Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the Generalized Singular Value Decomposition

Bipolar mania and epilepsy pathophysiology and treatment may converge in purine metabolism: A new perspective on available evidence

Decreased ATPergic signaling is an increasingly recognized pathophysiology in bipolar mania disease models. In parallel, adenosine deficit is increasingly recognized in epilepsy pathophysiology. Under-recognized ATP and/or adenosine-increasing mechanisms of several antimanic and antiseizure therapies including lithium, valproate, carbamazepine, and ECT suggest a fundamental pathogenic role of adenosine deficit in bipolar mania to match the established role of adenosine deficit in epilepsy. The depletion of adenosine-derivatives within the purine cycle is expected to result in a compensatory increase in oxopurines (uric acid precursors) and secondarily increased uric acid, observed in both bipolar mania and epilepsy. Cortisol-based inhibition of purine conversion to adenosine-derivatives may be reflected in observed uric acid increases and the well-established contribution of cortisol to both bipolar mania and epilepsy pathology. Cortisol-inhibited conversion from IMP to AMP as precursor of both ATP and adenosine may represent a mechanism for treatment resistance common in both bipolar mania and epilepsy. Anti-cortisol therapies may therefore augment other treatments both in bipolar mania and epilepsy. Evidence linking (i) adenosine deficit with a decreased need for sleep, (ii) IMP/cGMP excess with compulsive hypersexuality, and (iii) guanosine excess with grandiose delusions may converge to suggest a novel theory of bipolar mania as a condition characterized by disrupted purine metabolism. The potential for disease-modification and prevention related to adenosine-mediated epigenetic changes in epilepsy may be mirrored in mania. Evaluating the purinergic effects of existing agents and validating purine dysregulation may improve diagnosis and treatment in bipolar mania and epilepsy and provide specific targets for drug development.

Inosine as a Tool to Understand and Treat Central Nervous System Disorders: A Neglected Actor?

Since the 1970s, when ATP was identified as a co-transmitter in sympathetic and parasympathetic nerves, it and its active metabolite adenosine have been considered relevant signaling molecules in biological and pathological processes in the central nervous system (CNS). Meanwhile, inosine, a naturally occurring purine nucleoside formed by adenosine breakdown, was considered an inert adenosine metabolite and remained a neglected actor on the purinergic signaling scene in the CNS. However, this scenario began to change in the 1980s. In the last four decades, an extensive group of shreds of evidence has supported the importance of mediated effects by inosine in the CNS. Also, inosine was identified as a natural trigger of adenosine receptors. This evidence has shed light on the therapeutic potential of inosine on disease processes involved in neurological and psychiatric disorders. Here, we highlight the clinical and preclinical studies investigating the involvement of inosine in chronic pain, schizophrenia, epilepsy, depression, anxiety, and in neural regeneration and neurodegenerative diseases, such as Parkinson and Alzheimer. Thus, we hope that this review will strengthen the knowledge and stimulate more studies about the effects promoted by inosine in neurological and psychiatric disorders.

Metabolomics in Psychiatric Disorders: What We Learn from Animal Models

Biomarkers are a recent research target within biological factors of psychiatric disorders. There is growing evidence for deriving biomarkers within psychiatric disorders in serum or urine samples in humans, however, few studies have investigated this differentiation in brain or cerebral fluid samples in psychiatric disorders. As brain samples from humans are only available at autopsy, animal models are commonly applied to determine the pathogenesis of psychiatric diseases and to test treatment strategies. The aim of this review is to summarize studies on biomarkers in animal models for psychiatric disorders. For depression, anxiety and addiction disorders studies, biomarkers in animal brains are available. Furthermore, several studies have investigated psychiatric medication, e.g., antipsychotics, antidepressants, or mood stabilizers, in animals. The most notable changes in biomarkers in depressed animal models were related to the glutamate-γ-aminobutyric acid-glutamine-cycle. In anxiety models, alterations in amino acid and energy metabolism (i.e., mitochondrial regulation) were observed. Addicted animals showed several biomarkers according to the induced drugs. In summary, animal models provide some direct insights into the cellular metabolites that are produced during psychiatric processes. In addition, the influence on biomarkers due to short- or long-term medication is a noticeable finding. Further studies should combine representative animal models and human studies on cerebral fluid to improve insight into mental disorders and advance the development of novel treatment strategies.

Exposure-induced changes of plasma metabolome and gene expression in patients with panic disorder

BACKGROUND

Anxiety disorders including panic disorder (PD) are the most prevalent psychiatric diseases leading to high disability and burden in the general population. Acute panic attacks are distinctive for PD but also frequent in other anxiety disorders. The neurobiology or specific molecular changes leading to and present during panic attacks are insufficiently known so far.

METHODS

In the present pilot study, we investigated dynamic metabolomic and gene expression changes in peripheral blood of patients with PD (n = 25) during two exposure-induced acute panic attacks.

RESULTS

The results show that the metabolite glyoxylate was dynamically regulated in peripheral blood. Additionally, glyoxylate levels were associated with basal anxiety levels and showed gender-related differences at baseline. As glyoxylate is part of the degradation circuit of cholecystokinin, this suggests that this neuropeptide might be directly involved in exposure-induced panic attacks. Only gene expression changes of very small magnitude were observed in this experimental setting.

CONCLUSIONS

From this first metabolome and gene expression study in exposure-induced acute panic attacks in PD we conclude that metabolites can potentially serve as dynamic markers for different anxiety states. However, these findings have to be replicated in cohorts with greater sample sizes.

Mathematically universal and biologically consistent astrocytoma genotype encodes for transformation and predicts survival phenotype

DNA alterations have been observed in astrocytoma for decades. A copy-number genotype predictive of a survival phenotype was only discovered by using the generalized singular value decomposition (GSVD) formulated as a comparative spectral decomposition. Here, we use the GSVD to compare whole-genome sequencing (WGS) profiles of patient-matched astrocytoma and normal DNA. First, the GSVD uncovers a genome-wide pattern of copy-number alterations, which is bounded by patterns recently uncovered by the GSVDs of microarray-profiled patient-matched glioblastoma (GBM) and, separately, lower-grade astrocytoma and normal genomes. Like the microarray patterns, the WGS pattern is correlated with an approximately one-year median survival time. By filling in gaps in the microarray patterns, the WGS pattern reveals that this biologically consistent genotype encodes for transformation via the Notch together with the Ras and Shh pathways. Second, like the GSVDs of the microarray profiles, the GSVD of the WGS profiles separates the tumor-exclusive pattern from normal copy-number variations and experimental inconsistencies. These include the WGS technology-specific effects of guanine-cytosine content variations across the genomes that are correlated with experimental batches. Third, by identifying the biologically consistent phenotype among the WGS-profiled tumors, the GBM pattern proves to be a technology-independent predictor of survival and response to chemotherapy and radiation, statistically better than the patient's age and tumor's grade, the best other indicators, and MGMT promoter methylation and IDH1 mutation. We conclude that by using the complex structure of the data, comparative spectral decompositions underlie a mathematically universal description of the genotype-phenotype relations in cancer that other methods miss.

Functional mechanism of ASP5736, a selective serotonin 5-HT5A receptor antagonist with potential utility for the treatment of cognitive dysfunction in schizophrenia

The 5-HT_5A receptor is arguably the least understood 5-HT receptor. Despite widespread expression in human and rodent brains it lacks specific ligands. Our previous results suggest that 5-HT_5A receptor antagonists may be effective against cognitive impairment in schizophrenia. In this study, using behavioral, immunohistochemical, electrophysiological and microdialysis techniques, we examined the mechanism by which ASP5736, a novel and selective 5-HT_5A receptor antagonist, exerts a positive effect in animal models of cognitive impairment. We first confirmed the effect of ASP5736 on cognitive deficits in rats treated subchronically with phencyclidine hydrochloride (PCP) using an attentional set shifting task. Subsequently, we identified 5-HT_5A receptors in dopaminergic (DAergic) neurons and parvalbumin (PV)-positive interneurons in the ventral tegmental area (VTA) and in PV-positive interneurons in the medial prefrontal cortex (mPFC). Burst firing of the DAergic cells in the parabrachial pigmental nucleus (PBP) in the VTA, which predominantly project to the mPFC, was significantly enhanced by treatment with ASP5736. In contrast, ASP5736 exerted no significant effect on either the firing rate or burst firing in the DA cells in the paranigral nucleus (PN), that project to the nucleus accumbens (N. Acc.). ASP5736 increased the release of DA and gamma-aminobutyric acid (GABA) in the mPFC of subchronically PCP-treated rats. These results support our hypothesis that ASP5736 might block the inhibitory 5-HT_5A receptors on DAergic neurons in the VTA that project to the mPFC, and interneurons in the mPFC, and thereby improve cognitive impairment by preferentially enhancing DAergic and GABAergic neurons in the mPFC.

Frontal lobe alterations in schizophrenia: a review

Abstract Objective: To highlight the changes in the frontal lobe of the human brain in people with schizophrenia. Methods: This was a qualitative review of the literature. Results: Many schizophrenic patients exhibit functional, structural, and metabolic abnormalities in the frontal lobe. Some patients have few or no alterations, while some have more functional and structural changes than others. Magnetic resonance imaging (MRI) shows structural and functional changes in volume, gray matter, white matter, and functional activity in the frontal lobe, but the mechanisms underlying these changes are not yet fully understood. Conclusion: When schizophrenia is studied as an essential topic in the field of neuropsychiatry, neuroscientists find that the frontal lobe is the most commonly involved area of the human brain. A clear picture of how this lobe is affected in schizophrenia is still lacking. We therefore recommend that further research be conducted to improve understanding of the pathophysiology of this psychiatric dilemma.

Platform-Independent Genome-Wide Pattern of DNA Copy-Number Alterations Predicting Astrocytoma Survival and Response to Treatment Revealed by the GSVD Formulated as a Comparative Spectral Decomposition

We use the generalized singular value decomposition (GSVD), formulated as a comparative spectral decomposition, to model patient-matched grades III and II, i.e., lower-grade astrocytoma (LGA) brain tumor and normal DNA copy-number profiles. A genome-wide tumor-exclusive pattern of DNA copy-number alterations (CNAs) is revealed, encompassed in that previously uncovered in glioblastoma (GBM), i.e., grade IV astrocytoma, where GBM-specific CNAs encode for enhanced opportunities for transformation and proliferation via growth and developmental signaling pathways in GBM relative to LGA. The GSVD separates the LGA pattern from other sources of biological and experimental variation, common to both, or exclusive to one of the tumor and normal datasets. We find, first, and computationally validate, that the LGA pattern is correlated with a patient's survival and response to treatment. Second, the GBM pattern identifies among the LGA patients a subtype, statistically indistinguishable from that among the GBM patients, where the CNA genotype is correlated with an approximately one-year survival phenotype. Third, cross-platform classification of the Affymetrix-measured LGA and GBM profiles by using the Agilent-derived GBM pattern shows that the GBM pattern is a platform-independent predictor of astrocytoma outcome. Statistically, the pattern is a better predictor (corresponding to greater median survival time difference, proportional hazard ratio, and concordance index) than the patient's age and the tumor's grade, which are the best indicators of astrocytoma currently in clinical use, and laboratory tests. The pattern is also statistically independent of these indicators, and, combined with either one, is an even better predictor of astrocytoma outcome. Recurring DNA CNAs have been observed in astrocytoma tumors' genomes for decades, however, copy-number subtypes that are predictive of patients' outcomes were not identified before. This is despite the growing number of datasets recording different aspects of the disease, and due to an existing fundamental need for mathematical frameworks that can simultaneously find similarities and dissimilarities across the datasets. This illustrates the ability of comparative spectral decompositions to find what other methods miss.

Matrix factorization reveals aging-specific co-expression gene modules in the fat and muscle tissues in nonhuman primates

Accurate identification of coherent transcriptional modules (subnetworks) in adipose and muscle tissues is important for revealing the related mechanisms and co-regulated pathways involved in the development of aging-related diseases. Here, we proposed a systematically computational approach, called ICEGM, to Identify the Co-Expression Gene Modules through a novel mathematical framework of Higher-Order Generalized Singular Value Decomposition (HO-GSVD). ICEGM was applied on the adipose, and heart and skeletal muscle tissues in old and young female African green vervet monkeys. The genes associated with the development of inflammation, cardiovascular and skeletal disorder diseases, and cancer were revealed by the ICEGM. Meanwhile, genes in the ICEGM modules were also enriched in the adipocytes, smooth muscle cells, cardiac myocytes, and immune cells. Comprehensive disease annotation and canonical pathway analysis indicated that immune cells, adipocytes, cardiomyocytes, and smooth muscle cells played a synergistic role in cardiac and physical functions in the aged monkeys by regulation of the biological processes associated with metabolism, inflammation, and atherosclerosis. In conclusion, the ICEGM provides an efficiently systematic framework for decoding the co-expression gene modules in multiple tissues. Analysis of genes in the ICEGM module yielded important insights on the cooperative role of multiple tissues in the development of diseases.

Statistical HOmogeneous Cluster SpectroscopY (SHOCSY): an optimized statistical approach for clustering of ¹H NMR spectral data to reduce interference and enhance robust biomarkers selection

We propose a novel statistical approach to improve the reliability of (1)H NMR spectral analysis in complex metabolic studies. The Statistical HOmogeneous Cluster SpectroscopY (SHOCSY) algorithm aims to reduce the variation within biological classes by selecting subsets of homogeneous (1)H NMR spectra that contain specific spectroscopic metabolic signatures related to each biological class in a study. In SHOCSY, we used a clustering method to categorize the whole data set into a number of clusters of samples with each cluster showing a similar spectral feature and hence biochemical composition, and we then used an enrichment test to identify the associations between the clusters and the biological classes in the data set. We evaluated the performance of the SHOCSY algorithm using a simulated (1)H NMR data set to emulate renal tubule toxicity and further exemplified this method with a (1)H NMR spectroscopic study of hydrazine-induced liver toxicity study in rats. The SHOCSY algorithm improved the predictive ability of the orthogonal partial least-squares discriminatory analysis (OPLS-DA) model through the use of "truly" representative samples in each biological class (i.e., homogeneous subsets). This method ensures that the analyses are no longer confounded by idiosyncratic responders and thus improves the reliability of biomarker extraction. SHOCSY is a useful tool for removing irrelevant variation that interfere with the interpretation and predictive ability of models and has widespread applicability to other spectroscopic data, as well as other "omics" type of data.

Subanesthetic ketamine treatment promotes abnormal interactions between neural subsystems and alters the properties of functional brain networks

Acute treatment with subanesthetic ketamine, a non-competitive N-methyl-D-aspartic acid (NMDA) receptor antagonist, is widely utilized as a translational model for schizophrenia. However, how acute NMDA receptor blockade impacts on brain functioning at a systems level, to elicit translationally relevant symptomatology and behavioral deficits, has not yet been determined. Here, for the first time, we apply established and recently validated topological measures from network science to brain imaging data gained from ketamine-treated mice to elucidate how acute NMDA receptor blockade impacts on the properties of functional brain networks. We show that the effects of acute ketamine treatment on the global properties of these networks are divergent from those widely reported in schizophrenia. Where acute NMDA receptor blockade promotes hyperconnectivity in functional brain networks, pronounced dysconnectivity is found in schizophrenia. We also show that acute ketamine treatment increases the connectivity and importance of prefrontal and thalamic brain regions in brain networks, a finding also divergent to alterations seen in schizophrenia. In addition, we characterize how ketamine impacts on bipartite functional interactions between neural subsystems. A key feature includes the enhancement of prefrontal cortex (PFC)-neuromodulatory subsystem connectivity in ketamine-treated animals, a finding consistent with the known effects of ketamine on PFC neurotransmitter levels. Overall, our data suggest that, at a systems level, acute ketamine-induced alterations in brain network connectivity do not parallel those seen in chronic schizophrenia. Hence, the mechanisms through which acute ketamine treatment induces translationally relevant symptomatology may differ from those in chronic schizophrenia. Future effort should therefore be dedicated to resolve the conflicting observations between this putative translational model and schizophrenia.

Multi-tissue analysis of co-expression networks by higher-order generalized singular value decomposition identifies functionally coherent transcriptional modules

Recent high-throughput efforts such as ENCODE have generated a large body of genome-scale transcriptional data in multiple conditions (e.g., cell-types and disease states). Leveraging these data is especially important for network-based approaches to human disease, for instance to identify coherent transcriptional modules (subnetworks) that can inform functional disease mechanisms and pathological pathways. Yet, genome-scale network analysis across conditions is significantly hampered by the paucity of robust and computationally-efficient methods. Building on the Higher-Order Generalized Singular Value Decomposition, we introduce a new algorithmic approach for efficient, parameter-free and reproducible identification of network-modules simultaneously across multiple conditions. Our method can accommodate weighted (and unweighted) networks of any size and can similarly use co-expression or raw gene expression input data, without hinging upon the definition and stability of the correlation used to assess gene co-expression. In simulation studies, we demonstrated distinctive advantages of our method over existing methods, which was able to recover accurately both common and condition-specific network-modules without entailing ad-hoc input parameters as required by other approaches. We applied our method to genome-scale and multi-tissue transcriptomic datasets from rats (microarray-based) and humans (mRNA-sequencing-based) and identified several common and tissue-specific subnetworks with functional significance, which were not detected by other methods. In humans we recapitulated the crosstalk between cell-cycle progression and cell-extracellular matrix interactions processes in ventricular zones during neocortex expansion and further, we uncovered pathways related to development of later cognitive functions in the cortical plate of the developing brain which were previously unappreciated. Analyses of seven rat tissues identified a multi-tissue subnetwork of co-expressed heat shock protein (Hsp) and cardiomyopathy genes (Bag3, Cryab, Kras, Emd, Plec), which was significantly replicated using separate failing heart and liver gene expression datasets in humans, thus revealing a conserved functional role for Hsp genes in cardiovascular disease.

Metabolomic analysis reveals metabolic disturbance in the cortex and hippocampus of subchronic MK-801 treated rats

Background

Although a number of proteins and genes relevant to schizophrenia have been identified in recent years, few are known about the exact metabolic pathway involved in this disease. Our previous proteomic study has revealed the energy metabolism abnormality in subchronic MK-801 treated rat, a well-established animal model for schizophrenia. This prompted us to further investigate metabolite levels in the same rat model to better delineate the metabolism dysfunctions and provide insights into the pathology of schizophrenia.

Methods

Metabolomics, a high-throughput investigatory strategy developed in recent years, can offer comprehensive metabolite-level insights that complement protein and genetic findings. In this study, we employed a nondestructive metabolomic approach (1H-MAS-NMR) to investigate the metabolic traits in cortex and hippocampus of MK-801 treated rats. Multivariate statistics and ingenuity pathways analyses (IPA) were applied in data processing. The result was further integrated with our previous proteomic findings by IPA analysis to obtain a systematic view on our observations.

Results

Clear distinctions between the MK-801 treated group and the control group in both cortex and hippocampus were found by OPLS-DA models (with R²X = 0.441, Q²Y = 0.413 and R²X = 0.698, Q²Y = 0.677, respectively). The change of a series of metabolites accounted for the separation, such as glutamate, glutamine, citrate and succinate. Most of these metabolites fell in a pathway characterized by down-regulated glutamate synthesis and disturbed Krebs cycle. IPA analysis further confirmed the involvement of energy metabolism abnormality induced by MK-801 treatment.

Conclusions

Our metabolomics findings reveal systematic changes in pathways of glutamate metabolism and Krebs cycle in the MK-801 treated rats’ cortex and hippocampus, which confirmed and improved our previous proteomic observation and served as a valuable reference to the etiology research of schizophrenia.

Sustained NMDA receptor hypofunction induces compromised neural systems integration and schizophrenia-like alterations in functional brain networks

Compromised functional integration between cerebral subsystems and dysfunctional brain network organization may underlie the neurocognitive deficits seen in psychiatric disorders. Applying topological measures from network science to brain imaging data allows the quantification of complex brain network connectivity. While this approach has recently been used to further elucidate the nature of brain dysfunction in schizophrenia, the value of applying this approach in preclinical models of psychiatric disease has not been recognized. For the first time, we apply both established and recently derived algorithms from network science (graph theory) to functional brain imaging data from rats treated subchronically with the N-methyl-D-aspartic acid (NMDA) receptor antagonist phencyclidine (PCP). We show that subchronic PCP treatment induces alterations in the global properties of functional brain networks akin to those reported in schizophrenia. Furthermore, we show that subchronic PCP treatment induces compromised functional integration between distributed neural systems, including between the prefrontal cortex and hippocampus, that have established roles in cognition through, in part, the promotion of thalamic dysconnectivity. We also show that subchronic PCP treatment promotes the functional disintegration of discrete cerebral subsystems and also alters the connectivity of neurotransmitter systems strongly implicated in schizophrenia. Therefore, we propose that sustained NMDA receptor hypofunction contributes to the pathophysiology of dysfunctional brain network organization in schizophrenia.

Translational bioinformatics in psychoneuroimmunology: methods and applications

Translational bioinformatics plays an indispensable role in transforming psychoneuroimmunology (PNI) into personalized medicine. It provides a powerful method to bridge the gaps between various knowledge domains in PNI and systems biology. Translational bioinformatics methods at various systems levels can facilitate pattern recognition, and expedite and validate the discovery of systemic biomarkers to allow their incorporation into clinical trials and outcome assessments. Analysis of the correlations between genotypes and phenotypes including the behavioral-based profiles will contribute to the transition from the disease-based medicine to human-centered medicine. Translational bioinformatics would also enable the establishment of predictive models for patient responses to diseases, vaccines, and drugs. In PNI research, the development of systems biology models such as those of the neurons would play a critical role. Methods based on data integration, data mining, and knowledge representation are essential elements in building health information systems such as electronic health records and computerized decision support systems. Data integration of genes, pathophysiology, and behaviors are needed for a broad range of PNI studies. Knowledge discovery approaches such as network-based systems biology methods are valuable in studying the cross-talks among pathways in various brain regions involved in disorders such as Alzheimer's disease.