Plasma metabonomics as a novel diagnostic approach for major depressive disorder

Decreased gut microbiome-derived indole-3-propionic acid mediates the exacerbation of myocardial ischemia/reperfusion injury following depression via the brain-gut-heart axis

Despite the increasing recognition of the interplay between depression and cardiovascular disease (CVD), the precise mechanisms by which depression contributes to the pathogenesis of cardiovascular disease remain inadequately understood. The involvement of gut microbiota and their metabolites to health and disease susceptibility has been gaining increasing attention. In this study, it was found that depression exacerbated cardiac injury, impaired cardiac function (EF%: P < 0.01; FS%: P < 0.05), hindered long-term survival (P < 0.01), and intensified adverse cardiac remodeling (WGA: P < 0.01; MASSON: P < 0.0001) after myocardial ischemia/reperfusion (MI/R) in mice. Then we found that mice receiving microbiota transplants from chronic social defeat stress (CSDS) mice exhibited worse cardiac function (EF%: P < 0.01; FS%: P < 0.01) than those receiving microbiota transplants from non-CSDS mice after MI/R injury. Moreover, impaired tryptophan metabolism due to alterations in gut microbiota composition and structure was observed in the CSDS mice. Mechanistically, we analyzed the metabolomics of fecal and serum samples from CSDS mice and identified indole-3-propionic acid (IPA) as a protective agent for cardiomyocytes against ferroptosis after MI/R via NRF2/System xc-/GPX4 axis, played a role in mediating the detrimental influence of depression on MI/R. Our findings provide new insights into the role of the gut microbiota and IPA in depression and CVD, forming the basis of intervention strategies aimed at mitigating the deterioration of cardiac function following MI/R in patients experiencing depression.

Characterizing metabolomic and proteomic changes in depression: a systematic analysis

Despite the widespread use of metabolomics and proteomics to explore the molecular landscape of depression, there is a lack of consensus regarding dysregulated molecules with replicable evidence. Thus, this study aimed to identify robust metabolomic and proteomic features in depression by integrating evidence from large-scale studies. In this study, a knowledge base-mining approach was adopted to compile a list of dysregulated molecules derived from metabolomic and proteomic studies. A vote-counting approach was performed to identify consistently altered molecules in the blood and urine samples of patients with depression. A total of 2398 molecular entries were selected, comprising 857 unique metabolites and 468 unique proteins from 143 metabolomic and 23 proteomic studies in depression. The results of vote-counting analyses revealed that 11 metabolites in blood and 5 metabolites in urine exhibited consistent disturbances across studies. Circulating levels of glutamic acid and phosphatidylcholine (32:0) were elevated in depressive patients, whereas the levels of tryptophan, kynurenic acid, kynurenine, acetylcarnitine, serotonin, creatinine, inosine, phenylalanine, and valine were lower. Urinary levels of isobutyric acid, alanine, and nicotinic acid were higher, whereas the levels of N-methylnicotinamide and tyrosine were lower. Moreover, analysis of the proteomic dataset identified only one circulating protein, ceruloplasmin, that was consistently dysregulated. Convergence comparison prioritized tryptophan as the top-ranked circulating metabolite, followed by kynurenic acid, acetylcarnitine, creatinine, serotonin, and valine. Collectively, robust evidence of metabolomic changes was observed in patients with depression, pointing to a role as potential biomarkers. Further investigation of consensus proteomic features for depression is necessitated.

Metabolomics in Depression: What We Learn from Preclinical and Clinical Evidences

Depression is one of the predominant common mental illnesses that affects millions of people of all ages worldwide. Random mood changes, loss of interest in routine activities, and prevalent unpleasant senses often characterize this common depreciated mental illness. Subjects with depressive disorders have a likelihood of developing cardiovascular complications, diabesity, and stroke. The exact genesis and pathogenesis of this disease are still questionable. A significant proportion of subjects with clinical depression display inadequate response to antidepressant therapies. Hence, clinicians often face challenges in predicting the treatment response. Emerging reports have indicated the association of depression with metabolic alterations. Metabolomics is one of the promising approaches that can offer fresh perspectives into the diagnosis, treatment, and prognosis of depression at the metabolic level. Despite numerous studies exploring metabolite profiles post-pharmacological interventions, a quantitative understanding of consistently altered metabolites is not yet established. The article gives a brief discussion on different biomarkers in depression and the degree to which biomarkers can improve treatment outcomes. In this review article, we have systemically reviewed the role of metabolomics in depression along with current challenges and future perspectives.

Deciphering the role of miRNA-134 in the pathophysiology of depression: A comprehensive review

This study summarizes the significance of microRNA-134 (miRNA-134) in the pathophysiology, diagnosis, and treatment of depression, a disease still under investigation due to its complexity. miRNA-134 is an endogenous short non-coding RNA that can bind to the 3' untranslated region (3'UTR) of miRNA-134, inhibiting gene translation and showing great potential in the regulation of mood, synaptic plasticity, and neuronal function. This study included 15 articles retrieved from four English-language databases: PubMed, Embase, The Cochrane Library, and Web of Science, and three Chinese literature databases: CNKI, Wanfang, and Chinese Science and Technology Periodical Database (VIP).We evaluated each of the 15 articles using the Critical Appraisal Skills Program (CASP) tool.The standard integrates analyzes of genomic, transcriptomic, neuroimaging, and behavioral data analyses related to miRNA-134 and depression. A multidimensional framework based on standardized criteria was used for quality assessment. The main findings indicate that miRNA-134 significantly affects synaptic plasticity and neurotransmitter regulation, in particular the synthesis and release of serotonin and dopamine. miRNA-134 shows high sensitivity and specificity as a biomarker for the diagnosis of depression and has therapeutic potential for the targeted treatment of depression. miRNA-134 plays a crucial role in the pathogenesis of depression, providing valuable insights for early diagnosis and the development of targeted therapeutic strategies. This work highlights the potential of miRNA-134 as a focal point for advancing personalized medicine approaches for depression.

NMR-based metabolomics approach to study the effect and related molecular mechanisms of Saffron essential oil against depression

Depression currently ranks as the fourth leading cause of disability globally, affecting approximately 20% of the world's population. we established a chronic restraint stress (CRS) induced depression model in mice and employed fluoxetine as a reference drug. We assessed the therapeutic potential of saffron essential oil (SEO) and elucidated its underlying mechanisms through behavioral indices and NMR-based metabolomic analysis. The findings indicate that SEO ameliorates behavioral symptoms of depression, such as the number of entries into the central area, fecal count, latency to immobility, and duration of immobility in both the Tail Suspension Test (TST) and the Forced Swim Test (FST), along with correcting the dysregulation of 5-serotonin. Metabolomic investigations identified sixteen potential biomarkers across the liver, spleen, and kidneys. SEO notably modulated nine of these biomarkers: dimethylglycine, glycerol, adenosine, β-glucose, α-glucose, uridine, mannose, sarcosine, and aspartate, with glycerol emerging as a common biomarker in both the liver and spleen. Pathway analysis suggests that these biomarkers participate in glycolysis, glycine serine threonine metabolism, and energy metabolism, potentially implicating a role in neural regulation. In summary, SEO effectively mitigates depressive-like behaviors in CRS mice, predominantly via modulation of glycolysis, amino acid metabolism, and energy metabolism, and potentially exerts antidepressant effects through neural regulation. Our study offers insights into small molecule metabolite alterations in CRS mice through a metabolomics lens, providing evidence for the antidepressant potential of plant essential oils and contributing to our understanding of the mechanisms of traditional Chinese medicine in treating depression.

Impact of Serotonin Transporter Absence on Brain Insulin Receptor Expression, Plasma Metabolome Changes, and ADHD-like Behavior in Mice fed a Western Diet

The impaired function of the serotonin transporter (SERT) in humans has been linked to a higher risk of obesity and type 2 diabetes, especially as people age. Consuming a "Western diet" (WD), which is high in saturated fats, cholesterol, and sugars, can induce metabolic syndrome. Previous research indicated that mice carrying a targeted inactivation of the Sert gene (knockout, KO) and fed a WD display significant metabolic disturbances and behaviors reminiscent of ADHD. These abnormalities might be mediated via a dysfunction in insulin receptor (IR) signaling, which is also associated with adult ADHD. However, the impact of Sert deficiency on IR signaling and systemic metabolic changes has not been thoroughly explored. In this study, we conducted a detailed analysis of locomotor behavior in wild-type (WT) and KO mice fed a WD or control diet. We investigated changes in the blood metabolome and examined, via PCR, the expression of insulin receptor A and B isoforms and key regulators of their function in the brain. Twelve-month-old KO mice and their WT littermates were fed a WD for three weeks. Nuclear magnetic resonance spectroscopy analysis of plasma samples showed that KO mice on a WD had higher levels of lipids and lipoproteins and lower levels of glucose, lactate, alanine, valine, and isoleucine compared to other groups. SERT-KO mice on the control diet exhibited increased brain levels of both IR A and B isoforms, accompanied by a modest increase in the negative regulator ENPP. The KO mice also displayed anxiety-like behavior and reduced exploratory activity in an open field test. However, when the KO animals were fed a WD, the aberrant expression levels of IR isoforms in the KO mice and locomotor behavior were ameliorated indicating a complex interaction between genetic and dietary factors that might contribute to ADHD-like symptoms. Overall, our findings suggest that the lack of Sert leads to a unique metabolic phenotype in aged mice, characterized by dysregulated IR-related pathways. These changes are exacerbated by WD in the blood metabolome and are associated with behavioral abnormalities.

A Machine Learning Analysis of Big Metabolomics Data for Classifying Depression: Model Development and Validation

BACKGROUND

Many metabolomics studies of depression have been performed, but these have been limited by their scale. A comprehensive in silico analysis of global metabolite levels in large populations could provide robust insights into the pathological mechanisms underlying depression and candidate clinical biomarkers.

METHODS

Depression-associated metabolomics was studied in 2 datasets from the UK Biobank database: participants with lifetime depression (N = 123,459) and participants with current depression (N = 94,921). The Whitehall II cohort (N = 4744) was used for external validation. CatBoost machine learning was used for modeling, and Shapley additive explanations were used to interpret the model. Fivefold cross-validation was used to validate model performance, training the model on 3 of the 5 sets with the remaining 2 sets for validation and testing, respectively. Diagnostic performance was assessed using the area under the receiver operating characteristic curve.

RESULTS

In the lifetime depression and current depression datasets and sex-specific analyses, 24 significantly associated metabolic biomarkers were identified, 12 of which overlapped in the 2 datasets. The addition of metabolic features slightly improved the performance of a diagnostic model using traditional (nonmetabolomics) risk factors alone (lifetime depression: area under the curve 0.655 vs. 0.658 with metabolomics; current depression: area under the curve 0.711 vs. 0.716 with metabolomics).

CONCLUSIONS

The machine learning model identified 24 metabolic biomarkers associated with depression. If validated, metabolic biomarkers may have future clinical applications as supplementary information to guide early and population-based depression detection.

Progress of depression mechanism based on Omics method

Depression is a very common disabling mental disorder, which is typically characterized by high rates of disability and mortality. Although research into the various mechanisms of depression was still underway, its physiopathology remains uncertain. The rapid developments in new technologies and the combined use of a variety of techniques will help to understand the pathogenesis of depression and explore effective treatment methods. In this review, we focus on the combination of proteomic and metabolomic approaches to analyze metabolites and proteins in animal models of depression induced by different modeling approaches, with the aim of broadening the understanding of the physiopathological mechanisms of depression using complementary "omics" strategy.

Plasma metabolomics reveals the intervention mechanism of different types of exercise on chronic unpredictable mild stress-induced depression rat model

OBJECTIVE

To study the effects of different types of exercise on the plasma metabolomics of chronic unpredictable mild stress (CUMS)-induced depressed rats based on 1H-NMR metabolomics techniques, and to explore the potential mechanisms of exercise for the treatment of depression. Rats were randomly divided into blank control group (C), CUMS control group (D), pre-exercise with CUMS group (P), CUMS with aerobic exercise group, CUMS with resistance exercise group (R), and CUMS with aerobic + resistance exercise group (E). The corresponding protocol intervention was applied to each group of rats. Body weight, sucrose preference and open field tests were performed weekly during the experiment to evaluate the extent of depression in rats. Plasma samples from each group of rats were collected at the end of the experiment, and then the plasma was analyzed by 1H-NMR metabolomics combined with multivariate statistical analysis methods to identify differential metabolites and perform metabolic pathway analysis. (1) Compared with the group D, the body weight, sucrose preference rate, and the number of crossings and standings in the different types of exercise groups were significantly improved (p < 0.05 or p < 0.01). (2) Compared to group C, a total of 15 differential metabolites associated with depression were screened in the plasma of rats in group D, involving 6 metabolic pathways. Group P can regulate the levels of 6 metabolites: valine, lactate, inositol, glucose, phosphocreatine, acetoacetic acid. Group A can regulate the levels of 6 metabolites: N-acetylglycoprotein, leucine, lactate, low density lipoprotein, glucose and acetoacetic acid. Group R can regulate the levels of 6 metabolites: choline, lactate, inositol, glucose, phosphocreatine and acetoacetic acid. Group E can regulate the levels of 5 metabolites: choline, citric acid, glucose, acetone and acetoacetic acid. The different types of exercise groups can improve the depressive symptoms in CUMS rats, and there are common metabolites and metabolic pathways for their mechanism of effects. This study provides a powerful analytical tool to study the mechanism of the antidepressant effect of exercise, and provides an important method and basis for the early diagnosis, prevention and treatment of depression.

Characteristics of gut microbiota and metabolic phenotype in patients with major depressive disorder based on multi-omics analysis

Depression is a chronic, relapsing mental illness, often accompanied by loss of appetite, increased fatigue, insomnia and poor concentration. Here, we performed serum and urine metabolomics and fecal 16S rDNA sequencing studies on 57 unmedicated patients with major depressive disorder (MDD) and 57 healthy controls to characterize the metabolic and flora profile of MDD patients. We observed significant differences in serum and urinary metabolome between MDD patients and healthy individuals. Specifically, glycerophospholipid metabolism, primary bile acid biosynthesis and linoleic acid metabolism were significantly disordered in serum, and aminoacyl-tRNA biosynthesis, arginine biosynthesis, purine metabolism, phenylalanine metabolism, alanine, aspartate and glutamate metabolism, and pyrimidine metabolism were significantly impaired in urine. On this basis, we identified four potential diagnostic biomarkers for carnitine and four fatty acid classes in serum and urine, respectively. In addition, we observed significant disturbances of the gut microbiota in MDD patients. Spearman correlation analysis showed that imbalances in the gut microbiota were associated with metabolic disturbances, suggesting an important role of the gut microbiota in the pathogenesis of MDD. Our study provides a theoretical basis for further understanding of the pathogenesis of depression and for future clinical diagnosis and screening, as well as a basis for targeting the gut flora to optimize its structure for the prevention and treatment of depression.

Integrated pathway and network analyses of metabolomic alterations in peripheral blood of patients with depression

Depression is a serious mental illness, but the molecular mechanisms of depression remain unclear. Previous research has reported metabolomic changes in the blood of patients with depression, while integrated analysis based on these altered metabolites was still lacking. The objective of this study was to integrate metabolomic changes to reveal the underlying molecular alternations of depression. We retrieved altered metabolites in the blood of patients with depression from the MENDA database. Pathway analysis was conducted to explore enriched pathways based on candidate metabolites. Pathway crosstalk analysis was performed to explore potential correlations of these enriched pathways, based on their shared candidate metabolites. Moreover, potential interactions of candidate metabolites with other biomolecules such as proteins were assessed by network analysis. A total of 854 differential metabolite entries were retrieved in peripheral blood of patients with depression, including 555 unique candidate metabolites. Pathway analysis identified 215 significantly enriched pathways, then pathway crosstalk analysis revealed that these pathways were clustered into four modules, including amino acid metabolism, nucleotide metabolism, energy metabolism and others. Additionally, eight molecular networks were identified in the molecular network analysis. The main functions of these networks involved amino acid metabolism, molecular transport, inflammatory responses and others. Based on integrated analysis, our study revealed pathway-based modules and molecular networks associated with depression. These results will contribute to the underlying knowledge of the molecular mechanisms in depression.

Non-targeted Metabolomics Analysis Reveals Distinct Metabolic Profiles Between Positive and Negative Emotional Tears of Humans: A Preliminary Study

Background Basal, reflex, and emotional tears differ in chemical components. It is not yet known whether chemical differences exist in tears of different emotions. We investigated the biochemical basis of emotional tears by performing non-targeted metabolomics analyses of positive and negative emotional tears of humans. Methods Samples of reflex, negative, and positive emotional tears were obtained from 12 healthy college participants (11 females and one male). Untargeted metabolomics was performed to identify metabolites in different types of tears. The differentially altered metabolites were screened and assessed using univariate and multivariate analyses. Results The orthogonal partial least squares discriminant analysis model showed that reflex, negative, and positive emotional tears were clearly separated. A total of 133 significantly differentially expressed metabolites of electrospray ionization source (ESI-) mode were identified between negative and positive emotional tears. The top 50 differentially expressed metabolites between negative and positive emotional tears were highly correlated. Pathway analysis revealed that secretion of negative emotional tears was associated with some synapses in the brain, regulation of a series of endocrine hormones, including the estrogen signaling pathway, and inflammation activities, while secretion of positive emotional tears was correlated with biotin and caffeine metabolism. Conclusions It is indicated that metabolic profiles of reflex, positive, and negative emotional tears of humans are distinct, and secretion of the tears involves distinct biological activities. Therefore, we present a chemical method for detecting human emotions, which may become a powerful tool for the diagnosis of mental diseases and the identification of fake tears.

Stress, inflammation, microbiome and depression

Psychiatric disorders are mental illnesses involving changes in mood, cognition and behavior. Their prevalence has rapidly increased in the last decades. One of the most prevalent psychiatric disorders is major depressive disorder (MDD), a debilitating disease lacking efficient treatments. Increasing evidence shows that microbial and immunological changes contribute to the pathophysiology of depression and both are modulated by stress. This bidirectional relationship constitutes the brain-gut axis involving various neuroendocrine, immunological, neuroenterocrine and autonomic pathways. The present review covers the most recent findings on the relationships between stress, the gut microbiome and the inflammatory response and their contribution to depression.

Plasma metabolomics and network pharmacology identified glutamate, glutamine, and arginine as biomarkers of depression under Shuganjieyu capsule treatment

Depression is a psychiatric disorder and confers an enormous burden on society. Mild to moderate forms of depression (MMD) are particularly common. Our previous studies showed that the Shuganjieyu (SGJY) capsule might improve depressive and cognitive symptoms in patients with MMD. However, biomarkers evaluating the efficacy of SGJY and the underlying mechanism remains unclear. The aim of the present study was to discover efficacy biomarkers and explore the underlying mechanisms of SGJY as antidepression treatment. Twenty-three patients with MMD were recruited and administered with SGJY for 8 weeks. Results showed that the content of 19 metabolites changed significantly in the plasma of patients with MMD, among which 8 metabolites improved significantly after SGJY treatment. Network pharmacology analysis showed that 19 active compounds, 102 potential targets, and 73 enzymes were related to the mechanistic action of SGJY. Through a comprehensive analysis, we identified four hub enzymes (GLS2, GLS, GLUL, and ADC), three key differential metabolites (glutamine, glutamate, and arginine), and two shared pathways (alanine, aspartate, and glutamate metabolism; and arginine biosynthesis). Receiver operating characteristic curve (ROC) analysis showed that the three metabolites had a high diagnostic ability. The expression of hub enzymes was validated using RT-qPCR in animal models. Overall, glutamate, glutamine, and arginine may be potential biomarkers for evaluating the efficacy of SGJY. The present study provides a new strategy for pharmacodynamic evaluation and mechanistic study of SGJY, and offers new information for clinical practice and treatment research.

Identification of potential serum biomarkers for congenital heart disease children with pulmonary arterial hypertension by metabonomics

BACKGROUND

Pulmonary arterial hypertension is a common complication in patients with congenital heart disease. In the absence of early diagnosis and treatment, pediatric patients with PAH has a poor survival rate. Here, we explore serum biomarkers for distinguishing children with pulmonary arterial hypertension associated with congenital heart disease (PAH-CHD) from CHD.

METHODS

Samples were analyzed by nuclear magnetic resonance spectroscopy-based metabolomics and 22 metabolites were further quantified by ultra-high-performance liquid chromatography-tandem mass spectroscopy.

RESULTS

Serum levels of betaine, choline, S-Adenosyl methionine (SAM), acetylcholine, xanthosine, guanosine, inosine and guanine were significantly altered between CHD and PAH-CHD. Logistic regression analysis showed that combination of serum SAM, guanine and N-terminal pro-brain natriuretic peptide (NT-proBNP), yielded the predictive accuracy of 157 cases was 92.70% with area under the curve of the receiver operating characteristic curve value of 0.9455.

CONCLUSION

We demonstrated that a panel of serum SAM, guanine and NT-proBNP is potential serum biomarkers for screening PAH-CHD from CHD.

Mechanism of lily bulb and Rehmannia decoction in the treatment of lipopolysaccharide-induced depression-like rats based on metabolomics study and network pharmacology

CONTEXT

Lily bulb and Rehmannia decoction (LBRD), consisting of Lilium henryi Baker (Liliaceae) and Rehmannia glutinosa (Gaertn) DC (Plantaginaceae), is a specialized traditional Chinese medicine formula for treating depression. However, the underlying mechanisms, especially the relationship between LBRD efficacy and metabolomics, remains unclear.

OBJECTIVE

This study was aimed to investigate the metabolic mechanism of LBRD in treating depression.

MATERIALS AND METHODS

Network pharmacology was conducted using SwissTargetPrediction, DisGeNET, DrugBank, Metascape, etc., to construct component-target-pathway networks. The depression-like model was induced by intraperitoneal injection with lipopolysaccharide (LPS) (0.3 mg/kg) for 14 consecutive days. After the administration of LBRD (90 g/kg) and fluoxetine (2 mg/kg) for 14 days, we assessed behaviour and the levels of neurotransmitter, inflammatory cytokine and circulating stress hormone. Prefrontal metabolites of rats were detected by using liquid chromatography-mass spectrometry metabolomics method.

RESULTS

The results of network pharmacology showed that LBRD mainly acted on neurotransmitter and second messenger pathways. Compared to the model group, LBRD significantly ameliorated depressive phenotypes and increased the level of 5-HT (13.4%) and GABA (24.8%), as well as decreased IL-1β (30.7%), IL-6 (32.8%) and TNF-α (26.6%). Followed by LBRD treatment, the main metabolites in prefrontal tissue were contributed to retrograde endocannabinoid signalling, glycerophospholipid metabolism, glycosylphosphatidylinositol-anchor biosynthesis, autophagy signal pathway, etc.

DISCUSSION AND CONCLUSIONS

LBRD were effective at increasing neurotransmitter, attenuating proinflammatory cytokine and regulating glycerophospholipid metabolism and glutamatergic synapse, thereby ameliorating depressive phenotypes. This research will offer reference for elucidating the metabolomic mechanism underlying novel antidepressant agents contained LBRD formula.

1H-NMR-Based Metabolomics in Autism Spectrum Disorder and Pediatric Acute-Onset Neuropsychiatric Syndrome

We recently described a unique plasma metabolite profile in subjects with pediatric acute-onset neuropsychiatric syndrome (PANS), suggesting pathogenic models involving specific patterns of neurotransmission, neuroinflammation, and oxidative stress. Here, we extend the analysis to a group of patients with autism spectrum disorder (ASD), as a consensus has recently emerged around its immune-mediated pathophysiology with a widespread involvement of brain networks. This observational case-control study enrolled patients referred for PANS and ASD from June 2019 to May 2020, as well as neurotypical age and gender-matched control subjects. Thirty-four PANS outpatients, fifteen ASD outpatients, and twenty-five neurotypical subjects underwent physical and neuropsychiatric evaluations, alongside serum metabolomic analysis with 1H-NMR. In supervised models, the metabolomic profile of ASD was significantly different from controls (p = 0.0001), with skewed concentrations of asparagine, aspartate, betaine, glycine, lactate, glucose, and pyruvate. Metabolomic separation was also observed between PANS and ASD subjects (p = 0.02), with differences in the concentrations of arginine, aspartate, betaine, choline, creatine phosphate, glycine, pyruvate, and tryptophan. We confirmed a unique serum metabolomic profile of PANS compared with both ASD and neurotypical subjects, distinguishing PANS as a pathophysiological entity per se. Tryptophan and glycine appear as neuroinflammatory fingerprints of PANS and ASD, respectively. In particular, a reduction in glycine would primarily affect NMDA-R excitatory tone, overall impairing downstream glutamatergic, dopaminergic, and GABAergic transmissions. Nonetheless, we found metabolomic similarities between PANS and ASD that suggest a putative role of N-methyl-D-aspartate receptor (NMDA-R) dysfunction in both disorders. Metabolomics-based approaches could contribute to the identification of novel ASD and PANS biomarkers.

The molecular pathophysiology of depression and the new therapeutics

Major depressive disorder (MDD) is a highly prevalent and disabling disorder. Despite the many hypotheses proposed to understand the molecular pathophysiology of depression, it is still unclear. Current treatments for depression are inadequate for many individuals, because of limited effectiveness, delayed efficacy (usually two weeks), and side effects. Consequently, novel drugs with increased speed of action and effectiveness are required. Ketamine has shown to have rapid, reliable, and long-lasting antidepressant effects in treatment-resistant MDD patients and represent a breakthrough therapy for patients with MDD; however, concerns regarding its efficacy, potential misuse, and side effects remain. In this review, we aimed to summarize molecular mechanisms and pharmacological treatments for depression. We focused on the fast antidepressant treatment and clarified the safety, tolerability, and efficacy of ketamine and its metabolites for the MDD treatment, along with a review of the potential pharmacological mechanisms, research challenges, and future clinical prospects.

Develop a stepwise integrated method to screen biomarkers of Baihe-Dihuang Tang on the treatment of depression in Rats Applying with composition screened, untargeted and targeted metabolomics analysis

Baihe-Dihuang Tang is a commonly prescribed remedy for depression. In this study, component screening with untargeted and targeted metabolomics was used to to identify potential biomarkers for depression in chronic unpredictable mildly-stressed rats. Using this novel identification method, the screening of organic acids, lily saponins, iridoids, and other ingredients formed the basis for subsequent metabolomics research. Baihe-Dihuang Tang supplementation in chronic unpredictable mild-stress -induced depression models, increased their body weight, sucrose preference, brain-derived neurotrophic factor deposition, and spatial exploring. Untargeted metabolomics revealed that Baihe-Dihuang Tang exerts its antidepressant effects by regulating the levels of lipids, organic acids and its derivatives, and benzenoids in the brain, plasma, and urine of the depressed rats. Moreover, it also modulates the D-glutamine and D-glutamate metabolism and purine metabolism. Targeted metabolomics demonstrated significant reduction in L-glutamate levels in the brains of depressed rats. This could be a potential biomarker for depression. Baihe-Dihuang Tang alleviated depression by regulating the levels of L-glutamate, xanthine, and adenine in the brains of depressed rats. Together, these findings conclusively established the promising therapeutic effect of Baihe-Dihuang Tang on depression and also unraveled the underlying molecular mechanism of its potential antidepressant function. This article is protected by copyright. All rights reserved.

Changes in gut viral and bacterial species correlate with altered 1,2-diacylglyceride levels and structure in the prefrontal cortex in a depression-like non-human primate model

Major depressive disorder (MDD) is a debilitating mental disease, but its underlying molecular mechanisms remain obscure. Our previously established model of naturally occurring depression-like (DL) behaviors in Macaca fascicularis, which is characterized by microbiota-gut-brain (MGB) axis disturbances, can be used to interrogate how a disturbed gut ecosystem may impact the molecular pathology of MDD. Here, gut metagenomics were used to characterize how gut virus and bacterial species, and associated metabolites, change in depression-like monkey model. We identified a panel of 33 gut virus and 14 bacterial species that could discriminate the depression-like from control macaques. In addition, using lipidomic analyses of central and peripheral samples obtained from these animals, we found that the DL macaque were characterized by alterations in the relative abundance, carbon-chain length, and unsaturation degree of 1,2-diacylglyceride (DG) in the prefrontal cortex (PFC), in a brain region-specific manner. In addition, lipid-reaction analysis identified more active and inactive lipid pathways in PFC than in amygdala or hippocampus, with DG being a key nodal player in these lipid pathways. Significantly, co-occurrence network analysis showed that the DG levels may be relevant to the onset of negative emotions behaviors in PFC. Together our findings suggest that altered DG levels and structure in the PFC are hallmarks of the DL macaque, thus providing a new framework for understanding the gut microbiome's role in depression.

Plasma Metabolomics Reveals Diagnostic Biomarkers and Risk Factors for Esophageal Squamous Cell Carcinoma

Esophageal squamous carcinoma (ESCC) has a high morbidity and mortality rate. Identifying risk metabolites associated with its progression is essential for the early prevention and treatment of ESCC. A total of 373 ESCC, 40 esophageal squamous dysplasia (ESD), and 218 healthy controls (HC) subjects were enrolled in this study. Gas chromatography-mass spectrometry (GC/MS) was used to acquire plasma metabolic profiles. Receiver operating characteristic curve (ROC) and adjusted odds ratio (OR) were calculated to evaluate the potential diagnosis and prediction ability markers. The levels of alpha-tocopherol and cysteine were progressively decreased, while the levels of aminomalonic acid were progressively increased during the various stages (from precancerous lesions to advanced-stage) of exacerbation in ESCC patients. Alpha-tocopherol performed well for the differential diagnosis of HC and ESD/ESCC (AUROC>0.90). OR calculations showed that a high level of aminomalonic acid was not only a risk factor for further development of ESD to ESCC (OR>13.0) but also a risk factor for lymphatic metastasis in ESCC patients (OR>3.0). A low level of alpha-tocopherol was a distinguished independent risk factor of ESCC (OR< 0.5). The panel constructed by glycolic acid, oxalic acid, glyceric acid, malate and alpha-tocopherol performed well in distinguishing between ESD/ESCC from HC in the training and validation set (AUROC>0.95). In conclusion, the oxidative stress function was impaired in ESCC patients, and improving the body’s antioxidant function may help reduce the early occurrence of ESCC.

A Systematic Review of Circulatory microRNAs in Major Depressive Disorder: Potential Biomarkers for Disease Prognosis

Major depressive disorder (MDD) is a neuropsychiatric disorder, which remains challenging to diagnose and manage due to its complex endophenotype. In this aspect, circulatory microRNAs (cimiRNAs) offer great potential as biomarkers and may provide new insights for MDD diagnosis. Therefore, we systemically reviewed the literature to explore various cimiRNAs contributing to MDD diagnosis and underlying molecular pathways. A comprehensive literature survey was conducted, employing four databases from 2012 to January 2021. Out of 1004 records, 157 reports were accessed for eligibility criteria, and 32 reports meeting our inclusion criteria were considered for in-silico analysis. This study identified 99 dysregulated cimiRNAs in MDD patients, out of which 20 cimiRNAs found in multiple reports were selected for in-silico analysis. KEGG pathway analysis indicated activation of ALS, MAPK, p53, and P13K-Akt signaling pathways, while gene ontology analysis demonstrated that most protein targets were associated with transcription. In addition, chromosomal location analysis showed clustering of dysregulated cimiRNAs at proximity 3p22-p21, 9q22.32, and 17q11.2, proposing their coregulation with specific transcription factors primarily involved in MDD physiology. Further analysis of transcription factor sites revealed the existence of HIF-1, REST, and TAL1 in most cimiRNAs. These transcription factors are proposed to target genes linked with MDD, hypothesizing that first-wave cimiRNA dysregulation may trigger the second wave of transcription-wide changes, altering the protein expressions of MDD-affected cells. Overall, this systematic review presented a list of dysregulated cimiRNAs in MDD, notably miR-24-3p, let 7a-5p, miR-26a-5p, miR135a, miR-425-3p, miR-132, miR-124 and miR-16-5p as the most prominent cimiRNAs. However, various constraints did not permit us to make firm conclusions on the clinical significance of these cimiRNAs, suggesting the need for more research on single blood compartment to identify the biomarker potential of consistently dysregulated cimiRNAs in MDD, as well as the therapeutic implications of these in-silico insights.

A pilot exploration of multi-omics research of gut microbiome in major depressive disorders

The pathophysiology of major depressive disorder (MDD) remains obscure. Recently, the microbiota-gut-brain (MGB) axis's role in MDD has an increasing attention. However, the specific mechanism of the multi-level effects of gut microbiota on host metabolism, immunity, and brain structure is unclear. Multi-omics approaches based on the analysis of different body fluids and tissues using a variety of analytical platforms have the potential to provide a deeper understanding of MGB axis disorders. Therefore, the data of metagenomics, metabolomic, inflammatory factors, and MRI scanning are collected from the two groups including 24 drug-naïve MDD patients and 26 healthy controls (HCs). Then, the correlation analysis is performed in all omics. The results confirmed that there are many markedly altered differences, such as elevated Actinobacteria abundance, plasma IL-1β concentration, lipid, vitamin, and carbohydrate metabolism disorder, and diminished grey matter volume (GMV) of inferior frontal gyrus (IFG) in the MDD patients. Notably, three kinds of discriminative bacteria, Ruminococcus bromii, Lactococcus chungangensis, and Streptococcus gallolyticus have an extensive correlation with metabolome, immunology, GMV, and clinical symptoms. All three microbiota are closely related to IL-1β and lipids (as an example, phosphoethanolamine (PEA)). Besides, Lactococcus chungangensis is negatively related to the GMV of left IFG. Overall, this study demonstrate that the effects of gut microbiome exert in MDD is multifactorial.

MicroRNA-Messenger RNA Regulatory Network Mediates Disrupted TH17 Cell Differentiation in Depression

Accumulating evidence indicates an important role for microRNA (miRNA)-messenger RNA (mRNA) regulatory networks in human depression. However, the mechanisms by which these networks act are complex and remain poorly understood. We used data mining to identify differentially expressed miRNAs from GSE81152 and GSE152267 datasets, and differentially expressed mRNAs were identified from the Netherlands Study of Depression and Anxiety, the GlaxoSmithKline-High-Throughput Disease-specific target Identification Program, and the Janssen-Brain Resource Company study. We constructed a miRNA-mRNA regulatory network based on differentially expressed mRNAs that intersected with target genes of differentially expressed miRNAs, and then performed bioinformatics analysis of the network. The key candidate genes were assessed in the prefrontal cortex of chronic social defeat stress (CSDS) depression mice by quantitative real-time polymerase chain reaction (qRT-PCR). Three differentially expressed miRNAs were commonly identified across the two datasets, and 119 intersecting differentially expressed mRNAs were identified. A miRNA-mRNA regulatory network including these three key differentially expressed miRNAs and 119 intersecting differentially expressed mRNAs was constructed. Functional analysis of the intersecting differentially expressed mRNAs revealed that an abnormal inflammatory response characterized by disturbed T-helper cell 17 (Th17) differentiation was the primary altered biological function. qRT-PCR validated the decreased expression of Th17 cell differentiation-related genes, including interleukin (IL)17A, IL21, IL22, and IL1β, and the increased expression of retinoic acid receptor-related orphan receptor gamma-t (RORγt) in CSDS mice, which showed significant depressive- and anxiety-like behaviors. This study indicates that an abnormal inflammatory response characterized by disturbed Th17 cell differentiation is the primary altered biological process in major depressive disorder. Our findings indicate possible biomarkers and treatment targets and provide novel clues to understand the pathogenesis of major depressive disorder.

Effect of Coriander Plants on Human Emotions, Brain Electrophysiology, and Salivary Secretion

Simple Summary

This research aims to investigate the effects of coriander plants on human emotions and physiological activities. The results showed coriander plants could significantly reduce the angry sub-scores, alpha amylase and amino acids (arginine, proline, histidine, and taurine) concentrations in saliva. Theta (4–8 Hz) band activity of the cerebral cortex was significantly enhanced. Moreover, taurine significantly positively correlated with anger and negatively correlated with vigor. All the results signified that coriander plant could influence the activity of brain electrophysiological and salivary secretion through its VOCs to improve people’s negative emotions. This study will provide a theoretical basis for the living coriander plants have some therapeutic effect on the human psychological state.

Abstract

Coriander is a popular herb with versatile applications. However, the current research about coriander medicinal values have been mainly focusing on its extracts while lacking in the relationship between living coriander plants and emotion. Therefore, this study aims to investigate the effects of coriander plants on human emotions and physiological activities. The results showed that the main Volatile organic compounds (VOCs) of coriander plants were 2-ethyl-1-hexanol, d-limonene, eucalyptol, benzyl alcohol, Isophorone, dimethyl glutarate, α-terpineol, styrene, methyl methacrylate, α-pinene. Coriander plants could significantly reduce the angry sub-scores, alpha amylase and amino acids (arginine, proline, histidine, and taurine) concentrations in saliva. Theta (4–8 Hz) band activity of the cerebral cortex was significantly enhanced. Moreover, taurine significantly positively correlated with anger and negatively correlated with vigor. All the results signified that coriander plant could influence the activity of brain electrophysiological and salivary secretion through its VOCs to improve people’s negative emotions.

Simultaneous Analysis of the Metabolome and Lipidome Using Polarity Partition Two-Dimensional Liquid Chromatography-Mass Spectrometry

Comprehensive metabolic profiling is a considerable challenge for systems biology since the metabolites in biological samples have significant polarity differences. A heart-cutting two-dimensional liquid chromatography-mass spectrometry (2D-LC-MS) method-based polarity partition was established to analyze both the metabolome and lipidome in a single run. Based on the polarity partition strategy, metabolites with high polarity were retained and separated by one-dimensional hydrophilic chromatography, while low- and medium-polarity lipids were collected into a sample loop and injected into two-dimensional reversed-phase chromatography for separation. A simple online dilution strategy realized the online coupling of the 2D-LC-MS, which effectively solved band broadening and peak distortion caused by solvent incompatibility. Moreover, a dual gradient elution procedure was introduced to further broaden the coverage of low-polarity lipids. The metabolites' log P values, which this 2D-LC-MS method could analyze, ranged from -8.79 to 26.86. The feasibility of the 2D-LC-MS system was demonstrated by simultaneous analysis of the metabolome and lipidome in rat plasma related to depression. A total of 319 metabolites were determined within 40 min, including organic acids, nucleosides, carbohydrate derivatives, amino acids, lipids, and other organic compounds. Finally, 44 depression-related differential metabolites were screened. Compared with conventional LC-MS-based methods, the 2D-LC method covered over 99% of features obtained by two conventional methods. In addition, the selectivity and resolution of the hydrophilic metabolites were improved, and the matrix effects of the hydrophobic metabolites were reduced in the developed method. The results indicated that the established 2D-LC system is a powerful tool for comprehensive metabolomics studies.

Electromagnetic field exposure-induced depression features could be alleviated by heat acclimation based on remodeling the gut microbiota

BACKGROUND

Electromagnetic pollution cannot be ignored. Long-term low-dose electromagnetic field (EMF) exposure can cause central nervous system dysfunction without effective prevention.

MATERIALS/METHODS

Male C57BL/6J mice (6-8 weeks, 17-20 g) were used in this study. Depression-like and anxiety-like behaviors detected by behavioral experiments were compared among different treatments. 16S rRNA gene sequencing and non-targeted liquid chromatography-mass spectrometry (LC-MS) metabolomics were used to explore the relationship between EMF exposure and heat acclimation (HA) effects on gut microbes and serum metabolites.

RESULTS

Both EMF and HA regulated the proportions of p_Firmicutes and p_Bacteroidota. EMF exposure caused the proportions of 6 kinds of bacteria, such as g_Butyricicoccus and g_Anaerotruncus, to change significantly (p < 0.05). HA restored the balance of gut microbes that was affected by EMF exposure and the proportion of probiotics (g_Lactobacillus) increased significantly (p < 0.01). Serum metabolite analysis suggested that HA alleviated the disturbance of serum metabolites (such as cholesterol and D-mannose) induced by EMF exposure. Both the metabolic KEGG pathways and PICRUSt functional analysis demonstrated that tryptophan metabolism, pyrimidine metabolism and amino acid biosynthesis were involved.

CONCLUSIONS

EMF exposure not only led to depression-like neurobehavioral disorders, but also to gut microbiota imbalance. HA alleviated the depression features caused by EMF exposure. Based on the analysis of gut microbiota associated with serum metabolites, we speculated that gut microbiota might play a vital role in the cross-tolerance provided by HA.

Proteomics analysis of the gut-brain axis in a gut microbiota-dysbiosis model of depression

Major depressive disorder (MDD) is a serious mental illness. Increasing evidence from both animal and human studies suggested that the gut microbiota might be involved in the onset of depression via the gut-brain axis. However, the mechanism in depression remains unclear. To explore the protein changes of the gut-brain axis modulated by gut microbiota, germ-free mice were transplanted with gut microbiota from MDD patients to induce depression-like behaviors. Behavioral tests were performed following fecal microbiota transplantation. A quantitative proteomics approach was used to examine changes in protein expression in the prefrontal cortex (PFC), liver, cecum, and serum. Then differential protein analysis and weighted gene coexpression network analysis were used to identify microbiota-related protein modules. Our results suggested that gut microbiota induced the alteration of protein expression levels in multiple tissues of the gut-brain axis in mice with depression-like phenotype, and these changes of the PFC and liver were model specific compared to chronic stress models. Gene ontology enrichment analysis revealed that the protein changes of the gut-brain axis were involved in a variety of biological functions, including metabolic process and inflammatory response, in which energy metabolism is the core change of the protein network. Our data provide clues for future studies in the gut-brain axis on protein level and deepen the understanding of how gut microbiota cause depression-like behaviors.

Detection and verification of coexisting diagnostic markers in plasma and serum of patients with non-small-cell lung cancer

Aim: To screen and identify the potential biomarkers co-existing in plasma and serum of patients with non-small-cell lung cancer (NSCLC), and establish appropriate diagnostic models. Methods: A cohort of 195 plasma samples and 180 serum samples were obtained from healthy controls (HCs), adenocarcinoma (AdC) and squamous cell carcinoma (SqCC) patients enrolled from the First Affiliated Hospital of Nanjing Medical University. Metabolites in plasma and serum were analyzed by GC-MS. Results: Hypoxanthine was found to have good performance in the differential diagnosis of NSCLC (including AdC and SqCC) and HC (area under the receiver operating characteristic [AUROC] ≥0.85). Combinations of metabolites could be used for differential diagnosis of NSCLC and HC (AUROC >0.93), AdC and HC (AUROC >0.91), SqCC and HC (AUROC >0.95), AdC and SqCC (AUROC >0.72). Conclusions: Metabolomics based on GC-MS can screen and identify the differential metabolites coexisting in plasma and serum of patients with NSCLC, and prediction models established by this method can be used for the differential diagnosis of patients with NSCLC.

Impact of Inosine on Chronic Unpredictable Mild Stress-Induced Depressive and Anxiety-Like Behaviors With the Alteration of Gut Microbiota

Current antidepressants do not confer a clear advantage in children and adolescents with major depressive disorder (MDD). Accumulating evidence highlights the potential antidepressant-like effects of inosine on adult MDD, and gut microbiomes are significantly associated with MDD via the microbiota-gut-brain axis. However, few studies have investigated possible associations between inosine and gut microbiota in adolescents with MDD. The current study investigated the potential antidepressant effects of inosine in adolescent male C57BL/6 mice. After 4 weeks of chronic unpredictable mild stress (CUMS) stimulation, the mice were assessed by body weight, the sucrose preference test (SPT), open field test, and the elevated plus maze (EPM). The microbiota compositions of feces were determined by 16S rRNA gene sequencing. Inosine significantly improved CUMS-induced depressive and anxiety-like behaviors in adolescent mice including SPT and EPM results. Fecal microbial composition differed in the CON+saline, CUMS+saline, and CUMS+inosine groups, which were characterized by 126 discriminative amplicon sequence variants belonging to Bacteroidetes and Firmicute at the phylum level and Muribaculaceae and Lachnospiraceae at the family level. Muribaculaceae was positively associated with depressive and anxiety-like behaviors. KEGG functional analysis suggested that inosine might affect gut microbiota through carbohydrate metabolism and lipid metabolism pathways. The results of the study indicated that inosine improved depressive and anxiety-like behaviors in adolescent mice, in conjunction with the alteration of fecal microbial composition. Our findings may provide a novel perspective on the antidepressant effects of inosine in children and adolescents.

Classifying depression using blood biomarkers: A large population study

BACKGROUND

Depression is a common mood disorder characterized by persistent low mood or lack of interest in activities. People with other chronic medical conditions such as obesity and diabetes are at greater risk of depression. Diagnosing depression can be a challenge for primary care providers and others who lack specialized training for these disorders and have insufficient time for in-depth clinical evaluation. We aimed to create a more objective low-cost diagnostic tool based on patients' characteristics and blood biomarkers.

METHODS

Blood biomarker results were obtained from the National Health and Nutrition Examination Survey (NHANES, 2007-2016). A prediction model utilizing random forest (RF) in NHANES (2007-2014) to identify depression was derived and validated internally using out-of-bag technique. Afterwards, the model was validated externally using a validation dataset (NHANES, 2015-2016). We performed four subgroup comparisons (full dataset, overweight and obesity dataset (BMI≥25), diabetes dataset, and metabolic syndrome dataset) then selected features using backward feature selection from RF.

RESULTS

Family income, Gamma-glutamyl transferase (GGT), glucose, Triglyceride, red cell distribution width (RDW), creatinine, Basophils count or percent, Eosinophils count or percent, and Bilirubin were the most important features from four models. In the training set, AUC from full, overweight and obesity, diabetes, and metabolic syndrome datasets were 0.83, 0.80, 0.82, and 0.82, respectively. In the validation set, AUC were 0.69, 0.63, 0.66, and 0.64, respectively.

CONCLUSION

Results of routine blood laboratory tests had good predictive value for distinguishing depression cases from control groups not only in the general population, but also individuals with metabolism-related chronic diseases.

Investigation on the stability in plant metabolomics with a special focus on freeze-thaw cycles: LC-MS and NMR analysis to Cassiae Semen (Cassia obtusifolia L.) seeds as a case study

Metabolomics is a rapid and sensitive tool for the detection of dynamic metabolic compositions in the study of systemic metabolic consequences. However, it is also susceptible to a tiny variation of pre-analytical handling procedures. To provide reproducible results, specific knowledge on metabolites perturbance along with different freeze-thaw cycles (FTCs) is needed for further metabolomics studies. In this paper, five FTCs of germinated Cassiae Semen (CS) were chosen as a case study to investigate the influence of FTC effect based on UHPLC-Q-Orbitrap-MS and NMR technologies. A total of 108 metabolites were relatively quantified by LC-MS and NMR analyses. Principal component analysis (PCA) showed that the first and second FTC samples are welly separated from the other groups; however, the extent of FTC-induced effects are smaller after the third cycle. Upon five consecutive FTCs, alterations which consisted of decreased stachyose, sucrose, norrubrofusarin-6-O-β-d-glucopyranoside, and quercetin 3-(3″-acetylgalactoside), as well as increased phenylalanine, leucine, isoleucine, methionine, phenylalanine, mannose, gluconic acid, and valine, could be observed. FTC does not exert the same effect on all metabolites. Although a large number of secondary metabolites were stable when subjected to five FTCs, FTC effects may lead to false-positive in the discovery of biomarker. In the case of reusing plant seed samples, no more than three consecutive freeze-thaw cycles were found advisable. This work provides unique perspectives on the FTC effects, which may fill in some existing gaps in the knowledge of the stability of plant metabolites during sample pre-handling.

Associations of depression status with plasma levels of candidate lipid and amino acid metabolites: a meta-analysis of individual data from three independent samples of US postmenopausal women

Recent animal and small clinical studies have suggested depression is related to altered lipid and amino acid profiles. However, this has not been examined in a population-based sample, particularly in women. We identified multiple metabolites associated with depression as potential candidates from prior studies. Cross-sectional data from three independent samples of postmenopausal women were analyzed, including women from the Women's Health Initiative-Observational Study (WHI-OS, n = 926), the WHI-Hormone Trials (WHI-HT; n = 1,325), and the Nurses' Health Study II Mind-Body Study (NHSII-MBS; n = 218). Positive depression status was defined as having any of the following: elevated depressive symptoms, antidepressant use, or depression history. Plasma metabolites were measured using liquid chromatography-tandem mass spectrometry (21 phosphatidylcholines (PCs), 7 lysophosphatidylethanolamines, 5 ceramides, 3 branched chain amino acids, and 9 neurotransmitters). Associations between depression status and metabolites were evaluated using multivariable linear regression; results were pooled by random-effects meta-analysis with multiple testing adjustment using the false discovery rate (FDR). Prevalence rates of positive depression status were 24.4% (WHI-OS), 25.7% (WHI-HT), and 44.7% (NHSII-MBS). After multivariable adjustment, positive depression status was associated with higher levels of glutamate and PC 36 : 1/38 : 3, and lower levels of tryptophan and GABA-to-glutamate and GABA-to-glutamine ratio (FDR-p < 0.05). Positive associations with LPE 18 : 0/18 : 1 and inverse associations with valine and serotonin were also observed, although these associations did not survive FDR adjustment. Associations of positive depression status with several candidate metabolites including PC 36 : 1/38 : 3 and amino acids involved in neurotransmission suggest potential depression-related metabolic alterations in postmenopausal women, with possible implications for later chronic disease.

Blood Plasma Metabolic Profile of Newborns with Hypoxic-Ischaemic Encephalopathy by GC-MS

Background

Early diagnosis of hypoxic-ischaemic encephalopathy (HIE) is crucial in preventing neurodevelopmental disabilities and reducing morbidity and mortality. The study was to investigate the plasma metabolic signatures in the peripheral blood of HIE newborns and explore the potential diagnostic biomarkers.

Method

In the present study, 24 newborns with HIE and 24 healthy controls were recruited. The plasma metabolites were measured by gas chromatography-mass spectrometry (GC-MS), and the raw data was standardized by the EigenMS method. Significantly differential metabolites were identified by multivariate statistics. Pathway enrichment was performed by bioinformatics analysis. Meanwhile, the diagnostic value of candidate biomarkers was evaluated.

Result

The multivariate statistical models showed a robust capacity to distinguish the HIE cases from the controls. 52 metabolites were completely annotated. 331 significantly changed pathways were enriched based on seven databases, including 33 overlapped pathways. Most of them were related to amino acid metabolism, energy metabolism, neurotransmitter biosynthesis, pyrimidine metabolism, the regulation of HIF by oxygen, and GPCR downstream signaling. 14 candidate metabolites showed great diagnostic potential on HIE. Among them, alpha-ketoglutaric acid has the potential to assess the severity of HIE in particular.

Conclusion

The blood plasma metabolic profile could comprehensively reflect the metabolic disorders of the whole body under hypoxia-ischaemic injury. Several candidate metabolites may serve as promising biomarkers for the early diagnosis of HIE. Further validation based on large clinical samples and the establishment of guidelines for the clinical application of mass spectrometry data standardization methods are imperative in the future.

Treatment Effect of Exercise Intervention for Female College Students with Depression: Analysis of Electroencephalogram Microstates and Power Spectrum

This paper aims to assess the effect of exercise intervention on the improvement of college students with depression and to explore the change characteristics of microstates and the power spectrum in their resting-state electroencephalogram (EEG). Forty female college students with moderate depression were screened according to the Beck Depression Inventory-II (BDI-II) and Depression Self-Rating Scale (SDS) scores, and half of them received an exercise intervention for 18 weeks. The study utilized an EEG to define the resting-state networks, and the scores of all the participants were tracked during the intervention. Compared with those in the depression group, the power spectrum values in the θ and α bands were significantly decreased (p < 0.05), and the duration of microstate C increased significantly (p < 0.05), while the frequency of microstate B decreased significantly (p < 0.05) in the exercise intervention group. The transition probabilities showed that the exercise intervention group had a higher probability from B to D than those in the depression group (p < 0.01). In addition, the power of the δ and α bands were negatively correlated with the occurrence of microstate C (r = −0.842, p < 0.05 and r = −0.885, p < 0.01, respectively), and the power of the β band was positively correlated with the duration of microstate C (r = 0.900, p < 0.01) after exercise intervention. Our results suggest that the decreased duration of microstate C and the increased α power in depressed students are associated with reduced cognitive ability, emotional stability, and brain activity. Depression symptoms were notably improved after exercise intervention, thus providing a more scientific index for the research, rehabilitation mechanisms, and treatment of depression.

A metabolomic study on the anti-depressive effects of two active components from Chrysanthemum morifolium

Chrysanthemum morifolium (Chr) is a traditional Chinese medicine (TCM) that has been used in the treatment of inflammation-linked diseases for hundreds of years. Naringenin (Nar) and apigenin (Api) are the major active components in aqueous extracts of C. morifolium. The aim of our study was to clarify the roles of Chr, Nar and Api in ameliorating depression-like behaviour induced by corticosterone. First, the behavioural and biochemical indicators closely related to depression were examined to evaluate the therapeutic effects of Chr/Nar/Api on a depression model. Then, a metabolomics approach was utilized to screen for biomarkers and related pathways between a control group and Chr/Nar/Api groups. The comprehensive results revealed that Chr/Nar/Api exerted anti-depressant effects through interfering with tryptophan metabolism, arginine and prolinemetabolism, citrate cycle, niacin and niacinamide metabolism, phenylalanine metabolism, and alanine, aspartate and glutamate metabolism. The mechanism of Chr/Api/Nar in the treatment of depression was elucidated based on material and energy metabolism. Moreover, Nar could be used as a substitute for Chr for reversing depression-like behaviour, and Api was similar to a positive drug in terms of function on depression. The integrated metabolomics approach demonstrated here should be an effective method for interpreting the function of herbs from TCM and clarifying the mechanism of their components in future studies.

The gut microbiome modulates gut-brain axis glycerophospholipid metabolism in a region-specific manner in a nonhuman primate model of depression

Emerging research demonstrates that microbiota-gut-brain (MGB) axis changes are associated with depression onset, but the mechanisms underlying this observation remain largely unknown. The gut microbiome of nonhuman primates is highly similar to that of humans, and some subordinate monkeys naturally display depressive-like behaviors, making them an ideal model for studying these phenomena. Here, we characterized microbial composition and function, and gut-brain metabolic signatures, in female cynomolgus macaque (Macaca fascicularis) displaying naturally occurring depressive-like behaviors. We found that both microbial and metabolic signatures of depressive-like macaques were significantly different from those of controls. The depressive-like monkeys had characteristic disturbances of the phylum Firmicutes. In addition, the depressive-like macaques were characterized by changes in three microbial and four metabolic weighted gene correlation network analysis (WGCNA) clusters of the MGB axis, which were consistently enriched in fatty acyl, sphingolipid, and glycerophospholipid metabolism. These microbial and metabolic modules were significantly correlated with various depressive-like behaviors, thus reinforcing MGB axis perturbations as potential mediators of depression onset. These differential brain metabolites were mainly mapped into the hippocampal glycerophospholipid metabolism in a region-specific manner. Together, these findings provide new microbial and metabolic frameworks for understanding the MGB axis' role in depression, and suggesting that the gut microbiome may participate in the onset of depressive-like behaviors by modulating peripheral and central glycerophospholipid metabolism.

TLR4 Knockout Attenuates BDL-induced Liver Cholestatic Injury through Amino Acid and Choline Metabolic Pathways

The exact mechanism by which knockout of Toll-like receptor 4 (TLR4) attenuates the liver injury remains unclear. The present study aimed to examine the role of TLR4 in the pathogenesis of bile duct ligation (BDL)-induced liver cholestatic injury and the underlying mechanism. Wild type (WT) mice and TLR4 knockout (TLR4-KO) mice were used for the establishment of the BDL model. Metabolomics were applied to analyze the changes of small molecular metabolites in the serum and liver of the two groups. The serum biochemical indexes and the HE staining results of liver tissue showed that liver damage was significantly reduced in TLR4-KO mice after BDL when compared with that in WT mice. The metabolite analysis results showed that TLR4 KO could maintain the metabolisms of amino acids- and choline-related metabolites. After BDL, the amino acids- and choline-related metabolites, especially choline and 3-hydroxybutyrate, were significantly increased in WT mice (both in serum and liver), but these metabolites in the liver of TLR4-KO mice after BLD were not significant different from those before BLD. In conclusion, TLR4 KO could attenuate BDL-induced liver cholestatic injury through regulating amino acid and choline metabolic pathways.

Alteration of lipids and amino acids in plasma distinguish schizophrenia patients from controls: A targeted metabolomics study

BACKGROUND

Schizophrenia (SCZ) is a serious psychiatric disorder. Metabolite disturbance is an important pathogenic factor in schizophrenic patients. In this study, we aim to identify plasma lipid and amino acid biomarkers for SCZ using targeted metabolomics.

METHODS

Plasma from 76 SCZ patients and 50 matched controls were analyzed using the LC/MS-based multiple reaction monitoring (MRM) metabolomics approach. A total of 182 targeted metabolites, including 22 amino acids and 160 lipids or lipid-related metabolites were observed. We used binary logistic regression analysis to determine whether the lipid and amino acid biomarkers could discriminate SCZ patients from controls. The area under the curve (AUC) from receiver operation characteristic (ROC) curve analysis was conducted to evaluate the diagnostic performance of the biomarkers panel.

RESULTS

We identified 19 significantly differentially expressed metabolites between the SCZ patients and the controls (false discovery rate < 0.05), including one amino acid and 18 lipids or lipid-related metabolites. The binary logistic regression-selected panel showed good diagnostic performance in the drug-naïve group (AUC = 0.936) and all SCZ patients (AUC = 0.948), especially in the drug-treated group (AUC = 0.963).

CONCLUSIONS

Plasma lipids and amino acids showed significant dysregulation in SCZ, which could effectively discriminate SCZ patients from controls. The LC/MS/MS-based approach provides reliable data for the objective diagnosis of SCZ.

Identification of potential serum biomarkers for simultaneously classifying lung adenocarcinoma, squamous cell carcinoma and small cell carcinoma

BACKGROUND

Histological subtypes of lung cancer are crucial for making treatment decisions. However, multi-subtype classifications including adenocarcinoma (AC), squamous cell carcinoma (SqCC) and small cell carcinoma (SCLC) were rare in the previous studies. This study aimed at identifying and screening potential serum biomarkers for the simultaneous classification of AC, SqCC and SCLC.

PATIENTS AND METHODS

A total of 143 serum samples of AC, SqCC and SCLC were analyzed by 1HNMR and UPLC-MS/MS. The stepwise discriminant analysis (DA) and multilayer perceptron (MLP) were employed to screen the most efficient combinations of markers for classification.

RESULTS

The results of non-targeted metabolomics analysis showed that the changes of metabolites of choline, lipid or amino acid might contribute to the classification of lung cancer subtypes. 17 metabolites in those pathways were further quantified by UPLC-MS/MS. DA screened out that serum xanthine, S-adenosyl methionine (SAM), carcinoembryonic antigen (CEA), neuron-specific enolase (NSE) and squamous cell carcinoma antigen (SCC) contributed significantly to the classification of AC, SqCC and SCLC. The average accuracy of 92.3% and the area under the receiver operating characteristic curve of 0.97 would be achieved by MLP model when a combination of those five variables as input parameters.

CONCLUSION

Our findings suggested that metabolomics was helpful in screening potential serum markers for lung cancer classification. The MLP model established can be used for the simultaneous diagnosis of AC, SqCC and SCLC with high accuracy, which is worthy of further study.

An integrated meta-analysis of peripheral blood metabolites and biological functions in major depressive disorder

Major depressive disorder (MDD) is a serious mental illness, characterized by high morbidity, which has increased in recent decades. However, the molecular mechanisms underlying MDD remain unclear. Previous studies have identified altered metabolic profiles in peripheral tissues associated with MDD. Using curated metabolic characterization data from a large sample of MDD patients, we meta-analyzed the results of metabolites in peripheral blood. Pathway and network analyses were then performed to elucidate the biological themes within these altered metabolites. We identified 23 differentially expressed metabolites between MDD patients and controls from 46 studies. MDD patients were characterized by higher levels of asymmetric dimethylarginine, tyramine, 2-hydroxybutyric acid, phosphatidylcholine (32:1), and taurochenodesoxycholic acid and lower levels of L-acetylcarnitine, creatinine, L-asparagine, L-glutamine, linoleic acid, pyruvic acid, palmitoleic acid, L-serine, oleic acid, myo-inositol, dodecanoic acid, L-methionine, hypoxanthine, palmitic acid, L-tryptophan, kynurenic acid, taurine, and 25-hydroxyvitamin D compared with controls. L-tryptophan and kynurenic acid were consistently downregulated in MDD patients, regardless of antidepressant exposure. Depression rating scores were negatively associated with decreased levels of L-tryptophan. Pathway and network analyses revealed altered amino acid metabolism and lipid metabolism, especially for the tryptophan-kynurenine pathway and fatty acid metabolism, in the peripheral system of MDD patients. Taken together, our integrated results revealed that metabolic changes in the peripheral blood were associated with MDD, particularly decreased L-tryptophan and kynurenic acid levels, and alterations in the tryptophan-kynurenine and fatty acid metabolism pathways. Our findings may facilitate biomarker development and the elucidation of the molecular mechanisms that underly MDD.

Landscapes of bacterial and metabolic signatures and their interaction in major depressive disorders

Gut microbiome disturbances have been implicated in major depressive disorder (MDD). However, little is known about how the gut virome, microbiome, and fecal metabolome change, and how they interact in MDD. Here, using whole-genome shotgun metagenomic and untargeted metabolomic methods, we identified 3 bacteriophages, 47 bacterial species, and 50 fecal metabolites showing notable differences in abundance between MDD patients and healthy controls (HCs). Patients with MDD were mainly characterized by increased abundance of the genus Bacteroides and decreased abundance of the genera Blautia and Eubacterium These multilevel omics alterations generated a characteristic MDD coexpression network. Disturbed microbial genes and fecal metabolites were consistently mapped to amino acid (γ-aminobutyrate, phenylalanine, and tryptophan) metabolism. Furthermore, we identified a combinatorial marker panel that robustly discriminated MDD from HC individuals in both the discovery and validation sets. Our findings provide a deep insight into understanding of the roles of disturbed gut ecosystem in MDD.

Lipid Profile, Lipoprotein Subfractions, and Fluidity of Membranes in Children and Adolescents with Depressive Disorder: Effect of Omega-3 Fatty Acids in a Double-Blind Randomized Controlled Study

Depressive disorder (DD) is a psychiatric disorder whose molecular basis is not fully understood. It is assumed that reduced consumption of fish and omega-3 fatty acids (FA) is associated with DD. Other lipids such as total cholesterol (TCH), LDL-, and HDL-cholesterols (LDL-CH, HDL-CH) also play a role in depression. The primary endpoint of the study was the effect of omega-3 FA on the severity of depression in children and adolescents. This study aimed to investigate the secondary endpoint, relationship between depressive disorder symptoms and lipid profile, LDL- and HDL-cholesterol subfractions, Paraoxonase 1 (PON1) activities, and erythrocyte membrane fluidity in 58 depressed children and adolescents (calculated by the statistical program on the effect size), as well as the effect of omega-3 FA on the monitored parameters. Depressive symptoms were assessed by the Children’s Depression Inventory (CDI), lipid profile by standard biochemical procedures, and LDL- and HDL-subfractions by the Lipoprint system. Basic biochemical parameters including lipid profile were compared with levels in 20 healthy children and were in the physiological range. Improvement of symptoms in the group supplemented with a fish oil emulsion rich in omega-3 FA in contrast to omega-6 FA (emulsion of sunflower oil) has been observed. We are the first to report that omega-3 FAs, but not omega-6 FA, increase large HDL subfractions (anti-atherogenic) after 12 weeks of supplementation and decrease small HDL subfractions (proatherogenic) in depressed children. We found a negative correlation between CDI score and HDL-CH and the large HDL subfraction, but not LDL-CH subfractions. CDI score was not associated with erythrocyte membrane fluidity. Our results suggest that HDL-CH and its subfractions, but not LDL-CH may play a role in the pathophysiology of depressive disorder. The study was registered under ISRCTN81655012.

Gut microbiota and fecal metabolites in captive and wild North China leopard (Panthera pardus japonensis) by comparsion using 16 s rRNA gene sequencing and LC/MS-based metabolomics

Background

Gut microbes significantly contribute to nutrient digestion and absorption, intestinal health and immunity, and are essential for the survival and environmental adaptation of wild animals. However, there are few studies on the gut microbiota of captive and wild North China leopard (Panthera pardus japonensis).

Results

A total of 10 mainly bacterial phyla were identified in the fecal microbiota of North China leopard, Lachnoclostridium (p = 0.003), Peptoclostridium (p = 0.005), Bacteroides (p = 0.008), Fusobacterium (p = 0.017) and Collinsella (p = 0.019) were significantly higher than those of wild North China leopard. Distinct differences in the fecal metabolic phenotypes of captive and wild North China leopard were found, such as content of l-methionine, n-acetyl-l-tyrosine, pentadecanoic acid and oleic acid. Differentially abundant gut microbes were associated with fecal metabolites, especially the bacteria in Firmicutes and Bacteroidetes, involved in the metabolism of N-acetyl-L-alanine and D-quinovose.

Conclusion

This study reports for the first time the differences in gut microbiota abundance between captive and wild North China leopard, as well as significant differences in fecal metabolic phenotypes between two groups.

Analysis of biomarkers and metabolic pathways in patients with unstable angina based on ultra‑high‑performance liquid chromatography‑quadrupole time‑of‑flight mass spectrometry

Unstable angina (UA) is a coronary disease with a high mortality and morbidity worldwide. The present study aimed to use non-invasive techniques to identify urine biomarkers in patients with UA, so as to provide more information for the early diagnosis and treatment of the disease. Based on metabolomics, urine samples from 28 patients with UA and 28 healthy controls (HCs) were analyzed using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS). A total of 16 significant biomarkers that could distinguish between patients with UA and HCs, including D-glucuronic acid, creatinine, succinic acid and N-acetylneuraminic acid, were identified. The major metabolic pathways associated with UA were subsequently analyzed by non-targeted metabolomics. The results demonstrated that amino acid and energy metabolism, fatty acid metabolism, purine metabolism and steroid hormone biosynthetic metabolism may serve important roles in UA. The results of the current study may provide a theoretical basis for the early diagnosis of UA and novel treatment strategies for clinicians. The trial was registered with the Chinese Clinical Trial Registration Center (registration no. ChiCTR-ROC-17013957) at Tianjin University of Traditional Chinese Medicine.

Exploring serum metabolic markers for the discrimination of ccRCC from renal angiomyolipoma by metabolomics

Aim: The discrimination of renal cell carcinoma from renal angiomyolipoma (RAML) is crucial for the effective treatment of each. Materials & methods: Serum samples were analyzed by nuclear magnetic resonance spectroscopy-based metabolomics and a number of metabolites were further quantified by HPLC-UV. Results: Clear-cell renal carcinoma (ccRCC) was characterized by drastic disruptions in energy, amino acids, creatinine and uric acid metabolic pathways. A logistic model for the differential diagnosis of RAML from ccRCC was established using the combination of serum levels of uric acid, the ratio of uric acid to hypoxanthine and the ratio of hypoxanthine to creatinine as variables with area under the curve of the receiver operating characteristic curve value of 0.907. Conclusion: Alterations in serum purine metabolites may be used as potential metabolic markers for the differential diagnosis of ccRCC and RAML.

Extracellular Matrix and Oxidative Phosphorylation: Important Role in the Regulation of Hypothalamic Function by Gut Microbiota

Background

In previous studies, our team examined the gut microbiota of healthy individuals and depressed patients using fecal microbiota transplantation of germ-free (GF) mice. Our results showed that depression-like and anxiety-like behavioral phenotypes of host mice were increased, but the molecular mechanism by which gut microbiota regulate host behavioral phenotypes is still unclear.

Methods

To investigate the molecular mechanism by which gut microbiota regulate host brain function, adult GF mice were colonized with fecal samples derived from healthy control (HC) individuals or patients with major depressive disorder (MDD). Transcriptomic profiling of hypothalamus samples was performed to detect differentially expressed genes (DEGs). qRT-PCR was used for validation experiments.

Results

Colonization germ-free (CGF) mice had 243 DEGs compared with GF mice. The most enriched KEGG pathways associated with upregulated genes were "protein digestion and absorption," "extracellular matrix (ECM)-receptor interaction," and "focal adhesion." MDD mice had 642 DEGs compared with HC mice. The most enriched KEGG pathways associated with upregulated genes in MDD mice were also "protein digestion and absorption," "ECM-receptor interaction," and "focal adhesion." Meanwhile, the most enriched KEGG pathway associated with downregulated genes in these mice was "oxidative phosphorylation," and genes related to this pathway were found to be highly correlated in PPI network analysis.

Conclusion

In summary, our findings suggested that regulation of ECM is a key mechanism shared by different gut microbiota and that inhibition of energy metabolism in the hypothalamus by gut microbiota derived from MDD patients is a potential mechanism of behavioral regulation and depression.

Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection

To solve major limitations in algorithms for the metabolite-based prediction of psychiatric phenotypes, a novel prediction model for depressive symptoms based on nonlinear feature selection machine learning, the Hilbert-Schmidt independence criterion least absolute shrinkage and selection operator (HSIC Lasso) algorithm, was developed and applied to a metabolomic dataset with the largest sample size to date. In total, 897 population-based subjects were recruited from the communities affected by the Great East Japan Earthquake; 306 metabolite features (37 metabolites identified by nuclear magnetic resonance measurements and 269 characterized metabolites based on the intensities from mass spectrometry) were utilized to build prediction models for depressive symptoms as evaluated by the Center for Epidemiologic Studies-Depression Scale (CES-D). The nested fivefold cross-validation was used for developing and evaluating the prediction models. The HSIC Lasso-based prediction model showed better predictive power than the other prediction models, including Lasso, support vector machine, partial least squares, random forest, and neural network. L-leucine, 3-hydroxyisobutyrate, and gamma-linolenyl carnitine frequently contributed to the prediction. We have demonstrated that the HSIC Lasso-based prediction model integrating nonlinear feature selection showed improved predictive power for depressive symptoms based on metabolome data as well as on risk metabolites based on nonlinear statistics in the Japanese population. Further studies should use HSIC Lasso-based prediction models with different ethnicities to investigate the generality of each risk metabolite for predicting depressive symptoms.