Serum phosphatidylinositol as a biomarker for bipolar disorder liability

Marked alteration of phosphoinositide signaling-associated molecules in postmortem prefrontal cortex with bipolar disorder

AIM

The etiology of bipolar disorder (BD) remains unknown; however, lipid abnormalities in BD have received increasing attention in recent years. In this study, we examined the expression levels of enzyme proteins associated with the metabolic pathway of phosphoinositides (PIs) and their downstream effectors, protein kinase B (Akt1) and glycogen synthase kinase 3β (GSK3β), which have been assumed to be the targets of mood stabilizers such as lithium, in the postmortem brains of patients with BD.

METHODS

The protein expression levels of phosphatidylinositol 4-phosphate 5-kinase type-1 gamma (PIP5K1C), phosphatidylinositol 4-kinase alpha (PIK4CA), phosphatase and tensin homolog deleted from chromosome 10 (PTEN), Akt1, and GSK3β were measured using enzyme-linked immunosorbent assays and multiplex fluorescent bead-based immunoassays in the prefrontal cortex (PFC). Specifically, PTEN, Akt1, GSK3β, and PIP5K1C were measured in seven BD patients and 48 controls. Additionally, PIK4CA was analyzed in 10 cases and 34 controls.

RESULTS

PTEN expression levels were markedly decreased in the PFCs of patients with BD, whereas those of Akt and GSK3β were prominently elevated. Moreover, patients medicated with lithium exhibited higher Akt1 expression levels and lower PTEN expression levels in comparison with the untreated group.

CONCLUSION

Our results suggest that the expression levels of Akt1/GSK3β and its upstream regulator PTEN are considerably altered.

rvTWAS: identifying gene-trait association using sequences by utilizing transcriptome-directed feature selection

Toward the identification of genetic basis of complex traits, transcriptome-wide association study (TWAS) is successful in integrating transcriptome data. However, TWAS is only applicable for common variants, excluding rare variants in exome or whole-genome sequences. This is partly because of the inherent limitation of TWAS protocols that rely on predicting gene expressions. Our previous research has revealed the insight into TWAS: the 2 steps in TWAS, building and applying the expression prediction models, are essentially genetic feature selection and aggregations that do not have to involve predictions. Based on this insight disentangling TWAS, rare variants' inability of predicting expression traits is no longer an obstacle. Herein, we developed "rare variant TWAS," or rvTWAS, that first uses a Bayesian model to conduct expression-directed feature selection and then uses a kernel machine to carry out feature aggregation, forming a model leveraging expressions for association mapping including rare variants. We demonstrated the performance of rvTWAS by thorough simulations and real data analysis in 3 psychiatric disorders, namely schizophrenia, bipolar disorder, and autism spectrum disorder. We confirmed that rvTWAS outperforms existing TWAS protocols and revealed additional genes underlying psychiatric disorders. Particularly, we formed a hypothetical mechanism in which zinc finger genes impact all 3 disorders through transcriptional regulations. rvTWAS will open a door for sequence-based association mappings integrating gene expressions.

Untargeted Lipidomics Study of Bipolar Disorder Patients in Serbia

The Lipidomic profiles of serum samples from patients with bipolar disorder (BD) and healthy controls (C) were explored and compared. The sample cohort included 31 BD patients and 31 control individuals. An untargeted lipidomics study applying liquid chromatography (LC) coupled with high-resolution mass spectrometry (HRMS) was conducted to achieve the lipid profiles. Multivariate statistical analyses (principal component analysis and partial least squares discriminant analysis) were performed, and fifty-six differential lipids were confirmed in BD and controls. Our results pointed to alterations in lipid metabolism, including pathways of glycerophospholipids, sphingolipids, glycerolipids, and sterol lipids, in BD patient sera. This study emphasized the role of lipid pathways in BD, and comprehensive research using the LC-HRMS platform is necessary for future application in the diagnosis and improvement of BD treatments.

An overview of metabolomic and proteomic profiling in bipolar disorder and its clinical value

INTRODUCTION

Bipolar disorder (BD) is a complex psychiatric disease characterized by alternating mood episodes. As for any other psychiatric illness, currently there is no biochemical test that is able to support diagnosis or therapeutic decisions for BD. In this context, the discovery and validation of biomarkers are interesting strategies that can be achieved through proteomics and metabolomics.

AREAS COVERED

In this descriptive review, a literature search including original articles and systematic reviews published in the last decade was performed with the objective to discuss the results of BD proteomic and metabolomic profiling analyses and indicate proteins and metabolites (or metabolic pathways) with potential clinical value.

EXPERT OPINION

A large number of proteins and metabolites have been reported as potential BD biomarkers; however, most studies do not reach biomarker validation stages. An effort from the scientific community should be directed toward the validation of biomarkers and the development of simplified bioanalytical techniques or protocols to determine them in biological samples, in order to translate proteomic and metabolomic findings into clinical routine assays.

Modulation of the gut microbiota and lipidomic profiles by black chokeberry (Aronia melanocarpa L.) polyphenols via the glycerophospholipid metabolism signaling pathway

Black chokeberry (Aronia melanocarpa L.) is rich in polyphenols with various physiological and pharmacological activities. However, the relationship between the modulation effect of black chokeberry polyphenols on obesity and the alteration of lipid metabolism is not clearly understood. This study aimed to investigate the beneficial effects of the black chokeberry polyphenols (BCPs) treatment on the structure of gut microbiota, lipid metabolism, and associated mechanisms in high-fat diet (HFD)-induced obese rats. Here, we found that a high-fat diet promoted body weight gain and lipid accumulation in rats, while oral BCPs supplementation reduced body weight, liver, and white adipose tissue weight and alleviated dyslipidemia and hepatic steatosis in HFD-induced obese rats. In addition, BCPs supplementation prevented gut microbiota dysbiosis by increasing the relative abundance of Bacteroides, Prevotella, Romboutsia, and Akkermansia and decreasing the relative abundance of Desulfovibrio and Clostridium. Furthermore, 64 lipids were identified as potential lipid biomarkers through lipidomics analysis after BCPs supplementation, especially PE (16:0/22:6), PE (18:0/22:6), PC (20:3/19:0), LysoPE (24:0), LysoPE (24:1), and LysoPC (20:0). Moreover, our studies provided new evidence that composition of gut microbiota was closely related to the alteration of lipid profiles after BCPs supplementation. Additionally, BCPs treatment could ameliorate the disorder of lipid metabolism by regulating the mRNA and protein expression of genes related to the glycerophospholipid metabolism signaling pathway in HFD-induced obese rats. The mRNA and protein expression of PPARα, CPT1α, EPT1, and LCAT were significantly altered after BCPs treatment. In conclusion, the results of this study indicated that BCPs treatment alleviated HFD-induced obesity by modulating the composition and function of gut microbiota and improving the lipid metabolism disorder via the glycerophospholipid metabolism signaling pathway.

Metabolomic Identification of Serum Exosome-Derived Biomarkers for Bipolar Disorder

Background

Exosomes are extracellular vesicles that play important roles in various physiological and pathological functions. Previous studies have demonstrated that exosome-derived contents are promising biomarkers to inform the pathogenesis and diagnosis of major depressive disorder and schizophrenia.

Methods

We used ultraperformance liquid chromatography-tandem mass spectrometry to analyze the differentially expressed metabolites in serum exosomes of patients with bipolar disorder (BD) and evaluated the potential of exosomal metabolites as biomarkers for BD.

Results

Our results showed 26 differentially expressed serum exosomal metabolites in patients with BD (n = 32) when compared with healthy control (HC) subjects (n = 40), and these differentially expressed metabolites were enriched in pathways related to sugar metabolism. We then utilized random forest classifier and identified 15 exosomal metabolites that can be used to classify samples from patients with BD and HC subjects with 0.838 accuracy (95% CI, 0.604–1.00) in the training set of participants. These 15 metabolites showed excellent performance in differentiating between patients with BD and HC subjects in the testing set of participants, with 0.971 accuracy (95% CI, 0.865–1.00). Importantly, the 15 exosomal metabolites also showed good to excellent performance in differentiating between BD patients and other major psychiatric diseases (major depressive disorder and schizophrenia).

Conclusion

Collectively, our findings for the first time revealed a potential role of exosomal metabolite dysregulations in the onset and/or development of BD and suggested that blood exosomal metabolites are strong candidates to inform the diagnosis of BD.

Adaptation of Lipid Profiling in Depression Disease and Treatment: A Critical Review

Major depressive disorder (MDD), also called depression, is a serious disease that impairs the quality of life of patients and has a high incidence, affecting approximately 3.8% of the world population. Its diagnosis is very subjective and is not supported by measurable biomarkers mainly due to the lack of biochemical markers. Recently, disturbance of lipid profiling has been recognized in MDD, in animal models of MDD or in depressed patients, which may contribute to unravel the etiology of the disease and find putative new biomarkers, for a diagnosis or for monitoring the disease and therapeutics outcomes. In this review, we provide an overview of current knowledge of lipidomics analysis, both in animal models of MDD (at the brain and plasma level) and in humans (in plasma and serum). Furthermore, studies of lipidomics analyses after antidepressant treatment in rodents (in brain, plasma, and serum), in primates (in the brain) and in humans (in plasma) were reviewed and give evidence that antidepressants seem to counteract the modification seen in lipids in MDD, giving some evidence that certain altered lipid profiles could be useful MDD biomarkers for future precision medicine.

Alterations of Plasma Lipids in Adult Women With Major Depressive Disorder and Bipolar Depression

Lipidomics has been established as a potential tool for the investigation of mental diseases. However, the composition analysis and the comparison of the peripheral lipids regarding adult women with major depressive depression (MDD) or bipolar depression (BPD) has been poorly addressed. In the present study, age-matched female individuals with MDD (n = 28), BPD (n = 22) and healthy controls (HC, n = 25) were enrolled. Clinical symptoms were assessed and the plasma samples were analyzed by comprehensive lipid profiling based on liquid chromatography-mass spectrometry (LC/MS). We found that the composition of lipids was remarkably changed in the patients with MDD and BPD when compared to HC or compared to each other. Moreover, we identified diagnostic potential biomarkers comprising 20 lipids that can distinguish MDD from HC (area under the curve, AUC = 0.897) and 8 lipids that can distinguish BPD from HC (AUC = 0.784), as well as 13 lipids were identified to distinguish MDD from BPD with moderate reliability (AUC = 0.860). This study provides further understanding of abnormal lipid metabolism in adult women with MDD and BPD and may develop lipid classifiers able to effectively discriminate MDD from BPD and HC.

Alterations in the Plasma Lipidome of Adult Women With Bipolar Disorder: A Mass Spectrometry-Based Lipidomics Research

Lipidomics has become a pivotal tool in biomarker discovery for the diagnosis of psychiatric illnesses. However, the composition and quantitative analysis of peripheral lipids in female patients with bipolar disorder (BD) have been poorly addressed. In this study, plasma samples from 24 female patients with BD and 30 healthy controls (HCs) were analyzed by comprehensive lipid profiling and quantitative validation based on liquid chromatography-mass spectrometry. Clinical characteristics and a correlation between the level of lipid molecules and clinical symptoms were also observed. We found that the quantitative alterations in several lipid classes, including acylcarnitine, lysophosphatidylethanolamine, GM2, sphingomyelin, GD2, triglyceride, monogalactosyldiacylglycerol, phosphatidylinositol phosphate, phosphatidylinositol 4,5-bisphosphate, phosphatidylethanolamine, phosphatidylserine, and lysophosphatidylinositol, were remarkably upregulated or downregulated in patients with BD and were positively or negatively correlated with the severity of psychotic, affective, or mania symptoms. Meanwhile, the composition of different carbon chain lengths and degrees of fatty acid saturation for these lipid classes in BD were also different from those of HCs. Moreover, 55 lipid molecules with significant differences and correlations with the clinical parameters were observed. Finally, a plasma biomarker set comprising nine lipids was identified, and an area under the curve of 0.994 was obtained between patients with BD and the HCs. In conclusion, this study provides a further understanding of abnormal lipid metabolism in the plasma and suggests that specific lipid species can be used as complementary biomarkers for the diagnosis of BD in women.

Therapeutic lithium alters polar head-group region of lipid bilayer and prevents lipid peroxidation in forebrain cortex of sleep-deprived rats

Lithium is regarded as a unique therapeutic agent for the management of bipolar disorder (BD). In efforts to explain the favourable effects of lithium in BD, a wide range of mechanisms was suggested. Among those, the effect of clinically relevant concentrations of lithium on the plasma membrane was extensively studied. However, the biophysical properties of brain membranes isolated from experimental animals exposed to acute, short-term and chronic lithium have not been performed to-date. In this study, we compared the biophysical parameters and level of lipid peroxidation in membranes isolated from forebrain cortex (FBC) of therapeutic lithium-treated and/or sleep-deprived rats. Lithium interaction with FBC membranes was characterized by appropriate fluorescent probes. DPH (1,6-diphenyl-1,3,5-hexatriene) and TMA-DPH (1-(4-trimethylammoniumphenyl)-6-phenyl-1,3,5-hexatriene p-toluenesulphonate) were used for characterization of the hydrophobic lipid core and Laurdan (6-dodecanoyl-2-dimethylaminonaphthalene) for the membrane-water interface. Lipid peroxidation was determined by immunoblot analysis of 4-HNE-(4-hydroxynonenal)-protein adducts. The organization of polar head-group region of FBC membranes, measured by Laurdan generalized polarization, was substantially altered by sleep deprivation and augmented by lithium treatment. Hydrophobic membrane interior characterized by steady-state anisotropy of DPH and TMA-DPH fluorescence was unchanged. Chronic lithium had a protective effect against peroxidative damage of membrane lipids in FBC. In summary, lithium administration at a therapeutic level and/or sleep deprivation as an animal model of mania resulted in changes in rat FBC membrane properties.

Characterization of the COPD Salivary Fingerprint through Surface Enhanced Raman Spectroscopy: A Pilot Study

Chronic Obstructive Pulmonary Disease (COPD) is a debilitating pathology characterized by reduced lung function, breathlessness and rapid and unrelenting decrease in quality of life. The severity rate and the therapy selection are strictly dependent on various parameters verifiable after years of clinical observations, missing a direct biomarker associated with COPD. In this work, we report the methodological application of Surface Enhanced Raman Spectroscopy combined with Multivariate statistics for the analysis of saliva samples collected from 15 patients affected by COPD and 15 related healthy subjects in a pilot study. The comparative Raman analysis allowed to determine a specific signature of the pathological saliva, highlighting differences in determined biological species, already studied and characterized in COPD onset, compared to the Raman signature of healthy samples. The unsupervised principal component analysis and hierarchical clustering revealed a sharp data dispersion between the two experimental groups. Using the linear discriminant analysis, we created a classification model able to discriminate the collected signals with accuracies, specificities, and sensitivities of more than 98%. The results of this preliminary study are promising for further applications of Raman spectroscopy in the COPD clinical field.

Neurocognitive impairment in type 2 diabetes: evidence for shared genetic aetiology

AIMS/HYPOTHESIS

Type 2 diabetes is associated with cognitive impairments, but it is unclear whether common genetic factors influence both type 2 diabetes risk and cognition.

METHODS

Using data from 1892 Mexican-American individuals from extended pedigrees, including 402 with type 2 diabetes, we examined possible pleiotropy between type 2 diabetes and cognitive functioning, as measured by a comprehensive neuropsychological test battery.

RESULTS

Negative phenotypic correlations (ρp) were observed between type 2 diabetes and measures of attention (Continuous Performance Test [CPT d']: ρp = -0.143, p = 0.001), verbal memory (California Verbal Learning Test [CVLT] recall: ρp = -0.111, p = 0.004) and face memory (Penn Face Memory Test [PFMT]: ρp = -0.127, p = 0.002; PFMT Delayed: ρp = -0.148, p = 2 × 10-4), replicating findings of cognitive impairment in type 2 diabetes. Negative genetic correlations (ρg) were also observed between type 2 diabetes and measures of attention (CPT d': ρg = -0.401, p = 0.001), working memory (digit span backward test: ρg = -0.380, p = 0.005), and face memory (PFMT: ρg = -0.476, p = 2 × 10-4; PFMT Delayed: ρg = -0.376, p = 0.005), suggesting that the same genetic factors underlying risk for type 2 diabetes also influence poor cognitive performance in these domains. Performance in these domains was also associated with type 2 diabetes risk using an endophenotype ranking value approach. Specifically, on measures of attention (CPT d': β = -0.219, p = 0.005), working memory (digit span backward: β = -0.326, p = 0.035), and face memory (PFMT: β = -0.171, p = 0.023; PFMT Delayed: β = -0.215, p = 0.005), individuals with type 2 diabetes showed the lowest performance, while unaffected/unrelated individuals showed the highest performance, and those related to an individual with type 2 diabetes performed at an intermediate level.

CONCLUSIONS/INTERPRETATION

These findings suggest that cognitive impairment may be a useful endophenotype of type 2 diabetes and, therefore, help to elucidate the pathophysiological underpinnings of this chronic disease.

DATA AVAILABILITY

The data analysed in this study is available in dbGaP: www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001215.v2.p2.

Urinary metabolite signature in bipolar disorder patients during depressive episode

The first few episodes of bipolar disorder (BD) are highly likely to be depressive. This phenomenon causes many BD patients to be misdiagnosed as having major depression. Therefore, it is very important to correctly diagnose BD patients during depressive episode. Here, we conducted this study to identify potential biomarkers for young and middle-aged BD patients during depressive episode. Both gas chromatography-mass spectroscopy (GC-MS) and nuclear magnetic resonance (NMR) spectroscopy were used to profile the urine samples from the recruited subjects. In total, 13 differential metabolites responsible for the discrimination between healthy controls (HCs) and patients were identified. Most differential metabolites had a close relationship with energy homeostasis. Meanwhile, a panel consisting of five differential metabolites was identified. This panel could effectively distinguish the patients from HCs with an AUC of 0.998 in the training set and 0.974 in the testing set. Our findings on one hand could be helpful in developing an objective diagnostic method for young and middle-aged BD patients during depressive episode; on the other hand could provide critical insight into the pathological mechanism of BD and the biological mechanisms responsible for the transformation of different episodes.

Metabolic Profiling in Bipolar Disorder Patients During Depressive Episodes

Bipolar disorder (BD) is a common and debilitating mental disorder. Bipolar depression is the main episode of BD. Furthermore, there are no objective biomarkers available for diagnosing the disorder. In this research, a Nuclear Magnetic Resonance (NMR) spectroscopy based on a metabonomics technique was used to analyze serum samples from 37 patients with bipolar depression and 48 healthy control participants to determine potential biomarkers for bipolar depression. In total, seven different metabolites were identified that could effectively distinguish patients from healthy controls. The metabolites indicated that disturbances of amino acid and energy metabolisms might be involved in the pathogenesis of BD. Finally, a panel consisting of four potential biomarkers (lactate, trimethylamine oxide, N-acetyl glycoprotein, and α-glucose) was identified, which showed a higher combined diagnostic ability with an area under the curve of 0.893. Our findings may contribute to the development of an objective method for diagnosing bipolar depression.

Genetic Overlap Between Alzheimer's Disease and Bipolar Disorder Implicates the MARK2 and VAC14 Genes

Background: Alzheimer's disease (AD) and bipolar disorder (BIP) are complex traits influenced by numerous common genetic variants, most of which remain to be detected. Clinical and epidemiological evidence suggest that AD and BIP are related. However, it is not established if this relation is of genetic origin. Here, we applied statistical methods based on the conditional false discovery rate (FDR) framework to detect genetic overlap between AD and BIP and utilized this overlap to increase the power to identify common genetic variants associated with either or both traits. Methods: We obtained genome wide association studies data from the International Genomics of Alzheimer's Project part 1 (17,008 AD cases and 37,154 controls) and the Psychiatric Genetic Consortium Bipolar Disorder Working Group (20,352 BIP cases and 31,358 controls). We used conditional QQ-plots to assess overlap in common genetic variants between AD and BIP. We exploited the genetic overlap to re-rank test-statistics for AD and BIP and improve detection of genetic variants using the conditional FDR framework. Results: Conditional QQ-plots demonstrated a polygenic overlap between AD and BIP. Using conditional FDR, we identified one novel genomic locus associated with AD, and nine novel loci associated with BIP. Further, we identified two novel loci jointly associated with AD and BIP implicating the MARK2 gene (lead SNP rs10792421, conjunctional FDR = 0.030, same direction of effect) and the VAC14 gene (lead SNP rs11649476, conjunctional FDR = 0.022, opposite direction of effect). Conclusion: We found polygenic overlap between AD and BIP and identified novel loci for each trait and two jointly associated loci. Further studies should examine if the shared loci implicating the MARK2 and VAC14 genes could explain parts of the shared and distinct features of AD and BIP.

Disentangling the genetic overlap between cholesterol and suicide risk

Suicide is major public health concern; one million individuals worldwide die by suicide each year of which there are many more attempts. Thus, it is imperative that robust and reliable indicators, or biomarkers, of suicide risk be identified so that individuals at risk can be identified and provided appropriate interventions as quickly as possible. Previous work has revealed a relationship between low levels of circulating cholesterol and suicide risk, implicating cholesterol level as one such potential biomarker, but the factors underlying this relationship remain unknown. In the present study, we applied a combination of bivariate polygenic and coefficient-of-relatedness analysis, followed by mediation analysis, in a large sample of Mexican-American individuals from extended pedigrees [N = 1897; 96 pedigrees (average size = 19.17 individuals, range = 2-189) 60% female; mean age = 42.58 years, range = 18-97 years, sd = 15.75 years] with no exclusion criteria for any given psychiatric disorder. We observed that total esterified cholesterol measured at the time of psychiatric assessment shared a significant genetic overlap with risk for suicide attempt (ρ_g = -0.64, p = 1.24 × 10^-04). We also found that total unesterified cholesterol measured around 20 years prior to assessment varied as a function of genetic proximity to an affected individual (h² = 0.21, se = 0.10, p = 8.73 × 10^-04; β_suicide = -0.70, se = 0.25, p = 8.90 × 10^-03). Finally, we found that the relationship between total unesterified cholesterol and suicide risk was significantly mediated by ABCA-1-specific cholesterol efflux capacity (β_{suicide-efflux} = -0.45, p = 0.039; β_{efflux-cholexterol} = -0.34, p < 0.0001; β_indirect = -0.15, p = 0.044). These findings suggest that the relatively well-delineated process of cholesterol metabolism and associated molecular pathways will be informative for understanding the neurobiological underpinnings of risk for suicide attempt.

A preliminary study of bipolar disorder type I by mass spectrometry-based serum lipidomics

The present study aimed at investigating possible alterations in the serum lipid profile of euthymic patients with bipolar disorder type I (BD) compared to healthy controls (HC). Thirty-five individuals from both genders were recruited, with 14 diagnosed and treated as BD patients (BD group) and 21 healthy subjects (HC group). Clinical assessment was based on the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I), Young Mania Rating Scale (YMRS), and 17-items of Hamilton Depression Rating Scale (HDRS-17) data, which were used to confirm diagnosis, to verify psychiatric comorbidities, and to estimate the severity of manic and depressive symptoms. Ultra-high performance liquid chromatography (UHPLC) coupled to high resolution mass spectrometry (HRMS) was applied to analyze the lipids extracted from all serum samples from both studied groups. In this pioneer and exploratory study, we observed different serum lipid profiles for BD and HC groups, especially regarding glycerophospholipid, glycerolipid, and sphingolipid distribution. Multivariate statistical analyses indicated that 121 lipids were significantly different between BD and HC. Phosphatidylinositols were identified as the most altered lipids in BD patient sera. The results of this preliminary study reinforce the role of lipid abnormalities in BD and offer additional methodological possibilities for investigation in the field.