A molecular pathway analysis informs the genetic background at risk for schizophrenia

Transcriptional Regulators in the Cerebellum in Chronic Schizophrenia: Novel Possible Targets for Pharmacological Interventions

Despite the emerging evidence of the role of transcriptional regulators in schizophrenia as key molecular effectors responsible for the dysregulation of multiple biological processes, limited information is available for brain areas that control higher cognitive functions, such as the cerebellum. To identify transcription factors that could control a wide panel of altered proteins in the cerebellar cortex in schizophrenia, we analyzed a dataset obtained using one-shot liquid chromatography-tandem mass spectrometry on the postmortem human cerebellar cortex in chronic schizophrenia (PXD024937 identifier in the ProteomeXchange repository). Our analysis revealed a panel of 11 enriched transcription factors (SP1, KLF7, SP4, EGR1, HNF4A, CTCF, GABPA, NRF1, NFYA, YY1, and MEF2A) that could be controlling 250 altered proteins. The top three significantly enriched transcription factors were SP1, YY1, and EGR1, and the transcription factors with the largest number of targets were SP1, KLF7, and SP4 which belong to the Krüppel superfamily. An enrichment in vesicle-mediated transport was found for SP1, KLF7, EGR1, HNF4A, CTCF, and MEF2A targets, while pathways related to signaling, inflammation/immune responses, apoptosis, and energy were found for SP1 and KLF7 targets. EGR1 targets were enriched in RNA processing, and GABPA and YY1 targets were mainly involved in organelle organization and assembly. This study provides a reduced panel of transcriptional regulators that could impact multiple pathways through the control of a number of targets in the cerebellum in chronic schizophrenia. These findings suggest that this panel of transcription factors could represent key targets for pharmacological interventions in schizophrenia.

DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction

BACKGROUND

Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine learning methods can be applied for phenotype prediction at different scales, but due to the black-box nature of machine learning, integrating these modalities and interpreting biological mechanisms can be challenging. Additionally, the partial availability of these multimodal data presents a challenge in developing these predictive models.

METHOD

To address these challenges, we developed DeepGAMI, an interpretable neural network model to improve genotype-phenotype prediction from multimodal data. DeepGAMI leverages functional genomic information, such as eQTLs and gene regulation, to guide neural network connections. Additionally, it includes an auxiliary learning layer for cross-modal imputation allowing the imputation of latent features of missing modalities and thus predicting phenotypes from a single modality. Finally, DeepGAMI uses integrated gradient to prioritize multimodal features for various phenotypes.

RESULTS

We applied DeepGAMI to several multimodal datasets including genotype and bulk and cell-type gene expression data in brain diseases, and gene expression and electrophysiology data of mouse neuronal cells. Using cross-validation and independent validation, DeepGAMI outperformed existing methods for classifying disease types, and cellular and clinical phenotypes, even using single modalities (e.g., AUC score of 0.79 for Schizophrenia and 0.73 for cognitive impairment in Alzheimer's disease).

CONCLUSION

We demonstrated that DeepGAMI improves phenotype prediction and prioritizes phenotypic features and networks in multiple multimodal datasets in complex brains and brain diseases. Also, it prioritized disease-associated variants, genes, and regulatory networks linked to different phenotypes, providing novel insights into the interpretation of gene regulatory mechanisms. DeepGAMI is open-source and available for general use.

Counting the Toll of Inflammation on Schizophrenia-A Potential Role for Toll-like Receptors

Toll-like receptors (TLRs) are a family of pattern recognition receptors (PRRs) that are ubiquitously expressed in the human body. They protect the brain and central nervous system from self and foreign antigens/pathogens. The immune response elicited by these receptors culminates in the release of cytokines, chemokines, and interferons causing an inflammatory response, which can be both beneficial and harmful to neurodevelopment. In addition, the detrimental effects of TLR activation have been implicated in multiple neurodegenerative diseases such as Alzheimer's, multiple sclerosis, etc. Many studies also support the theory that cytokine imbalance may be involved in schizophrenia, and a vast amount of literature showcases the deleterious effects of this imbalance on cognitive performance in the human population. In this review, we examine the current literature on TLRs, their potential role in the pathogenesis of schizophrenia, factors affecting TLR activity that contribute towards the risk of schizophrenia, and lastly, the role of TLRs and their impact on cognitive performance in schizophrenia.

The relationship between TLR4/NF-κB/IL-1β signaling, cognitive impairment, and white-matter integrity in patients with stable chronic schizophrenia

OBJECTIVE

Previous studies have implicated intricate interactions between innate immunity and the brain in schizophrenia. Monocytic Toll-like receptor (TLR) 4 signaling, a crucial "sensor" of innate immunity, was reported to be over-activated in link with cognitive impairment in schizophrenia. As TLR4 is predominantly expressed on gliocytes prior to expression in neurons, we hypothesized that higher TLR4 levels may contribute to cognitive deterioration by affecting white matter microstructure.

METHODS

Forty-four patients with stable chronic schizophrenia (SCS) and 59 healthy controls (HCs) were recruited in this study. The monocytic function was detected with lipopolysaccharide (LPS) stimulation to simulate bacterial infection. Basal and LPS- stimulated levels of TLR4, nuclear factor-kappa B (NF-κB), and interleukin (IL)-1β were quantified with flow cytometry. Cognitive function was assessed by the MATRICS Consensus Cognitive Battery (MCCB) and psychopathological symptoms were evaluated by the Positive and Negative Syndrome Scale (PANSS). We employed diffusion tensor imaging with a 3-T scanner and evaluated white-matter integrity with fractional anisotropy (FA). Subcortical volume and cortical thickness were also assessed.

RESULTS

The TLR4/NF-κB/IL-1β signaling pathway was activated in patients with SCS, but responded sluggishly to LPS stimulation when compared with HCs. Furthermore, monocytic TLR4 expressions were inversely correlated with cognitive function and white matter FA, but not with cortical thickness or subcortical gray matter volume in schizophrenia.

CONCLUSION

Our findings support altered TLR4 signaling pathway activity in association with deficits in cognition and white matter integrity in schizophrenia.

Mitogen-activated protein kinase (MAPK) signalling corresponds with distinct behavioural profiles in a rat model of maternal immune activation

Dysfunction within the mitogen-activated protein kinase (MAPK) cascade has been recognised as a pathological feature of schizophrenia, however the possible mechanistic connection to the disease phenotype remains unexplored. Using the maternal immune activation (MIA) rat model of schizophrenia, the present study investigated the involvement of prefrontal cortex (PFC) MAPK in sensorimotor gating and adaptive learning deficits via western blot, pre-pulse inhibition (PPI) testing, and a contingency degradation operant task, respectively. Principle findings identified a negative relationship between basal MAPK expression and PPI exclusively in MIA rats, suggesting a modulatory role for MAPK in sensorimotor gating pathology. In addition, the correlation between MAPK and adaptive learning capacity observed in control rats was absent for rats exposed to MIA. Findings are considered with respect to the glutamatergic NMDA hypofunction theory of schizophrenia, as well as the critical role of PFC in contingency learning.

More dampened monocytic Toll-like receptor 4 response to lipopolysaccharide and its association with cognitive function in Chinese Han first-episode patients with schizophrenia

OBJECTIVE

Accumulating evidence suggests alterations of the innate immune system are related to schizophrenia, although the precise mechanism remains to be elucidated. In this study, we aimed to detect the monocytic toll-like receptor 4 (TLR4) expression under basal and lipopolysaccharide (LPS)-stimulated conditions in first-episode (FE) Han Chinese patients with schizophrenia, as well as its association with cognitive function.

METHODS

Whole blood samples were taken in 42 FE schizophrenia patients and 36 healthy controls. Expressions of TLR4 on monocytes under basal and LPS-stimulated conditions were measured with flow cytometry. Psychopathological symptoms of schizophrenia were assessed by the Positive and Negative Syndrome Scale (PANSS) and the MATRICS Consensus Cognitive Battery (MCCB) was administered to all of the participants.

RESULTS

We found no differences in percentage and mean fluorescence intensity (MFI) of TLR4 expression on monocytes between patients and controls at basal status. However, LPS challenge resulted in a lower cell-surface level of TLR4 on monocytes in FE schizophrenia patients as compared to healthy controls (TLR4+%: F = 4.092, p = 0.047; TLR4 + MFI: F = 4.820, p = 0.031). In addition, correlation analysis together with multivariate linear regression analysis identified basal percentage of TLR4 in monocytes as the beneficial factor for visual learning and working memory in FE patients with schizophrenia.

CONCLUSIONS

Our findings suggested that TLR4 may be involved in the pathophysiology of schizophrenia, corroborating the role of innate immunity-related functional deficits in increased risk of schizophrenia.

Machine learning in schizophrenia genomics, a case-control study using 5,090 exomes

Our hypothesis is that machine learning (ML) analysis of whole exome sequencing (WES) data can be used to identify individuals at high risk for schizophrenia (SCZ). This study applies ML to WES data from 2,545 individuals with SCZ and 2,545 unaffected individuals, accessed via the database of genotypes and phenotypes (dbGaP). Single nucleotide variants and small insertions and deletions were annotated by ANNOVAR using the reference genome hg19/GRCh37. Rare (predicted functional) variants with a minor allele frequency ≤1% and genotype quality ≥90 including missense, frameshift, stop gain, stop loss, intronic, and exonic splicing variants were selected. A file containing all cases and controls, the names of genes with variants meeting our criteria, and the number of variants per gene for each individual, was used for ML analysis. The supervised machine-learning algorithm used the patterns of variants observed in the different genes to determine which subset of genes can best predict that an individual is affected. Seventy percent of the data was used to train the algorithm and the remaining 30% of data (n = 1,526) was used to evaluate its efficiency. The supervised ML algorithm, gradient boosted trees with regularization (eXtreme Gradient Boosting implementation) was the best performing algorithm yielding promising results (accuracy: 85.7%, specificity: 86.6%, sensitivity: 84.9%, area under the receiver-operator characteristic curve: 0.95). The top 50 features (genes) of the algorithm were analyzed using bioinformatics resources for new insights about the pathophysiology of SCZ. This manuscript presents a novel predictor which could potentially enable studies exploring disease-modifying intervention in the early stages of the disease.

An increase in plasma brain derived neurotrophic factor levels is related to n-3 polyunsaturated fatty acid efficacy in first episode schizophrenia: secondary outcome analysis of the OFFER randomized clinical trial

RATIONALE

N-3 polyunsaturated fatty acids (n-3 PUFA) influence multiple biochemical mechanisms postulated in the pathogenesis of schizophrenia that may influence BDNF synthesis.

OBJECTIVES

A randomized placebo-controlled study was designed to compare the efficacy of a 26-week intervention composed of either 2.2 g/day of n-3 PUFA or olive oil placebo, with regard to symptom severity in first-episode schizophrenia patients. The secondary outcome measure of the study was to describe the association between n-3 PUFA clinical effect and changes in peripheral BDNF levels.

METHODS

Seventy-one patients aged 16-35 were enrolled in the study and randomly assigned to the following study arms: 36 to the EPA + DHA group and 35 to the placebo group. Plasma BDNF levels were assessed three times, at baseline and at weeks 8 and 26 of the intervention. BDNF levels were determined in plasma samples using Quantikine Human BDNF ELISA kit. Plasma BDNF level changes were further correlated with changes in the severity of symptoms in different clinical domains.

RESULTS

A significantly greater increase in plasma BDNF levels was observed in the intervention compared to the placebo group (Cohen's d = 1.54). Changes of BDNF levels inversely correlated with change in depressive symptoms assessed using the Calgary Depression Rating Scale in Schizophrenia (Pearson's r = - 0.195; p = 0.018).

CONCLUSIONS

The efficacy of a six-month intervention with n-3 PUFA observed in first-episode schizophrenia may be related to an increase in BDNF levels, which may be triggered by the activation of intracellular signaling pathways including transcription factors such as cAMP-reactive element binding protein.

A systems medicine approach for finding target proteins affecting treatment outcomes in patients with non-Hodgkin lymphoma

Autoantibody profiling with a systems medicine approach can help identify critical dysregulated signaling pathways (SPs) in cancers. In this way, immunoglobulins G (IgG) purified from the serum samples of 92 healthy controls, 10 pre-treated (PR) non-Hodgkin lymphoma (NHL) patients, and 20 NHL patients who underwent chemotherapy (PS) were screened with a phage-displayed random peptide library. Protein-protein interaction networks of the PR and PS groups were analyzed and visualized by Gephi. The results indicated AXIN2, SENP2, TOP2A, FZD6, NLK, HDAC2, HDAC1, and EHMT2, in addition to CAMK2A, PLCG1, PLCG2, GRM5, GRIN2B, GRIN2D, CACNA2D3, and SPTAN1 as hubs in 11 and 7 modules of PR and PS networks, respectively. PR- and PS-specific hubs were evaluated in the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases. The PR-specific hubs were involved in Wnt SP, signaling by Notch1 in cancer, telomere maintenance, and transcriptional misregulation. In contrast, glutamate receptor SP, Fc receptor-related pathways, growth factors-related SPs, and Wnt SP were statistically significant enriched pathways, based on the pathway analysis of PS hubs. The results revealed that the most PR-specific proteins were associated with events involved in tumor development, while chemotherapy in the PS group was associated with side effects of drugs and/or cancer recurrence. As the findings demonstrated, PR- and PS-specific proteins in this study can be promising therapeutic targets in future studies.

Modeling a linkage between blood transcriptional expression and activity in brain regions to infer the phenotype of schizophrenia patients

Hundreds of genetic loci participate to schizophrenia liability. It is also known that impaired cerebral connectivity is directly related to the cognitive and affective disturbances in schizophrenia. How genetic susceptibility and brain neural networks interact to specify a pathological phenotype in schizophrenia remains elusive. Imaging genetics, highlighting brain variations, has proven effective to establish links between vulnerability loci and associated clinical traits. As previous imaging genetics works in schizophrenia have essentially focused on structural DNA variants, these findings could be blurred by epigenetic mechanisms taking place during gene expression. We explored the meaningful links between genetic data from peripheral blood tissues on one hand, and regional brain reactivity to emotion task assayed by blood oxygen level-dependent functional magnetic resonance imaging on the other hand, in schizophrenia patients and matched healthy volunteers. We applied Sparse Generalized Canonical Correlation Analysis to identify joint signals between two blocks of variables: (i) the transcriptional expression of 33 candidate genes, and (ii) the blood oxygen level-dependent activity in 16 region of interest. Results suggested that peripheral transcriptional expression is related to brain imaging variations through a sequential pathway, ending with the schizophrenia phenotype. Generalization of such an approach to larger data sets should thus help in outlining the pathways involved in psychiatric illnesses such as schizophrenia.

IMAGING

SEARCHING FOR LINKS TO AID DIAGNOSIS: Researchers explore links between the expression of genes associated with schizophrenia in blood cells and variations in brain activity during emotion processing. El Chérif Ibrahim and Eric Fakra at Aix-Marseille Université, France, and colleagues have developed a method to relate the expression levels of 33 schizophrenia susceptibility genes in blood cells and functional magnetic resonance imaging (fMRI) data obtained as individuals carry out a task that triggers emotional responses. Although they found no significant differences in the expression of genes between the 26 patients with schizophrenia and 26 healthy controls they examined, variations in activity in the superior temporal gyrus were strongly linked to schizophrenia-associated gene expression and presence of disease. Similar analyses of larger data sets will shed further light on the relationship between peripheral molecular changes and disease-related behaviors and ultimately, aid the diagnosis of neuropsychiatric disease.

Uniting the neurodevelopmental and immunological hypotheses: Neuregulin 1 receptor ErbB and Toll-like receptor activation in first-episode schizophrenia

Current pathophysiological models of schizophrenia focus on neurodevelopmental and immunological mechanisms. We investigated a molecular pathway traditionally linked to the neurodevelopmental hypothesis (neuregulin 1 - ErbB), and pathogen-associated pattern recognition receptors associated with the immune hypothesis (Toll-like receptors, TLRs). We recruited 42 first-episode, drug-naïve patients with schizophrenia and 42 matched healthy control subjects. In monocytes TLR4/TLR5 and ErbB expressions were measured with flow-cytometry. Pro-inflammatory cytokines (IL-1β, IL-6, and TNF-α) and the anti-inflammatory cytokine IL-10 were determined following the stimulation of TLR4/TLR5 and ErbB. Results revealed increased TLR4/TLR5 and decreased ErbB4 expression in schizophrenia relative to the control subjects. The expression of ErbB2 and ErbB3 receptors was unaltered in schizophrenia. TLR4 stimulation resulted in lower pro-inflammatory cytokine production in schizophrenia compared to the control levels, whereas the stimulation of ErbB by neuregulin 1 led to higher pro-inflammatory cytokine levels in patients with schizophrenia relative to the control group. In healthy controls, ErbB activation was associated with a marked production of IL-10, which was dampened in schizophrenia. These results indicate that the stimulation of TLR4 and ErbB induces opposite pro-inflammatory cytokine responses in schizophrenia.

A molecular pathway analysis stresses the role of inflammation and oxidative stress towards cognition in schizophrenia

Cognitive processes have a genetic component and are impaired in Schizophrenia (SKZ). The exact nature of such impairment escapes definition. The aim of the present contribution was the identification of the molecular pathways enriched with mutations (SNPs) associated with cognitive performance during antipsychotic treatment. 765 individuals from the CATIE study, males = 559, mean age 40.93 ± 11.03 were included. Working memory and the verbal memory were the evaluated outcomes. A mixed regression model for repeated measures served in R for clinical and molecular pathway analysis. The analysis of quality was conducted under the following criteria: minor allele frequency >0.01, genotype call rate >95%, missing data frequency <5%, Hardy-Weimberg equilibrium threshold >0.0001. The inflation factor was controlled by lambda values. Input for the pathway analysis was SNPs at a p level <0.05 of association genome-wide. Gender, age, education and the duration of the disease were the clinical and socio-demographic variables associated with the cognitive performance. 4268977 SNPs were available after imputation and quality analysis. Pathways related to inflammation and oxidation were the most strongly associated with verbal memory and working memory at a conservative adjusted p value < 0.01. We report that inflammation and in particular the pathway associated with arachidonic acid was enriched in mutations associated with poorer performance at the verbal memory and working memory tasks in SKZ patients.

Antipsychotics influence Toll-like receptor (TLR) expression and its relationship with cognitive functions in schizophrenia

Increasing evidence suggests that altered immune functions are related to the pathophysiology of schizophrenia. Relatively little information is available on Toll-like receptors (TLRs), which are implicated in the recognition of molecular patterns associated with pathogens and internal cellular damage signals. By using immunophenotyping and flow cytometry, we investigated TLRs in CD14+ monocytes, CD4+CD25+Foxp3+ regulatory T cells (T_reg), and CD3+CD4+CD25+ activated T cells (T_act) in 35 drug-naïve patients with schizophrenia before and after an 8-week period of antipsychotic treatment with risperidone or olanzapine. As compared with 30 healthy control individuals, drug-naïve patients with schizophrenia exhibited an increased percentage of TLR4+ and TLR5+ monocytes and TLR5+ T_reg/T_act cells. At the end of the treatment period, we observed normalized TLR4+ monocytes and an up-regulation of TLR2+ monocytes and T_reg/T_act cells. Mean fluorescent intensity values, indicating receptor density, were consistent with these findings. In the drug-naïve state, but not after treatment, higher percentages of TLR4+ and TLR5+ monocytes were correlated with more severe cognitive deficits. Positive, negative, and general clinical symptoms were not associated with TLRs. There were no significant differences between patients receiving olanzapine and risperidone. These results indicate that abnormal expression of TLRs can be detected in the earliest stage of schizophrenia, which is modulated by antipsychotics. Immunological alterations in unmedicated schizophrenia patients may be linked to cognitive deficits.

Connectome Disconnectivity and Cortical Gene Expression in Patients With Schizophrenia

BACKGROUND

Genome-wide association studies have identified several common risk loci for schizophrenia (SCZ). In parallel, neuroimaging studies have shown consistent findings of widespread white matter disconnectivity in patients with SCZ.

METHODS

We examined the role of genes in brain connectivity in patients with SCZ by combining transcriptional profiles of 43 SCZ risk genes identified by the recent genome-wide association study of the Schizophrenia Working Group of the Psychiatric Genomics Consortium with data on macroscale connectivity reductions in patients with SCZ. Expression profiles of 43 Psychiatric Genomics Consortium SCZ risk genes were extracted from the Allen Human Brain Atlas, and their average profile across the cortex was correlated to the pattern of cortical disconnectivity as derived from diffusion-weighted magnetic resonance imaging data of patients with SCZ (n = 48) and matched healthy controls (n = 43).

RESULTS

The expression profile of SCZ risk genes across cortical regions was significantly correlated with the regional macroscale disconnectivity (r = .588; p = .017). In addition, effects were found to be potentially specific to SCZ, with transcriptional profiles not related to cortical disconnectivity in patients with bipolar I disorder (diffusion-weighted magnetic resonance imaging data; 216 patients, 144 controls). Further examination of correlations across all 20,737 genes present in the Allen Human Brain Atlas showed the set of top 100 strongest correlating genes to display significant enrichment for the disorder, potentially identifying new genes involved in the pathophysiology of SCZ.

CONCLUSIONS

Our results suggest that under disease conditions, cortical areas with pronounced expression of risk genes implicated in SCZ form central areas for white matter disconnectivity.

Clinically proven drug targets differentially expressed in the prefrontal cortex of schizophrenia patients

BACKGROUND

Due to the heterogeneous nature of schizophrenia, understanding the genetic risk for the disease is a complex task. Gene expression studies have proven to be more reliable than association studies as they are consistently replicated in a tissue specific manner.

METHODS

Using RNA-Seq we analysed gene expression in the frontal cortex of 24 individuals with schizophrenia and 25 unaffected controls.

RESULTS

We identified 1146 genes that were differentially expressed in schizophrenia, approximately 60% of which were up-regulated and 366 of 1146 (32%) also have aberrant DNA methylation (p=2.46×10^-39). The differentially expressed genes were significantly overrepresented in several pathways including inflammatory (p=8.7×10^-3) and nitric oxide pathways (p=9.2×10^-4). Moreover, these genes were significantly enriched for those with a druggable genome (p=0.04). We identified a number of genes that are significantly up-regulated in schizophrenia as confirmed in other gene expression studies using different brain tissues. Of the 349 genes associated with schizophrenia from the Psychiatric Genomics Consortium we identified 16 genes that are significant from our list of differentially expressed genes.

CONCLUSIONS

Our results identified biological functional genes that are differentially expressed in schizophrenia. A subset of these genes are clinically proven drug targets. We also found a strong pattern of differentially expressed immune response genes that may reflect an underlying defect in schizophrenia.

The schizophrenia risk gene MIR137 acts as a hippocampal gene network node orchestrating the expression of genes relevant to nervous system development and function

MicroRNAs (miRs) are small regulatory molecules, which orchestrate neuronal development and plasticity through modulation of complex gene networks. MicroRNA-137 (miR-137) is a brain-enriched RNA with a critical role in regulating brain development and in mediating synaptic plasticity. Importantly, mutations in this miR are associated with the pathoetiology of schizophrenia (SZ), and there is a widespread assumption that disruptions in miR-137 expression lead to aberrant expression of gene regulatory networks associated with SZ. To systematically identify the mRNA targets for this miR, we performed miR-137 gain- and loss-of-function experiments in primary rat hippocampal neurons and profiled differentially expressed mRNAs through next-generation sequencing. We identified 500 genes that were bidirectionally activated or repressed in their expression by the modulation of miR-137 levels. Gene ontology analysis using two independent software resources suggested functions for these miR-137-regulated genes in neurodevelopmental processes, neuronal maturation processes and cell maintenance, all of which known to be critical for proper brain circuitry formation. Since many of the putative miR-137 targets identified here also have been previously shown to be associated with SZ, we propose that this miR acts as a critical gene network hub contributing to the pathophysiology of this neurodevelopmental disorder.

Possible biomarkers modulating haloperidol efficacy and/or tolerability

Haloperidol (HP) is widely used in the treatment of several forms of psychosis. Despite of its efficacy, HP use is a cause of concern for the elevated risk of adverse drug reactions. adverse drug reactions risk and HP efficacy greatly vary across subjects, indicating the involvement of several factors in HP mechanism of action. The use of biomarkers that could monitor or even predict HP treatment impact would be of extreme importance. We reviewed the elements that could potentially be used as peripheral biomarkers of HP effectiveness. Although a validated biomarker still does not exist, we underlined the several potential findings (e.g., about cytokines, HP metabolites and genotypic biomarkers) which could pave the way for future research on HP biomarkers.

Multianalyte markers of schizophrenia and bipolar disorder: A preliminary study

Previous studies have identified altered molecular profiles in blood samples from individuals with schizophrenia and with bipolar disorder using multianalyte immunoassay platforms but there has been little comparison of the two groups in the same investigation. A total of 337 participants including 146 with schizophrenia, 79 with bipolar disorder, and 112 non-psychiatric controls had a blood sample drawn from which 166 analytes were measured. The initial dataset was split; classification models were developed in a training dataset and their performance evaluated in a test dataset. Principal component analysis was used to generate factor scores that were then compared between the groups. In a training set, a total of 7 independent factors were generated using 29 markers that were both normally distributed and significantly associated with diagnosis. Many of these analytes are components of the immune system and involved in the inflammatory response to infectious agents and foreign antigens. Two of the seven principal component scores discriminated between individuals with schizophrenia and with bipolar disorder; additional factors distinguished individuals with either schizophrenia or bipolar disorder from control individuals, while two factors were not significantly different between any of the diagnostic groups. In a test dataset, the schizophrenia vs. control Receiver Operating Curve (ROC) analysis shows an overall accuracy of 77% for schizophrenia vs. bipolar disorder, 84% for schizophrenia vs. controls, and 72% for bipolar disorder vs.

CONTROLS

An increased understanding of the role of altered pathways in serious psychiatric disorders may lead to novel methods for disease diagnosis and therapy.