRC). In the present study, the identification of muscle blind like splicing regulator 1 antisense RNA 1 (MBNL1‑AS1) lncRNA was reported. Firstly, Cell Counting Kit‑8, EdU and colony formation assays were uesed to explore the role of MBNL1‑AS1 in regulating the proliferation of CRC cells. According to TCGA database, it was found that MBNL1‑AS1 was correlated with microRNA (miR)‑29c‑3p and blood vessel epicardial substance (BVES) expression in CRC cells. Then, the regulation among MBNL1‑AS1, miR‑29C‑3P and BVES was detected by dual luciferase reporter assay and the function of MBNL1‑AS1/miR‑29C‑3P/BVES axis was explored by rescue assay. The results demonstrated that MBNL1‑AS1 expression was decreased in CRC and was associated with the size of tumors derived from patients with CRC. Functionally, the upregulation of MBNL1‑AS1 suppressed CRC cell proliferation in vitro and inhibited tumor growth in vivo, while knockdown of MBNL1‑AS1 expression caused the opposite effects. MBNL1‑AS1 expression correlated with BVES expression in CRC tissues and MBNL1‑AS1 enhanced the stability of BVES mRNA by functioning as a competing endogenous RNA to sponge miR‑29c‑3p; the latter directly targeted MBNL1‑AS1 and BVES mRNA 3'UTR. Collectively, the results indicated that MBNL1‑AS1 suppressed CRC cell proliferation by regulating miR‑29c‑3p/BVES signaling, suggesting that the MBNL1‑AS1/miR‑29c‑3p/BVES axis may be a potential therapeutic target for CRC.

The Neurobiological Underpinnings of Obsessive-Compulsive Symptoms in Psychosis, Translational Issues for Treatment-Resistant Schizophrenia

Almost 25% of schizophrenia patients suffer from obsessive-compulsive symptoms (OCS) considered a transdiagnostic clinical continuum. The presence of symptoms pertaining to both schizophrenia and obsessive-compulsive disorder (OCD) may complicate pharmacological treatment and could contribute to lack or poor response to the therapy. Despite the clinical relevance, no reviews have been recently published on the possible neurobiological underpinnings of this comorbidity, which is still unclear. An integrative view exploring this topic should take into account the following aspects: (i) the implication for glutamate, dopamine, and serotonin neurotransmission as demonstrated by genetic findings; (ii) the growing neuroimaging evidence of the common brain regions and dysfunctional circuits involved in both diseases; (iii) the pharmacological modulation of dopaminergic, serotoninergic, and glutamatergic systems as current therapeutic strategies in schizophrenia OCS; (iv) the recent discovery of midbrain dopamine neurons and dopamine D1- and D2-like receptors as orchestrating hubs in repetitive and psychotic behaviors; (v) the contribution of N-methyl-D-aspartate receptor subunits to both psychosis and OCD neurobiology. Finally, we discuss the potential role of the postsynaptic density as a structural and functional hub for multiple molecular signaling both in schizophrenia and OCD pathophysiology.

Why haven't we solved the addiction crisis?

The current addiction crisis has destroyed a multitude of lives, leaving millions of fatalities worldwide in its wake. At the same time, various governmental agencies dedicated to solving this seemingly never-ending dilemma have not yet succeeded or delivered on their promises. We understand that addictive behavioral seeking is a multi-faceted neurobiological and spiritually complicated phenomenon. However, although the substitution replacement approach, especially to treat Opioid Use Disorder (OUD), has importance for harm reduction in the short term, it does not bring about a harm-free recovery or prevention. Instead, we propose a promising novel approach that uses genetic risk testing with induction of dopamine homeostasis and an objective Brain Health Check during youth. Our model involves a six-hit approach known as the "Reward Dysregulation Syndrome Solution System," which can identify addiction risk and target the root cause of addiction, dopamine dysregulation. While we applaud all past sophisticated neurogenetic and neuropharmacological research, our opinion is that in the long term, addiction scientists and clinicians might characterize preaddiction using tests; for example, administering the validated RDSQuestionarre29, genetic risk assessment, a modified brain health check, or diagnostic framing of mild to moderate Substance Use Disorder (SUD). The preaddiction concept could incentivize the development of interventions to prevent addiction from developing in the first place and target and treat neurotransmitter imbalances and other early indications of addiction. WC 222.

Reward Deficiency Syndrome (RDS) Surprisingly Is Evolutionary and Found Everywhere: Is It "Blowin' in the Wind"?

Reward Deficiency Syndrome (RDS) encompasses many mental health disorders, including a wide range of addictions and compulsive and impulsive behaviors. Described as an octopus of behavioral dysfunction, RDS refers to abnormal behavior caused by a breakdown of the cascade of reward in neurotransmission due to genetic and epigenetic influences. The resultant reward neurotransmission deficiencies interfere with the pleasure derived from satisfying powerful human physiological drives. Epigenetic repair may be possible with precision gene-guided therapy using formulations of KB220, a nutraceutical that has demonstrated pro-dopamine regulatory function in animal and human neuroimaging and clinical trials. Recently, large GWAS studies have revealed a significant dopaminergic gene risk polymorphic allele overlap between depressed and schizophrenic cohorts. A large volume of literature has also identified ADHD, PTSD, and spectrum disorders as having the known neurogenetic and psychological underpinnings of RDS. The hypothesis is that the true phenotype is RDS, and behavioral disorders are endophenotypes. Is it logical to wonder if RDS exists everywhere? Although complex, “the answer is blowin’ in the wind,” and rather than intangible, RDS may be foundational in species evolution and survival, with an array of many neurotransmitters and polymorphic loci influencing behavioral functionality.

Time to Recover From Daily Caffeine Intake

Caffeine elicits widespread effects in the central nervous system and is the most frequently consumed psychostimulant worldwide. First evidence indicates that, during daily intake, the elimination of caffeine may slow down, and the primary metabolite, paraxanthine, may accumulate. The neural impact of such adaptions is virtually unexplored. In this report, we leveraged the data of a laboratory study with N = 20 participants and three within-subject conditions: caffeine (150 mg caffeine × 3/day × 10 days), placebo (150 mg mannitol × 3/day × 10 days), and acute caffeine deprivation (caffeine × 9 days, afterward placebo × 1 day). On day 10, we determined the course of salivary caffeine and paraxanthine using liquid chromatography-mass spectrometry coupled with tandem mass spectrometry. We assessed gray matter (GM) intensity and cerebral blood flow (CBF) after acute caffeine deprivation as compared to changes in the caffeine condition from our previous report. The results indicated that levels of paraxanthine and caffeine remained high and were carried overnight during daily intake, and that the levels of paraxanthine remained elevated after 24 h of caffeine deprivation compared to placebo. After 36 h of caffeine deprivation, the previously reported caffeine-induced GM reduction was partially mitigated, while CBF was elevated compared to placebo. Our findings unveil that conventional daily caffeine intake does not provide sufficient time to clear up psychoactive compounds and restore cerebral responses, even after 36 h of abstinence. They also suggest investigating the consequences of a paraxanthine accumulation during daily caffeine intake.

Elevated endogenous GDNF induces altered dopamine signalling in mice and correlates with clinical severity in schizophrenia

Presynaptic increase in striatal dopamine is the primary dopaminergic abnormality in schizophrenia, but the underlying mechanisms are not understood. Here, we hypothesized that increased expression of endogenous GDNF could induce dopaminergic abnormalities that resemble those seen in schizophrenia. To test the impact of GDNF elevation, without inducing adverse effects caused by ectopic overexpression, we developed a novel in vivo approach to conditionally increase endogenous GDNF expression. We found that a 2-3-fold increase in endogenous GDNF in the brain was sufficient to induce molecular, cellular, and functional changes in dopamine signalling in the striatum and prefrontal cortex, including increased striatal presynaptic dopamine levels and reduction of dopamine in prefrontal cortex. Mechanistically, we identified adenosine A2a receptor (A_2AR), a G-protein coupled receptor that modulates dopaminergic signalling, as a possible mediator of GDNF-driven dopaminergic abnormalities. We further showed that pharmacological inhibition of A_2AR with istradefylline partially normalised striatal GDNF and striatal and cortical dopamine levels in mice. Lastly, we found that GDNF levels are increased in the cerebrospinal fluid of first episode psychosis patients, and in post-mortem striatum of schizophrenia patients. Our results reveal a possible contributor for increased striatal dopamine signalling in a subgroup of schizophrenia patients and suggest that GDNF-A_2AR crosstalk may regulate dopamine function in a therapeutically targetable manner.

Strategies for multivariate analyses of imaging genetics study in Alzheimer's disease

Alzheimer's disease (AD) is an incurable neurodegenerative disease primarily affecting the elderly population. Early diagnosis of AD is critical for the management of this disease. Imaging genetics examines the influence of genetic variants (i.e., single nucleotide polymorphisms (SNPs)) on brain structure and function and many novel approaches of imaging genetics are proposed for studying AD. We review and synthesize the Alzheimer's Disease Neuroimaging Initiative (ADNI) genetic associations with quantitative disease endophenotypes including structural and functional neuroimaging, diffusion tensor imaging (DTI), positron emission tomography (PET), and fluid biomarker assays. In this review, we survey recent publications using neuroimaging and genetic data of AD, with a focus on methods capturing multivariate effects accommodating the large number variables from both imaging data and genetic data. We review methods focused on bridging the imaging and genetic data by establishing genotype-phenotype association, including sparse canonical correlation analysis, parallel independent component analysis, sparse reduced rank regression, sparse partial least squares, genome-wide association study, and so on. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future pharmaceutical therapy and biomarker development.

Isoform-specific roles for AKT in affective behavior, spatial memory, and extinction related to psychiatric disorders

AKT is implicated in neurological disorders. AKT has three isoforms, AKT1/AKT2/AKT3, with brain cell type-specific expression that may differentially influence behavior. Therefore, we examined single Akt isoform, conditional brain-specific Akt1, and double Akt1/3 mutant mice in behaviors relevant to neuropsychiatric disorders. Because sex is a determinant of these disorders but poorly understood, sex was an experimental variable in our design. Our studies revealed AKT isoform- and sex-specific effects on anxiety, spatial and contextual memory, and fear extinction. In Akt1 mutant males, viral-mediated AKT1 restoration in the prefrontal cortex rescued extinction phenotypes. We identified a novel role for AKT2 and overlapping roles for AKT1 and AKT3 in long-term memory. Finally, we found that sex-specific behavior effects were not mediated by AKT expression or activation differences between sexes. These results highlight sex as a biological variable and isoform- or cell type-specific AKT signaling as potential targets for improving treatment of neuropsychiatric disorders.

A joint study of whole exome sequencing and structural MRI analysis in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a leading cause of disability worldwide and influenced by both environmental and genetic factors. Genetic studies of MDD have focused on common variants and have been constrained by the heterogeneity of clinical symptoms.

METHODS

We sequenced the exome of 77 cases and 245 controls of Han Chinese ancestry and scanned their brain. Burden tests of rare variants were performed first to explore the association between genes/pathways and MDD. Secondly, parallel Independent Component Analysis was conducted to investigate genetic underpinnings of gray matter volume (GMV) changes of MDD.

RESULTS

Two genes (CSMD1, p = 5.32×10-6; CNTNAP5, p = 1.32×10-6) and one pathway (Neuroactive Ligand Receptor Interactive, p = 1.29×10-5) achieved significance in burden test. In addition, we identified one pair of imaging-genetic components of significant correlation (r = 0.38, p = 9.92×10-6). The imaging component reflected decreased GMV in cases and correlated with intelligence quotient (IQ). IQ mediated the effects of GMV on MDD. The genetic component enriched in two gene sets, namely Singling by G-protein coupled receptors [false discovery rate (FDR) q = 3.23×10-4) and Alzheimer Disease Up (FDR q = 6.12×10-4).

CONCLUSIONS

Both rare variants analysis and imaging-genetic analysis found evidence corresponding with the neuroinflammation and synaptic plasticity hypotheses of MDD. The mediation of IQ indicates that genetic component may act on MDD through GMV alteration and cognitive impairment.

Pharmacogenetics of tardive dyskinesia in schizophrenia: The role of CHRM1 and CHRM2 muscarinic receptors

Objectives: Acetylcholine M (muscarinic) receptors are possibly involved in tardive dyskinesia (TD). The authors tried to verify this hypothesis by testing for possible associations between two muscarinic receptor genes (CHRM1 and CHRM2) polymorphisms and TD in patients with schizophrenia.Methods: A total of 472 patients with schizophrenia were recruited. TD was assessed cross-sectionally using the Abnormal Involuntary Movement Scale. Fourteen allelic variants of CHRM1 and CHRM2 were genotyped using Applied Biosystems amplifiers (USA) and the MassARRAY System by Agena Bioscience.Results: The prevalence of the rs1824024*GG genotype of the CHRM2 gene was lower in TD patients compared to the group without it (χ2 = 6.035, p = 0.049). This suggested that this genotype has a protective effect for the development of TD (OR = 0.4, 95% CI: 0.19-0.88). When age, gender, duration of schizophrenia and dosage of antipsychotic treatment were added as covariates in regression analysis, the results did not reach statistical significance.Conclusions: This study did identify associations between CHRM2 variations and TD; the results of logistic regression analysis with covariates suggest that the association is, however, likely to be secondary to other concomitant factors.

Association between the SLC6A4 gene and schizophrenia: an updated meta-analysis

BACKGROUND

In order to explore the association between the SLC6A4 gene and the risk of schizophrenia, an updated meta-analysis was conducted using a total of 46 scientific articles.

METHODS

Through a literature search, papers studied included 35 articles on serotonin-transporter-linked polymorphic region (5-HTTLPR) with 8,752 cases and 10,610 controls, 17 articles on second intron variable number of tandem repeats with 7,284 cases and 8,544 controls, four studies on rs1042173 with 1,351 cases and 2,101 controls, and four studies on rs140700 with 1,770 cases and 2,386 controls. Pooled, subgroup, and sensitivity analyses were performed, and the results were visualized by forest and funnel plots.

RESULTS

An association between 5-HTTLPR and the risk of schizophrenia was not found, except for an Indian subgroup analysis (P_z =0.014, OR =1.749, 95% CI =1.120-2.731). A 10 repeats/12 repeats (10R/12R) genotype was a protective factor against schizophrenia (P_z =0.020, OR =0.789, 95% CI =0.646-0.963), but a 12R/12R genotype was a risk factor for schizophrenia (P_z =0.004, OR =1.936, 95% CI =1.238-3.029) in the pooled analyses. In Caucasians, a GG genotype of rs1042173 may be a risk factor for schizophrenia (P_z =0.006, OR =1.299, 95% CI =1.079-1.565). No association was found between rs140700 and the risk for schizophrenia.

CONCLUSION

Through meta-analysis, we were able to gain insight into previously reported associations between SLC6A4 polymorphism and schizophrenia.

Multimodal Neuroimaging: Basic Concepts and Classification of Neuropsychiatric Diseases

Neuroimaging techniques are widely used in neuroscience to visualize neural activity, to improve our understanding of brain mechanisms, and to identify biomarkers-especially for psychiatric diseases; however, each neuroimaging technique has several limitations. These limitations led to the development of multimodal neuroimaging (MN), which combines data obtained from multiple neuroimaging techniques, such as electroencephalography, functional magnetic resonance imaging, and yields more detailed information about brain dynamics. There are several types of MN, including visual inspection, data integration, and data fusion. This literature review aimed to provide a brief summary and basic information about MN techniques (data fusion approaches in particular) and classification approaches. Data fusion approaches are generally categorized as asymmetric and symmetric. The present review focused exclusively on studies based on symmetric data fusion methods (data-driven methods), such as independent component analysis and principal component analysis. Machine learning techniques have recently been introduced for use in identifying diseases and biomarkers of disease. The machine learning technique most widely used by neuroscientists is classification-especially support vector machine classification. Several studies differentiated patients with psychiatric diseases and healthy controls with using combined datasets. The common conclusion among these studies is that the prediction of diseases increases when combining data via MN techniques; however, there remain a few challenges associated with MN, such as sample size. Perhaps in the future N-way fusion can be used to combine multiple neuroimaging techniques or nonimaging predictors (eg, cognitive ability) to overcome the limitations of MN.

A framework for linking resting-state chronnectome/genome features in schizophrenia: A pilot study

Multimodal, imaging-genomics techniques offer a platform for understanding genetic influences on brain abnormalities in psychiatric disorders. Such approaches utilize the information available from both imaging and genomics data and identify their association. Particularly for complex disorders such as schizophrenia, the relationship between imaging and genomic features may be better understood by incorporating additional information provided by advanced multimodal modeling. In this study, we propose a novel framework to combine features corresponding to functional magnetic resonance imaging (functional) and single nucleotide polymorphism (SNP) data from 61 schizophrenia (SZ) patients and 87 healthy controls (HC). In particular, the features for the functional and genetic modalities include dynamic (i.e., time-varying) functional network connectivity (dFNC) features and the SNP data, respectively. The dFNC features are estimated from component time-courses, obtained using group independent component analysis (ICA), by computing sliding-window functional network connectivity, and then estimating subject specific states from this dFNC data using a k-means clustering approach. For each subject, both the functional (dFNC states) and SNP data are selected as features for a parallel ICA (pICA) based imaging-genomic framework. This analysis identified a significant association between a SNP component (defined by large clusters of functionally related SNPs statistically correlated with phenotype components) and time-varying or dFNC component (defined by clusters of related connectivity links among distant brain regions distributed across discrete dynamic states, and statistically correlated with genomic components) in schizophrenia. Importantly, the polygenetic risk score (PRS) for SZ (computed as a linearly weighted sum of the genotype profiles with weights derived from the odds ratios of the psychiatric genomics consortium (PGC)) was negatively correlated with the significant dFNC component, which were mostly present within a state that exhibited a lower occupancy rate in individuals with SZ compared with HC, hence identifying a potential dFNC imaging biomarker for schizophrenia. Taken together, the current findings provide preliminary evidence for a link between dFNC measures and genetic risk, suggesting the application of dFNC patterns as biomarkers in imaging genetic association study.

Novel gene-brain structure relationships in psychotic disorder revealed using parallel independent component analyses

BACKGROUND

Schizophrenia, schizoaffective disorder, and psychotic bipolar disorder overlap with regard to symptoms, structural and functional brain abnormalities, and genetic risk factors. Neurobiological pathways connecting genes to clinical phenotypes across the spectrum from schizophrenia to psychotic bipolar disorder remain largely unknown.

METHODS

We examined the relationship between structural brain changes and risk alleles across the psychosis spectrum in the multi-site Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) cohort. Regional MRI brain volumes were examined in 389 subjects with a psychotic disorder (139 schizophrenia, 90 schizoaffective disorder, and 160 psychotic bipolar disorder) and 123 healthy controls. 451,701 single-nucleotide polymorphisms were screened and processed using parallel independent component analysis (para-ICA) to assess associations between genes and structural brain abnormalities in probands.

RESULTS

482 subjects were included after quality control (364 individuals with psychotic disorder and 118 healthy controls). Para-ICA identified four genetic components including several risk genes already known to contribute to schizophrenia and bipolar disorder and revealed three structural components that showed overlapping relationships with the disease risk genes across the three psychotic disorders. Functional ontologies representing these gene clusters included physiological pathways involved in brain development, synaptic transmission, and ion channel activity.

CONCLUSIONS

Heritable brain structural findings such as reduced cortical thickness and surface area in probands across the psychosis spectrum were associated with somewhat distinct genes related to putative disease pathways implicated in psychotic disorders. This suggests that brain structural alterations might represent discrete psychosis intermediate phenotypes along common neurobiological pathways underlying disease expression across the psychosis spectrum.

Multimodal fusion of brain imaging data: A key to finding the missing link(s) in complex mental illness

It is becoming increasingly clear that combining multi-modal brain imaging data is able to provide more information for individual subjects by exploiting the rich multimodal information that exists. However, the number of studies that do true multimodal fusion (i.e. capitalizing on joint information among modalities) is still remarkably small given the known benefits. In part, this is because multi-modal studies require broader expertise in collecting, analyzing, and interpreting the results than do unimodal studies. In this paper, we start by introducing the basic reasons why multimodal data fusion is important and what it can do, and importantly how it can help us avoid wrong conclusions and help compensate for imperfect brain imaging studies. We also discuss the challenges that need to be confronted for such approaches to be more widely applied by the community. We then provide a review of the diverse studies that have used multimodal data fusion (primarily focused on psychosis) as well as provide an introduction to some of the existing analytic approaches. Finally, we discuss some up-and-coming approaches to multi-modal fusion including deep learning and multimodal classification which show considerable promise. Our conclusion is that multimodal data fusion is rapidly growing, but it is still underutilized. The complexity of the human brain coupled with the incomplete measurement provided by existing imaging technology makes multimodal fusion essential in order to mitigate against misdirection and hopefully provide a key to finding the missing link(s) in complex mental illness.

Phosphoinositide-Dependent Protein Kinase 1 (PDK1)

Abstract. Phosphatidylinositol-3-kinase (PI3K) signaling influences susceptibility to virus infections, anoxia, obstetric complications, and cancer; which are changed in patients with schizophrenia and their first degree relatives. Therefore PI3K signaling might have impact on the pathophysiology of schizophrenia. PI3K signaling crucially involves phosphoinositide-dependent protein kinase (PDK1). Increased anxiety behavior is observed in PDK1 hypomorphic mice. Here we show enhanced prevalence of schizophrenia in carriers of the PDK1 CC genotype in human beings. Moreover, decreased parietal P300 amplitude, which is a well-studied schizophrenic endophenotype, was observed in PDK1 CC carriers. Glutamate and glutamine concentrations are increased in the frontal lobe of PDK1 dysmorphic mice and human CC individuals. Our results demonstrate that the PDK1 CC genotype is associated with increased risk to develop schizophrenia, a typical endophenotype profile observed in the disease and modified neurotransmitter concentrations in brain regions associated with the disease.

Is there a role for ADORA2A polymorphisms in levodopa-induced dyskinesia in Parkinson's disease patients?

AIM

Levodopa is first line treatment of Parkinson's disease (PD). However, its use is associated with the presence of motor fluctuations and dyskinesias. In recent years, adenosine A2A receptor (A2AR) is rising as a therapeutic target for PD. The aim of the present study was to investigate whether ADORA2A is associated with levodopa adverse effects.

PATIENTS & METHODS

Two hundred and eight PD patients on levodopa therapy were investigated. rs2298383 and rs3761422 at the ADORA2A gene were genotyped by allelic discrimination assays.

RESULTS

A trend for association was observed for both polymorphism and diplotypes with dyskinesia.

CONCLUSION

The present results should be considered as positive preliminary evidence. Further studies are needed to determine the association between ADORA2A and dyskinesia. Original submitted 3 December 2014; Revision submitted 13 February 2015.

Dopamine D2 receptor blocker thioridazine induces cell death in human uterine cervical carcinoma cell line SiHa

AIM

The aim of this study was to explore the correlation of dopamine D2 receptor (DRD2) and the development of uterine cervical cancer, and the effect of thioridazine (an antagonist of DRD2) on the SiHa cell line.

MATERIAL AND METHODS

The expression of DRD2 in tissues was detected with immunohistochemistry. SiHa cells were exposed to different concentrations of thioridazine for 24 h, and then cell viability was determined. After 20-μM thioridazine treatment for 24 h, the protein level of DRD2 in SiHa cells was analyzed by Western blots, apoptosis was detected with the phosphatidylserine externalization and comet assay, and necrosis was detected by measuring high-mobility group box 1 protein (HMGB1).

RESULTS

The expression of DRD2 gradually increased from normal to cancer tissues (P < 0.01). In vitro, DRD2 blocker thioridazine treatment resulted in death of SiHa cells with the expression of DRD2 significantly regulated down (P < 0.05), and thioridazine significantly induced SiHa apoptosis (P = 0.016) and necrosis (P < 0.01).

CONCLUSION

Higher DRD2 expression is closely associated with cervical cancer progression. After blocking DRD2, SiHa cell growth is significantly suppressed, indicating that DRD2 may function as a novel tumor marker and a potential therapeutic target for cervical cancer.

Hepatocyte nuclear factor 4 alpha is a key factor related to depression and physiological homeostasis in the mouse brain

Major depressive disorder (MDD) is a common psychiatric disorder that involves marked disabilities in global functioning, anorexia, and severe medical comorbidities. MDD is associated with not only psychological and sociocultural problems, but also pervasive physical dysfunctions such as metabolic, neurobiological and immunological abnormalities. Nevertheless, the mechanisms underlying the interactions between these factors have yet to be determined in detail. The aim of the present study was to identify the molecular mechanisms responsible for the interactions between MDD and dysregulation of physiological homeostasis, including immunological function as well as lipid metabolism, coagulation, and hormonal activity in the brain. We generated depression-like behavior in mice using chronic mild stress (CMS) as a model of depression. We compared the gene expression profiles in the prefrontal cortex (PFC) of CMS and control mice using microarrays. We subsequently categorized genes using two web-based bioinformatics applications: Ingenuity Pathway Analysis and The Database for Annotation, Visualization, and Integrated Discovery. We then confirmed significant group-differences by analyzing mRNA and protein expression levels not only in the PFC, but also in the thalamus and hippocampus. These web tools revealed that hepatocyte nuclear factor 4 alpha (Hnf4a) may exert direct effects on various genes specifically associated with amine synthesis, such as genes involved in serotonin metabolism and related immunological functions. Moreover, these genes may influence lipid metabolism, coagulation, and hormonal activity. We also confirmed the significant effects of Hnf4a on both mRNA and protein expression levels in the brain. These results suggest that Hnf4a may have a critical influence on physiological homeostasis under depressive states, and may be associated with the mechanisms responsible for the interactions between MDD and the dysregulation of physiological homeostasis in humans.

The association of ADORA2A and ADORA2B polymorphisms with the risk and severity of chronic heart failure: a case-control study of a northern Chinese population

The causes of chronic heart failure (CHF) and its progression are likely to be due to complex genetic factors. Adenosine receptors A2A and A2B (ADORA2A and ADORA2B, respectively) play an important role in cardio-protection. Therefore, polymorphisms in the genes encoding those receptors may affect the risk and severity of CHF. This study was a case-control comparative investigation of 300 northern Chinese Han CHF patients and 400 ethnicity-matched healthy controls. Four common single-nucleotide polymorphisms (SNPs) of ADORA2A (rs2236625, rs2236624, rs4822489, and rs5751876) and one SNP of ADORA2B (rs7208480) were genotyped and an association between SNPs and clinical outcomes was evaluated. Odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess the association. The rs4822489 was significantly associated with the severity of CHF after adjustment for traditional cardiovascular risk factors (p = 0.040, OR = 1.912, 95% CI = 1.029–3.550). However, the five SNPs as well as the haplotypes were not found to be associated with CHF susceptibility. The findings of this study suggest that rs4822489 may contribute to the severity of CHF in the northern Chinese. However, further studies performed in larger populations and aimed at better defining the role of this gene are required.

Genetic underpinnings of white matter 'connectivity': heritability, risk, and heterogeneity in schizophrenia

Schizophrenia is a highly heritable disorder. Thus, the combination of genetics and brain imaging may be a useful strategy to investigate the effects of risk genes on anatomical connectivity, and for gene discovery, i.e. discovering the genetic correlates of white matter phenotypes. Following a database search, I review evidence for heritability of white matter phenotypes. I also review candidate gene investigations, examining association of putative risk variants with white matter phenotypes, as well as the recent flurry of research exploring relationships of genome-wide significant risk loci with white matter phenotypes. Finally, I review multivariate and polygene approaches, which constitute a new wave of imaging-genetics research, including large collaborative initiatives aiming to discover new genes that may predict aspects of white matter microstructure. The literature supports the heritability of white matter phenotypes. Loci in genes intimately implicated in oligodendrocyte and myelin development, growth and maintenance, and neurotrophic systems are associated with white matter microstructure. GWAS variants have not yet sufficiently been explored using DTI-based evaluation of white matter to draw conclusions, although micro-RNA 137 is promising due to its potential regulation of other GWAS schizophrenia genes. Many imaging-genetic studies only include healthy participants, which, while helping control for certain confounds, cannot address questions related to disease heterogeneity or symptom expression, and thus more studies should include participants with schizophrenia. With sufficiently large sample sizes, the future of this field lies in polygene strategies aimed at risk prediction and heterogeneity dissection of schizophrenia that can translate to personalized interventions.

Imaging proteomics for diagnosis, monitoring and prediction of Alzheimer's disease

Proteomic and imaging markers have been widely studied as potential biomarkers for diagnosis, monitoring and prognosis of Alzheimer's disease. In this study, we used Alzheimer Disease Neuroimaging Initiative dataset and performed parallel independent component analysis on cross sectional and longitudinal proteomic and imaging data in order to identify the best proteomic model for diagnosis, monitoring and prediction of Alzheimer disease (AD). We used plasma proteins measurement and imaging data from AD and healthy controls (HC) at the baseline and 1 year follow-up. Group comparisons at baseline and changes over 1 year were calculated for proteomic and imaging data. The results were fed into parallel independent component analysis in order to identify proteins that were associated with structural brain changes cross sectionally and longitudinally. Regression model was used to find the best model that can discriminate AD from HC, monitor AD and to predict MCI converters from non-converters. We showed that five proteins are associated with structural brain changes in the brain. These proteins could discriminate AD from HC with 57% specificity and 89% sensitivity. Four proteins whose change over 1 year were associated with brain structural changes could discriminate AD from HC with sensitivity of 93%, and specificity of 92%. This model predicted MCI conversion to AD in 2 years with 94% accuracy. This model has the highest accuracy in prediction of MCI conversion to AD within the ADNI-1 dataset. This study shows that combination of selected plasma protein levels and MR imaging is a useful method in identifying potential biomarker.

A review of multivariate analyses in imaging genetics

Recent advances in neuroimaging technology and molecular genetics provide the unique opportunity to investigate genetic influence on the variation of brain attributes. Since the year 2000, when the initial publication on brain imaging and genetics was released, imaging genetics has been a rapidly growing research approach with increasing publications every year. Several reviews have been offered to the research community focusing on various study designs. In addition to study design, analytic tools and their proper implementation are also critical to the success of a study. In this review, we survey recent publications using data from neuroimaging and genetics, focusing on methods capturing multivariate effects accommodating the large number of variables from both imaging data and genetic data. We group the analyses of genetic or genomic data into either a priori driven or data driven approach, including gene-set enrichment analysis, multifactor dimensionality reduction, principal component analysis, independent component analysis (ICA), and clustering. For the analyses of imaging data, ICA and extensions of ICA are the most widely used multivariate methods. Given detailed reviews of multivariate analyses of imaging data available elsewhere, we provide a brief summary here that includes a recently proposed method known as independent vector analysis. Finally, we review methods focused on bridging the imaging and genetic data by establishing multivariate and multiple genotype-phenotype-associations, including sparse partial least squares, sparse canonical correlation analysis, sparse reduced rank regression and parallel ICA. These methods are designed to extract latent variables from both genetic and imaging data, which become new genotypes and phenotypes, and the links between the new genotype-phenotype pairs are maximized using different cost functions. The relationship between these methods along with their assumptions, advantages, and limitations are discussed.

Function-structure associations of the brain: evidence from multimodal connectivity and covariance studies

Despite significant advances in multimodal imaging techniques and analysis approaches, unimodal studies are still the predominant way to investigate brain changes or group differences, including structural magnetic resonance imaging (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI) and electroencephalography (EEG). Multimodal brain studies can be used to understand the complex interplay of anatomical, functional and physiological brain alterations or development, and to better comprehend the biological significance of multiple imaging measures. To examine the function-structure associations of the brain in a more comprehensive and integrated manner, we reviewed a number of multimodal studies that combined two or more functional (fMRI and/or EEG) and structural (sMRI and/or DTI) modalities. In this review paper, we specifically focused on multimodal neuroimaging studies on cognition, aging, disease and behavior. We also compared multiple analysis approaches, including univariate and multivariate methods. The possible strengths and limitations of each method are highlighted, which can guide readers when selecting a method based on a given research question. In particular, we believe that multimodal fusion approaches will shed further light on the neuronal mechanisms underlying the major structural and functional pathophysiological features of both the healthy brain (e.g. development) or the diseased brain (e.g. mental illness) and, in the latter case, may provide a more sensitive measure than unimodal imaging for disease classification, e.g. multimodal biomarkers, which potentially can be used to support clinical diagnosis based on neuroimaging techniques.

Genetic influences of resting state fMRI activity in language-related brain regions in healthy controls and schizophrenia patients: a pilot study

Individuals with schizophrenia show a broad range of language impairments, similar to those observed in reading disability (RD). Genetic linkage and association studies of RD have identified a number of candidate RD-genes that are associated with neuronal migration. Some individuals with schizophrenia also show evidence of impaired cortical neuronal migration. We have previously linked RD-related genes with gray matter distributions in healthy controls and schizophrenia. The aim of the current study was to extend these structural findings and to examine links between putative RD-genes and functional connectivity of language-related regions in healthy controls (n = 27) and schizophrenia (n = 28). Parallel independent component analysis (parallel-ICA) was used to examine the relationship between language-related regions extracted from resting-state fMRI and 16 single nucleotide polymorphisms (SNPs) spanning 5 RD-related genes. Parallel-ICA identified four significant fMRI-SNP relationships. A Left Broca-Superior/Inferior Parietal network was related to two KIAA0319 SNPs in controls but not in schizophrenia. For both diagnostic groups, a Broca-Medial Parietal network was related to two DCDC2 SNPs, while a Left Wernicke-Fronto-Occipital network was related to two KIAA0319 SNPs. A Bilateral Wernicke-Fronto-Parietal network was related to one KIAA0319 SNP only in controls. Thus, RD-genes influence functional connectivity in language-related regions, but no RD-gene uniquely affected network function in schizophrenia as compared to controls. This is in contrast with our previous study where RD-genes affected gray matter distribution in some structural networks in schizophrenia but not in controls. Thus these RD-genes may exert a more important influence on structure rather than function of language-related networks in schizophrenia.

Heritability of multivariate gray matter measures in schizophrenia

Structural brain measures are employed as endophenotypes in the search for schizophrenia susceptibility genes. We analyzed two independent structural imaging datasets with voxel-based morphometry and with source-based morphometry, a multivariate, independent components analysis, to determine the stability and heritability of regional gray matter concentration abnormalities in schizophrenia. The samples comprised 209 and 102 patients with schizophrenia and 208 and 96 healthy volunteers, respectively. The second sample additionally included non-ill siblings of participants with and without schizophrenia. A standard voxel-based analysis showed reproducible regional gray matter deficits in the affected participants compared with unrelated, unaffected controls in both datasets: patients showed significant gray matter concentration deficits in cortical frontal, temporal, and insular lobes. Source-based morphometry (SBM) was applied to the gray matter images of the entire sample to determine the effects of diagnosis on networks of covarying structures. The SBM analysis extracted 24 significant sets of covarying regions (components). Four of these components showed significantly lower gray matter concentrations in patients (p < .05). We determined the familiality of the observed SBM components based on 66 sibling pairs (25 discordant for schizophrenia). Two components, one including the medial frontal, insular, inferior frontal, and temporal lobes, and the other including the posterior occipital lobe, showed significant familiality (p < .05). We conclude that structural brain deficits in schizophrenia are replicable, and that SBM can extract unique familial and likely heritable components. SBM provides a useful data reduction technique that can provide measures that may serve as endophenotypes for schizophrenia.

Differential effects of ADORA2A gene variations in pre-attentive visual sensory memory subprocesses

The ADORA2A gene encodes the adenosine A(2A) receptor that is highly expressed in the striatum where it plays a role in modulating glutamatergic and dopaminergic transmission. Glutamatergic signaling has been suggested to play a pivotal role in cognitive functions related to the pre-attentive processing of external stimuli. Yet, the precise molecular mechanism of these processes is poorly understood. Therefore, we aimed to investigate whether ADORA2A gene variation has modulating effects on visual pre-attentive sensory memory processing. Studying two polymorphisms, rs5751876 and rs2298383, in 199 healthy control subjects who performed a partial-report paradigm, we find that ADORA2A variation is associated with differences in the efficiency of pre-attentive sensory memory sub-processes. We show that especially the initial visual availability of stimulus information is rendered more efficiently in the homozygous rare genotype groups. Processes related to the transfer of information into working memory and the duration of visual sensory (iconic) memory are compromised in the homozygous rare genotype groups. Our results show a differential genotype-dependent modulation of pre-attentive sensory memory sub-processes. Hence, we assume that this modulation may be due to differential effects of increased adenosine A(2A) receptor signaling on glutamatergic transmission and striatal medium spiny neuron (MSN) interaction.

Neuroglialpharmacology: myelination as a shared mechanism of action of psychotropic treatments

Current psychiatric diagnostic schema segregate symptom clusters into discrete entities, however, large proportions of patients suffer from comorbid conditions that fit neither diagnostic nor therapeutic schema. Similarly, psychotropic treatments ranging from lithium and antipsychotics to serotonin reuptake inhibitors (SSRIs) and acetylcholinesterase inhibitors have been shown to be efficacious in a wide spectrum of psychiatric disorders ranging from autism, schizophrenia (SZ), depression, and bipolar disorder (BD) to Alzheimer's disease (AD). This apparent lack of specificity suggests that psychiatric symptoms as well as treatments may share aspects of pathophysiology and mechanisms of action that defy current symptom-based diagnostic and neuron-based therapeutic schema. A myelin-centered model of human brain function can help integrate these incongruities and provide novel insights into disease etiologies and treatment mechanisms. Available data are integrated herein to suggest that widely used psychotropic treatments ranging from antipsychotics and antidepressants to lithium and electroconvulsive therapy share complex signaling pathways such as Akt and glycogen synthase kinase-3 (GSK3) that affect myelination, its plasticity, and repair. These signaling pathways respond to neurotransmitters, neurotrophins, hormones, and nutrition, underlie intricate neuroglial communications, and may substantially contribute to the mechanisms of action and wide spectra of efficacy of current therapeutics by promoting myelination. Imaging and genetic technologies make it possible to safely and non-invasively test these hypotheses directly in humans and can help guide clinical trial efforts designed to correct myelination abnormalities. Such efforts may provide insights into novel avenues for treatment and prevention of some of the most prevalent and devastating human diseases.

A selective review of multimodal fusion methods in schizophrenia

Schizophrenia (SZ) is one of the most cryptic and costly mental disorders in terms of human suffering and societal expenditure (van Os and Kapur, 2009). Though strong evidence for functional, structural, and genetic abnormalities associated with this disease exists, there is yet no replicable finding which has proven accurate enough to be useful in clinical decision making (Fornito et al., 2009), and its diagnosis relies primarily upon symptom assessment (Williams et al., 2010a). It is likely in part that the lack of consistent neuroimaging findings is because most models favor only one data type or do not combine data from different imaging modalities effectively, thus missing potentially important differences which are only partially detected by each modality (Calhoun et al., 2006a). It is becoming increasingly clear that multimodal fusion, a technique which takes advantage of the fact that each modality provides a limited view of the brain/gene and may uncover hidden relationships, is an important tool to help unravel the black box of schizophrenia. In this review paper, we survey a number of multimodal fusion applications which enable us to study the schizophrenia macro-connectome, including brain functional, structural, and genetic aspects and may help us understand the disorder in a more comprehensive and integrated manner. We also provide a table that characterizes these applications by the methods used and compare these methods in detail, especially for multivariate models, which may serve as a valuable reference that helps readers select an appropriate method based on a given research question.

A large scale multivariate parallel ICA method reveals novel imaging-genetic relationships for Alzheimer's disease in the ADNI cohort

The underlying genetic etiology of late onset Alzheimer's disease (LOAD) remains largely unknown, likely due to its polygenic architecture and a lack of sophisticated analytic methods to evaluate complex genotype-phenotype models. The aim of the current study was to overcome these limitations in a bi-multivariate fashion by linking intermediate magnetic resonance imaging (MRI) phenotypes with a genome-wide sample of common single nucleotide polymorphism (SNP) variants. We compared associations between 94 different brain regions of interest derived from structural MRI scans and 533,872 genome-wide SNPs using a novel multivariate statistical procedure, parallel-independent component analysis, in a large, national multi-center subject cohort. The study included 209 elderly healthy controls, 367 subjects with amnestic mild cognitive impairment and 181 with mild, early-stage LOAD, all of them Caucasian adults, from the Alzheimer's Disease Neuroimaging Initiative cohort. Imaging was performed on comparable 1.5 T scanners at over 50 sites in the USA/Canada. Four primary "genetic components" were associated significantly with a single structural network including all regions involved neuropathologically in LOAD. Pathway analysis suggested that each component included several genes already known to contribute to LOAD risk (e.g. APOE4) or involved in pathologic processes contributing to the disorder, including inflammation, diabetes, obesity and cardiovascular disease. In addition significant novel genes identified included ZNF673, VPS13, SLC9A7, ATP5G2 and SHROOM2. Unlike conventional analyses, this multivariate approach identified distinct groups of genes that are plausibly linked in physiologic pathways, perhaps epistatically. Further, the study exemplifies the value of this novel approach to explore large-scale data sets involving high-dimensional gene and endophenotype data.

A review of multivariate methods for multimodal fusion of brain imaging data

The development of various neuroimaging techniques is rapidly improving the measurements of brain function/structure. However, despite improvements in individual modalities, it is becoming increasingly clear that the most effective research approaches will utilize multi-modal fusion, which takes advantage of the fact that each modality provides a limited view of the brain. The goal of multi-modal fusion is to capitalize on the strength of each modality in a joint analysis, rather than a separate analysis of each. This is a more complicated endeavor that must be approached more carefully and efficient methods should be developed to draw generalized and valid conclusions from high dimensional data with a limited number of subjects. Numerous research efforts have been reported in the field based on various statistical approaches, e.g. independent component analysis (ICA), canonical correlation analysis (CCA) and partial least squares (PLS). In this review paper, we survey a number of multivariate methods appearing in previous multimodal fusion reports, mostly fMRI with other modality, which were performed with or without prior information. A table for comparing optimization assumptions, purpose of the analysis, the need of priors, dimension reduction strategies and input data types is provided, which may serve as a valuable reference that helps readers understand the trade-offs of the 7 methods comprehensively. Finally, we evaluate 3 representative methods via simulation and give some suggestions on how to select an appropriate method based on a given research.

Multilocus genetic analysis of brain images

The quest to identify genes that influence disease is now being extended to find genes that affect biological markers of disease, or endophenotypes. Brain images, in particular, provide exquisitely detailed measures of anatomy, function, and connectivity in the living brain, and have identified characteristic features for many neurological and psychiatric disorders. The emerging field of imaging genomics is discovering important genetic variants associated with brain structure and function, which in turn influence disease risk and fundamental cognitive processes. Statistical approaches for testing genetic associations are not straightforward to apply to brain images because the data in brain images is spatially complex and generally high dimensional. Neuroimaging phenotypes typically include 3D maps across many points in the brain, fiber tracts, shape-based analyses, and connectivity matrices, or networks. These complex data types require new methods for data reduction and joint consideration of the image and the genome. Image-wide, genome-wide searches are now feasible, but they can be greatly empowered by sparse regression or hierarchical clustering methods that isolate promising features, boosting statistical power. Here we review the evolution of statistical approaches to assess genetic influences on the brain. We outline the current state of multivariate statistics in imaging genomics, and future directions, including meta-analysis. We emphasize the power of novel multivariate approaches to discover reliable genetic influences with small effect sizes.

Genetic influences of cortical gray matter in language-related regions in healthy controls and schizophrenia

Individuals with schizophrenia show a broad range of language impairments, including reading difficulties. A recent structural MRI (sMRI) study linked these difficulties to structural abnormalities in language-related regions (Leonard et al., 2008). Similar regions have been implicated in primary reading disability (RD). Major hypotheses of RD implicate abnormal embryonic neuronal migration in the cortex, and genetic linkage and association studies have identified a number of candidate RD genes that are associated with neuronal migration (Paracchini et al., 2007). Interestingly, evidence suggests at least some individuals with schizophrenia also show impaired neuronal migration in the cortex (Akbarian et al., 1996). Thus the aim of this study was to examine the link between RD-related genes and gray matter volumes in healthy controls and schizophrenia. We used parallel independent component analysis (parallel-ICA) to examine the relationship between gray matter volumes extracted using voxel-based morphometry (VBM) and 16 single nucleotide polymorphisms (SNPs) spanning FOXP2 and four RD-related genes, DCDC2, DYX1C1, KIAA0319 and TTRAP. Parallel-ICA identified five sMRI-SNP relationships. Superior and inferior cerebellar networks were related to DYX1C1 and DCDC2/KIAA0319 respectively in both groups. The superior prefrontal, temporal and occipital networks were positively related to DCDC2 in the schizophrenia, but not the control group. The identified networks closely correspond to the known distribution of language processes in the cortex. Thus, reading and language difficulties in schizophrenia may be related to distributed cortical structural abnormalities associated with RD-related genes.

Voxelwise gene-wide association study (vGeneWAS): multivariate gene-based association testing in 731 elderly subjects

Imaging traits provide a powerful and biologically relevant substrate to examine the influence of genetics on the brain. Interest in genome-wide, brain-wide search for influential genetic variants is growing, but has mainly focused on univariate, SNP-based association tests. Moving to gene-based multivariate statistics, we can test the combined effect of multiple genetic variants in a single test statistic. Multivariate models can reduce the number of statistical tests in gene-wide or genome-wide scans and may discover gene effects undetectable with SNP-based methods. Here we present a gene-based method for associating the joint effect of single nucleotide polymorphisms (SNPs) in 18,044 genes across 31,662 voxels of the whole brain in 731 elderly subjects (mean age: 75.56±6.82SD years; 430 males) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Structural MRI scans were analyzed using tensor-based morphometry (TBM) to compute 3D maps of regional brain volume differences compared to an average template image based on healthy elderly subjects. Using the voxel-level volume difference values as the phenotype, we selected the most significantly associated gene (out of 18,044) at each voxel across the brain. No genes identified were significant after correction for multiple comparisons, but several known candidates were re-identified, as were other genes highly relevant to brain function. GAB2, which has been previously associated with late-onset AD, was identified as the top gene in this study, suggesting the validity of the approach. This multivariate, gene-based voxelwise association study offers a novel framework to detect genetic influences on the brain.