Integrating proteomic and clinical data to discriminate major psychiatric disorders: Applications for major depressive disorder, bipolar disorder, and schizophrenia.
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ABSTRACT:
SUBMITTER: Shin D
PROVIDER: S-EPMC9235346 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Schizophrenia (SCZ), bipolar disorder (BP), and major depressive disorder (MDD) are the most common psychiatric disorders. Because there were lots of overlaps among these disorders from genetic epidemiology and molecular genetics, it is hard to realize the diagnoses of these psychiatric disorders. Currently, plenty of studies have been conducted for contributing to the diagnoses of these diseases. However, constructing a classification model with superior performance for differentiating SCZ, BP, and MDD samples is still a great challenge. In this study, the transcriptomic data was applied for discovering key genes and constructing a classification model. In this dataset, there were 268 samples including four groups (67 SCZ patients, 40 BP patients, 57 MDD patients, and 104 healthy controls), which were applied for constructing a classification model. First, 269 probes of differentially expressed genes (DEGs) among four sample groups were identified by the feature selection method. Second, these DEGs were validated by the literature review including disease relevance with the psychiatric disorders of these DEGs, the hub genes in the PPI (protein-protein interaction) network, and GO (gene ontology) terms and pathways. Third, a classification model was constructed using the identified DEGs by machine learning method to classify different groups. The ROC (receiver operator characteristic) curve and AUC (area under the curve) value were used to assess the classification capacity of the model. In summary, this classification model might provide clues for the diagnoses of these psychiatric disorders.
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Project description:BackgroundMitochondria provide most of the energy for brain cells by the process of oxidative phosphorylation. Mitochondrial abnormalities and deficiencies in oxidative phosphorylation have been reported in individuals with schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD) in transcriptomic, proteomic, and metabolomic studies. Several mutations in mitochondrial DNA (mtDNA) sequence have been reported in SZ and BD patients.Methodology/principal findingsDorsolateral prefrontal cortex (DLPFC) from a cohort of 77 SZ, BD, and MDD subjects and age-matched controls (C) was studied for mtDNA sequence variations and heteroplasmy levels using Affymetrix mtDNA resequencing arrays. Heteroplasmy levels by microarray were compared to levels obtained with SNaPshot and allele specific real-time PCR. This study examined the association between brain pH and mtDNA alleles. The microarray resequencing of mtDNA was 100% concordant with conventional sequencing results for 103 mtDNA variants. The rate of synonymous base pair substitutions in the coding regions of the mtDNA genome was 22% higher (p = 0.0017) in DLPFC of individuals with SZ compared to controls. The association of brain pH and super haplogroup (U, K, UK) was significant (p = 0.004) and independent of postmortem interval time.ConclusionsFocusing on haplogroup and individual susceptibility factors in psychiatric disorders by considering mtDNA variants may lead to innovative treatments to improve mitochondrial health and brain function.
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Project description:Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk.
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Project description:OBJECTIVES:Genome-wide association studies (GWAS) in complex phenotypes, including psychiatric disorders, have yielded many replicated findings, yet individual markers account for only a small fraction of the inherited differences in risk. We tested the performance of polygenic models in discriminating between cases and healthy controls and among cases with distinct psychiatric diagnoses. METHODS:GWAS results in bipolar disorder (BD), major depressive disorder (MDD), schizophrenia (SZ), and Parkinson's disease (PD) were used to assign weights to individual alleles, based on odds ratios. These weights were used to calculate allele scores for individual cases and controls in independent samples, summing across many single nucleotide polymorphisms (SNPs). How well allele scores discriminated between cases and controls and between cases with different disorders was tested by logistic regression. RESULTS:Large sets of SNPs were needed to achieve even modest discrimination between cases and controls. The most informative SNPs were overlapping in BD, SZ, and MDD, with correlated effect sizes. Little or no overlap was seen between allele scores for psychiatric disorders and those for PD. CONCLUSIONS:BD, SZ, and MDD all share a similar polygenic component, but the polygenic models tested lack discriminative accuracy and are unlikely to be useful for clinical diagnosis.
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Project description:In 1983, reports of antibodies in subjects with major depressive disorder (MDD) to an as-yet uncharacterized infectious agent associated with meningoencephalitis in horses and sheep led to molecular cloning of the genome of a novel, negative-stranded neurotropic virus, Borna disease virus (BDV). This advance has enabled the development of new diagnostic assays, including in situ hybridization, PCR and serology based on recombinant proteins. Since these assays were first implemented in 1990, more than 80 studies have reported an association between BDV and a wide range of human illnesses that include MDD, bipolar disorder (BD), schizophrenia (SZ), anxiety disorder, chronic fatigue syndrome, multiple sclerosis, amyotrophic lateral sclerosis, dementia and glioblastoma multiforme. However, to date there has been no blinded case-control study of the epidemiology of BDV infection. Here, in a United States-based, multi-center, yoked case-control study with standardized methods for clinical assessment and blinded serological and molecular analysis, we report the absence of association of psychiatric illness with antibodies to BDV or with BDV nucleic acids in serially collected serum and white blood cell samples from 396 subjects, a study population comprised of 198 matched pairs of patients and healthy controls (52 SZ/control pairs, 66 BD/control pairs and 80 MDD/control pairs). Our results argue strongly against a role for BDV in the pathogenesis of these psychiatric disorders.
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Project description:Objectives: Schizophrenia (SZ), major depressive disorder (MDD), and bipolar disorder (BD) are serious mental disorders with distinct diagnostic criteria. They share common clinical and biological features. However, there are still only few studies on the common and specific brain imaging changes associated with the three mental disorders. Therefore, the aim of this study was to identify the common and specific functional activity and connectivity changes in SZ, MDD, and BD. Methods: A total of 271 individuals underwent functional magnetic resonance imaging: SZ (n = 64), MDD (n = 73), BD (n = 41), and healthy controls (n = 93). The symptoms of SZ patients were evaluated by the Positive and Negative Syndrome Scale. The Beck Depression Inventory (BDI), and Beck Anxiety Inventory (BAI) were used to evaluate the symptoms of MDD patients. The BDI, BAI, and Young Mania Rating Scale were used to evaluate the symptoms of MDD and BD patients. In addition, we compared the fALFF and functional connectivity between the three mental disorders and healthy controls using two sample t-tests. Results: Significantly decreased functional activity was found in the right superior frontal gyrus, middle cingulate gyrus, left middle frontal gyrus, and decreased functional connectivity (FC) of the insula was found in SZ, MDD, and BD. Specific fALFF changes, mainly in the ventral lateral pre-frontal cortex, striatum, and thalamus were found for SZ, in the left motor cortex and parietal lobe for MDD, and the dorsal lateral pre-frontal cortex, orbitofrontal cortex, and posterior cingulate cortex in BD. Conclusion: Our findings of common abnormalities in SZ, MDD, and BD provide evidence that salience network abnormality may play a critical role in the pathogenesis of these three mental disorders. Meanwhile, our findings also indicate that specific alterations in SZ, MDD, and BD provide neuroimaging evidence for the differential diagnosis of the three mental disorders.
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Project description:Major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ) are classified as major mental disorders and together account for the second-highest global disease burden, and half of these patients experience symptom onset in adolescence. Several studies have reported both similar and unique features regarding the risk factors and clinical symptoms of these three disorders. However, it is still unclear whether these disorders have similar or unique metabolic characteristics in adolescents. We conducted a metabolomics analysis of plasma samples from adolescent healthy controls (HCs) and patients with MDD, BD, and SCZ. We identified differentially expressed metabolites between patients and HCs. Based on the differentially expressed metabolites, correlation analysis, metabolic pathway analysis, and potential diagnostic biomarker identification were conducted for disorders and HCs. Our results showed significant changes in plasma metabolism between patients with these mental disorders and HCs; the most distinct changes were observed in SCZ patients. Moreover, the metabolic differences in BD patients shared features with those in both MDD and SCZ, although the BD metabolic profile was closer to that of MDD than to SCZ. Additionally, we identified the metabolites responsible for the similar and unique metabolic characteristics in multiple metabolic pathways. The similar significant differences among the three disorders were found in fatty acid, steroid-hormone, purine, nicotinate, glutamate, tryptophan, arginine, and proline metabolism. Interestingly, we found unique characteristics of significantly altered glycolysis, glycerophospholipid, and sphingolipid metabolism in SCZ; lysine, cysteine, and methionine metabolism in MDD and BD; and phenylalanine, tyrosine, and aspartate metabolism in SCZ and BD. Finally, we identified five panels of potential diagnostic biomarkers for MDD-HC, BD-HC, SCZ-HC, MDD-SCZ, and BD-SCZ comparisons. Our findings suggest that metabolic characteristics in plasma vary across psychiatric disorders and that critical metabolites provide new clues regarding molecular mechanisms in these three psychiatric disorders.
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Project description:We investigated for the first time the proteomic profiles both in the dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC) of major depressive disorder (MDD) and bipolar disorder (BD) patients. Cryostat sections of DLPFC and ACC of MDD and BD patients with their respective well-matched controls were used for study. Proteins were quantified by tandem mass tag and high-performance liquid chromatography-mass spectrometry system. Gene Ontology terms and functional cluster alteration were analyzed through bioinformatic analysis. Over 3000 proteins were accurately quantified, with more than 100 protein expressions identified as significantly changed in these two brain areas of MDD and BD patients as compared to their respective controls. These include OGDH, SDHA and COX5B in the DLPFC in MDD patients; PFN1, HSP90AA1 and PDCD6IP in the ACC of MDD patients; DBN1, DBNL and MYH9 in the DLPFC in BD patients. Impressively, depending on brain area and distinct diseases, the most notable change we found in the DLPFC of MDD was 'suppressed energy metabolism'; in the ACC of MDD it was 'suppressed tissue remodeling and suppressed immune response'; and in the DLPFC of BD it was differentiated 'suppressed tissue remodeling and suppressed neuronal projection'. In summary, there are distinct proteomic changes in different brain areas of the same mood disorder, and in the same brain area between MDD and BD patients, which strengthens the distinct pathogeneses and thus treatment targets.
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Project description:Background: Cognitive dysfunction is considered a core feature among schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). Despite abundant literature comparing cognitive dysfunction among these disorders, the relationship between cognitive dysfunction and symptom dimensions remains unclear. The study aims are a) to identify the factor structure of the BPRS-18 and b) to examine the relationship between symptom domains and cognitive function across SZ, BD, and MDD. Methods: A total of 716 participants [262 with SZ, 104 with BD, 101 with MDD, and 249 healthy controls (HC)] were included in the study. One hundred eighty participants (59 with SZ, 23 with BD, 24 with MDD, and 74 HC) completed the MATRICS Consensus Cognitive Battery (MCCB), and 507 participants (85 with SZ, 89 with BD, 90 with MDD, and 243 HC) completed the Wisconsin Card Sorting Test (WCST). All patients completed the Brief Psychiatric Rating Scale (BPRS). Results: We identified five BPRS exploratory factor analysis (EFA) factors ("affective symptoms," "psychosis," "negative/disorganized symptoms," "activation," and "noncooperation") and found cognitive dysfunction in all of the participant groups with psychiatric disorders. Negative/disorganized symptoms were the most strongly associated with cognitive dysfunctions across SZ, BD, and MDD. Conclusions: Our findings suggest that cognitive dysfunction severity relates to the negative/disorganized symptom domain across SZ, BD, and MDD, and negative/disorganized symptoms may be an important target for effective cognitive remediation in SZ, BD, and MDD.
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Project description:Emerging evidence has demonstrated overlapping biological abnormalities underlying schizophrenia (SCZ), bipolar disorder (BP), and major depressive disorder (MDD); these overlapping abnormalities help explain the high heterogeneity and the similarity of patients within and among diagnostic categories. This study aimed to identify transdiagnostic subtypes of these psychiatric disorders based on lipidomics abnormalities. We performed discriminant analysis to identify lipids that classified patients (N = 349, 112 with SCZ, 132 with BP, and 105 with MDD) and healthy controls (N = 198). Ten lipids that mainly regulate energy metabolism, inflammation, oxidative stress, and fatty acylation of proteins were identified. We found two subtypes (named Cluster 1 and Cluster 2 subtypes) across patients with SCZ, BP, and MDD by consensus clustering analysis based on the above 10 lipids. The distribution of clinical diagnosis, functional impairment measured by Global Assessment of Functioning (GAF) scales, and brain white matter abnormalities measured by fractional anisotropy (FA) and radial diffusivity (RD) differed in the two subtypes. Patients within the Cluster 2 subtype were mainly SCZ and BP patients and featured significantly elevated RD along the genu of corpus callosum (GCC) region and lower GAF scores than patients within the Cluster 1 subtype. The SCZ and BP patients within the Cluster 2 subtype shared similar biological patterns; that is, these patients had comparable brain white matter abnormalities and functional impairment, which is consistent with previous studies. Our findings indicate that peripheral lipid abnormalities might help identify homogeneous transdiagnostic subtypes across psychiatric disorders.
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