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Background: Major depressive disorder (MDD) is a highly heterogenous disease, both in terms of clinical profiles and pathobiological alterations. Recently, immunometabolic dysregulations were shown to be correlated with atypical, energy-related symptoms but less so with the Melancholic or Anxious distress symptom dimensions of depression in The Netherlands Study of Depression and Anxiety (NESDA) study. In this study, we aimed to replicate these immunometabolic associations and to characterize the metabolomic correlates of each of the three MDD dimensions. Methods: Using three clinical rating scales, Melancholic, and Anxious distress, and Immunometabolic (IMD) dimensions were characterized in 158 patients who participated in the Predictors of Remission to Individual and Combined Treatments (PReDICT) study and from whom plasma and serum samples were available. The NESDA-defined inflammatory index, a composite measure of interleukin-6 and C-reactive protein, was measured from pre-treatment plasma samples and a metabolomic profile was defined using serum samples analyzed on three metabolomics platforms targeting fatty acids and complex lipids, amino acids, acylcarnitines, and gut microbiome-derived metabolites among other metabolites of central metabolism. Results: The IMD clinical dimension and the inflammatory index were positively correlated (r = 0.19, p = 0.019) after controlling for age, sex, and body mass index, whereas the Melancholic and Anxious distress dimensions were not, replicating the previous NESDA findings. The three symptom dimensions had distinct metabolomic signatures using both univariate and set enrichment statistics. IMD severity correlated mainly with gut-derived metabolites and a few acylcarnitines and long chain saturated free fatty acids. Melancholia severity was significantly correlated with several phosphatidylcholines, primarily the ether-linked variety, lysophosphatidylcholines, as well as several amino acids. Anxious distress severity correlated with several medium and long chain free fatty acids, both saturated and polyunsaturated ones, sphingomyelins, as well as several amino acids and bile acids. Conclusion: The IMD dimension of depression appears reliably associated with markers of inflammation. Metabolomics provides powerful tools to inform about depression heterogeneity and molecular mechanisms related to clinical dimensions in MDD, which include a link to gut microbiome and lipids implicated in membrane structure and function.

Age at depressive onset (AAO) corresponds to unique symptomatology and clinical outcomes. Integration of genome-wide association study (GWAS) results with additional “omic” measures to evaluate AAO has not been reported and may reveal novel markers of susceptibility and/or resistance to major depressive disorder (MDD). To address this gap, we integrated genomics with metabolomics using data-driven network analysis to characterize and differentiate MDD based on AAO. This study first performed two GWAS for AAO as a continuous trait in (a) 486 adults from the Pharmacogenomic Research Network-Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS), and (b) 295 adults from the Combining Medications to Enhance Depression Outcomes (CO-MED) study. Variants from top signals were integrated with 153 p180-assayed metabolites to establish multi-omics network characterizations of early (

Selective serotonin reuptake inhibitors (SSRIs) are standard of care for major depressive disorder (MDD) pharmacotherapy, but only approximately half of these patients remit on SSRI therapy. Our previous genome-wide association study identified a single-nucleotide polymorphism (SNP) signal across the glutamate-rich 3 (ERICH3) gene that was nearly genome-wide significantly associated with plasma serotonin (5-HT) concentrations, which were themselves associated with SSRI response for MDD patients enrolled in the Mayo Clinic PGRN-AMPS SSRI trial. In this study, we performed a meta-analysis which demonstrated that those SNPs were significantly associated with SSRI treatment outcomes in four independent MDD trials. However, the function of ERICH3 and molecular mechanism(s) by which it might be associated with plasma 5-HT concentrations and SSRI clinical response remained unclear. Therefore, we characterized the human ERICH3 gene functionally and identified ERICH3 mRNA transcripts and protein isoforms that are highly expressed in central nervous system cells. Coimmunoprecipitation identified a series of ERICH3 interacting proteins including clathrin heavy chain which are known to play a role in vesicular function. Immunofluorescence showed ERICH3 colocalization with 5-HT in vesicle-like structures, and ERICH3 knock-out dramatically decreased 5-HT staining in SK-N-SH cells as well as 5-HT concentrations in the culture media and cell lysates without changing the expression of 5-HT synthesizing or metabolizing enzymes. Finally, immunofluorescence also showed ERICH3 colocalization with dopamine in human iPSC-derived neurons. These results suggest that ERICH3 may play a significant role in vesicular function in serotonergic and other neuronal cell types, which might help explain its association with antidepressant treatment response.

Changes to lipid metabolism are tightly associated with the onset and pathology of Alzheimer’s disease (AD). Lipids are complex molecules comprising many isomeric and isobaric species, necessitating detailed analysis to enable interpretation of biological significance. Our expanded targeted lipidomics platform (569 species across 32 classes) allows for detailed lipid separation and characterisation. In this study we examined peripheral samples of two cohorts (AIBL, n = 1112 and ADNI, n = 800). We are able to identify concordant peripheral signatures associated with prevalent AD arising from lipid pathways including; ether lipids, sphingolipids (notably GM3 gangliosides) and lipid classes previously associated with cardiometabolic disease (phosphatidylethanolamine and triglycerides). We subsequently identified similar lipid signatures in both cohorts with future disease. Lastly, we developed multivariate lipid models that improved classification and prediction. Our results provide a holistic view between the lipidome and AD using a comprehensive approach, providing targets for further mechanistic investigation., The onset and pathology of Alzheimer’s disease (AD) is associated with changes to lipid metabolism. Here, the authors analysed 569 lipids from 32 classes and subclasses in two independent patient cohorts to identify key lipid pathways to link the plasma lipidome with AD and the future onset of AD.

Mounting evidence suggests that the gut microbiome impacts brain development and function. Gut-brain connections may be mediated by an assortment of microbial molecules that are produced in the gastrointestinal tract, which can subsequently permeate many organs, including sometimes the brain. Studies in animal models have identified molecular cues propagated from intestinal bacteria to the brain that can affect neurological function and/or neurodevelopmental and neurodegenerative conditions. Herein, we describe bacterial metabolites with known or suspected neuromodulatory activity, define mechanisms of signalling pathways from the gut microbiota to the brain and discuss direct effects that gut bacterial molecules are likely exerting on specific brain cells. Many discoveries are recent, and the findings described in this Perspective are largely novel and yet to be extensively validated. However, expanding research into the dynamic molecular communications between gut microorganisms and the CNS continues to uncover critical and previously unappreciated clues in understanding the pathophysiology of behavioural, psychiatric and neurodegenerative diseases.

ObjectiveTo investigate the association of triglyceride (TG) principal component scores with Alzheimer disease (AD) and the amyloid, tau, neurodegeneration, and cerebrovascular disease (A/T/N/V) biomarkers for AD.MethodsSerum levels of 84 TG species were measured with untargeted lipid profiling of 689 participants from the Alzheimer's Disease Neuroimaging Initiative cohort, including 190 cognitively normal older adults (CN), 339 with mild cognitive impairment (MCI), and 160 with AD. Principal component analysis with factor rotation was used for dimension reduction of TG species. Differences in principal components between diagnostic groups and associations between principal components and AD biomarkers (including CSF, MRI and [18F]fluorodeoxyglucose-PET) were assessed with a generalized linear model approach. In both cases, the Bonferroni method of adjustment was used to correct for multiple comparisons.ResultsThe 84 TGs yielded 9 principal components, 2 of which, consisting of long-chain, polyunsaturated fatty acid–containing TGs (PUTGs), were significantly associated with MCI and AD. Lower levels of PUTGs were observed in MCI and AD compared to CN. PUTG principal component scores were also significantly associated with hippocampal volume and entorhinal cortical thickness. In participants carrying the APOE ε4 allele, these principal components were significantly associated with CSF β-amyloid1–42 values and entorhinal cortical thickness.ConclusionThis study shows that PUTG component scores were significantly associated with diagnostic group and AD biomarkers, a finding that was more pronounced in APOE ε4 carriers. Replication in independent larger studies and longitudinal follow-up are warranted.

BACKGROUND Basic research has implicated intracellular cholesterol in neurons, microglia, and astrocytes in the pathogenesis of Alzheimer's disease (AD), but there is presently no assay to access intracellular cholesterol in neural cells in living people in the context of AD. OBJECTIVE To devise and characterize an assay that can access intracellular cholesterol and cholesterol efflux in neural cells in living subjects. METHODS We modified the protocol for high-density lipoprotein cholesterol efflux capacity (CEC) from macrophages, a biomarker that accesses cholesterol in macrophages in atherosclerosis. To measure cerebrospinal fluid (CSF) CECs from neurons, microglia, and astrocytes, CSF was exposed to, correspondingly, neuronal, microglial, and astrocytic cholesterol source cells. Human neuroblastoma SH-SY5Y, mouse microglial N9, and human astroglial A172 cells were used as the cholesterol source cells. CSF samples were screened for contamination with blood. CSF CECs were measured in a small cohort of 22 individuals. RESULTS CSF CECs from neurons, microglia, and astrocytes were moderately to moderately strongly correlated with CSF concentrations of cholesterol, apolipoprotein A-I, apolipoprotein E, and clusterin (Pearson's r = 0.53-0.86), were in poor agreement with one another regarding CEC of the CSF samples (Lin's concordance coefficient rc = 0.71-0.76), and were best predicted by models consisting of, correspondingly, CSF phospholipid (R2 = 0.87, p

Background Food allergy (FA) affects an increasing proportion of children for reasons that remain obscure. Novel disease biomarkers and curative treatment options are strongly needed. Objective We sought to apply untargeted metabolomic profiling to identify pathogenic mechanisms and candidate disease biomarkers in patients with FA. Methods Mass spectrometry–based untargeted metabolomic profiling was performed on serum samples of children with either FA alone, asthma alone, or both FA and asthma, as well as healthy pediatric control subjects. Results In this pilot study patients with FA exhibited a disease-specific metabolomic signature compared with both control subjects and asthmatic patients. In particular, FA was uniquely associated with a marked decrease in sphingolipid levels, as well as levels of a number of other lipid metabolites, in the face of normal frequencies of circulating natural killer T cells. Specific comparison of patients with FA and asthmatic patients revealed differences in the microbiota-sensitive aromatic amino acid and secondary bile acid metabolism. Children with both FA and asthma exhibited a metabolomic profile that aligned with that of FA alone but not asthma. Among children with FA, the history of severe systemic reactions and the presence of multiple FAs were associated with changes in levels of tryptophan metabolites, eicosanoids, plasmalogens, and fatty acids. Conclusions Children with FA have a disease-specific metabolomic profile that is informative of disease mechanisms and severity and that dominates in the presence of asthma. Lower levels of sphingolipids and ceramides and other metabolomic alterations observed in children with FA might reflect the interplay between an altered microbiota and immune cell subsets in the gut.

Late-onset Alzheimer’s disease (AD) can, in part, be considered a metabolic disease. Besides age, female sex and APOE ε4 genotype represent strong risk factors for AD that also give rise to large metabolic differences. We systematically investigated group-specific metabolic alterations by conducting stratified association analyses of 139 serum metabolites in 1,517 individuals from the AD Neuroimaging Initiative with AD biomarkers. We observed substantial sex differences in effects of 15 metabolites with partially overlapping differences for APOE ε4 status groups. Several group-specific metabolic alterations were not observed in unstratified analyses using sex and APOE ε4 as covariates. Combined stratification revealed further subgroup-specific metabolic effects limited to APOE ε4+ females. The observed metabolic alterations suggest that females experience greater impairment of mitochondrial energy production than males. Dissecting metabolic heterogeneity in AD pathogenesis can therefore enable grading the biomedical relevance for specific pathways within specific subgroups, guiding the way to personalized medicine., Sex and the APOE ε4 genotype are important risk factors for late-onset Alzheimer’s disease. In the current study, the authors investigate how sex and APOE ε4 genotype modify the association between Alzheimer’s disease biomarkers and metabolites in serum.

The substantial progress made in the basic sciences of the brain has yet to be adequately translated to successful clinical therapeutics to treat central nervous system (CNS) diseases. Possible explanations include the lack of quantitative and validated biomarkers, the subjective nature of many clinical endpoints, and complex pharmacokinetic/pharmacodynamic relationships, but also the possibility that highly selective drugs in the CNS do not reflect the complex interactions of different brain circuits. Although computational systems pharmacology modeling designed to capture essential components of complex biological systems has been increasingly accepted in pharmaceutical research and development for oncology, inflammation, and metabolic disorders, the uptake in the CNS field has been very modest. In this article, a cross-disciplinary group with representatives from academia, pharma, regulatory, and funding agencies make the case that the identification and exploitation of CNS therapeutic targets for drug discovery and development can benefit greatly from a system and network approach that can span the gap between molecular pathways and the neuronal circuits that ultimately regulate brain activity and behavior. The National Institute of Neurological Disorders and Stroke (NINDS), in collaboration with the National Institute on Aging (NIA), National Institute of Mental Health (NIMH), National Institute on Drug Abuse (NIDA), and National Center for Advancing Translational Sciences (NCATS), convened a workshop to explore and evaluate the potential of a quantitative systems pharmacology (QSP) approach to CNS drug discovery and development. The objective of the workshop was to identify the challenges and opportunities of QSP as an approach to accelerate drug discovery and development in the field of CNS disorders. In particular, the workshop examined the potential for computational neuroscience to perform QSP-based interrogation of the mechanism of action for CNS diseases, along with a more accurate and comprehensive method for evaluating drug effects and optimizing the design of clinical trials. Following up on an earlier white paper on the use of QSP in general disease mechanism of action and drug discovery, this report focuses on new applications, opportunities, and the accompanying limitations of QSP as an approach to drug development in the CNS therapeutic area based on the discussions in the workshop with various stakeholders.

Late-onset Alzheimer's disease (LOAD) is a complex neurodegenerative disease characterized by multiple progressive stages, glucose metabolic dysregulation, Alzheimer's disease (AD) pathology, and inexorable cognitive decline. Discovery of metabolic profiles unique to sex, apolipoprotein E (APOE) genotype, and stage of disease progression could provide critical insights for personalized LOAD medicine.Sex- and APOE-specific metabolic networks were constructed based on changes in 127 metabolites of 656 serum samples from the Alzheimer's Disease Neuroimaging Initiative cohort.Application of an advanced analytical platform identified metabolic drivers and signatures clustered with sex and/or APOE ɛ4, establishing patient-specific biomarkers predictive of disease state that significantly associated with cognitive function. Presence of the APOE ɛ4 shifts metabolic signatures to a phosphatidylcholine-focused profile overriding sex-specific differences in serum metabolites of AD patients.These findings provide an initial but critical step in developing a diagnostic platform for personalized medicine by integrating metabolomic profiling and cognitive assessments to identify targeted precision therapeutics for AD patient subgroups through computational network modeling.

INTRODUCTIONAlzheimer’s disease (AD) is accompanied by metabolic alterations both in the periphery and the central nervous system. However, so far, a global view of AD-associated metabolic changes in brain has been missing.METHODSWe metabolically profiled 500 samples from the dorsolateral prefrontal cortex. Metabolite levels were correlated with eight clinical parameters, covering both late-life cognitive performance and AD neuropathology measures.RESULTSWe observed widespread metabolic dysregulation associated with AD, spanning 298 metabolites from various AD-relevant pathways. These included alterations to bioenergetics, cholesterol metabolism, neuroinflammation and metabolic consequences of neurotransmitter ratio imbalances. Our findings further suggest impaired osmoregulation as a potential pathomechanism in AD. Finally, inspecting the interplay of proteinopathies provided evidence that metabolic associations were largely driven by tau pathology rather than β-amyloid pathology.DISCUSSIONThis work provides a comprehensive reference map of metabolic brain changes in AD which lays the foundation for future mechanistic follow-up studies.

The apolipoprotein E (APOE) genotype is the strongest genetic risk factor for late-onset Alzheimer's disease. However, its effect on lipid metabolic pathways, and their mediating effect on disease risk, is poorly understood.We performed lipidomic analysis on three independent cohorts (the Australian Imaging, Biomarkers and Lifestyle [AIBL] flagship study, n = 1087; the Alzheimer's Disease Neuroimaging Initiative [ADNI] 1 study, n = 819; and the Busselton Health Study [BHS], n = 4384), and we defined associations between APOE ε2 and ε4 and 569 plasma/serum lipid species. Mediation analysis defined the proportion of the treatment effect of the APOE genotype mediated by plasma/serum lipid species.A total of 237 and 104 lipid species were associated with APOE ε2 and ε4, respectively. Of these 68 (ε2) and 24 (ε4) were associated with prevalent Alzheimer's disease. Individual lipid species or lipidomic models of APOE genotypes mediated up to 30% and 10% of APOE ε2 and ε4 treatment effect, respectively.Plasma lipid species mediate the treatment effect of APOE genotypes on Alzheimer's disease and as such represent a potential therapeutic target.

BackgroundAltered metabolism of acylcarnitines – transporting fatty acids to mitochondria – may link cellular energy dysfunction to depression. We examined the potential causal role of acylcarnitine metabolism in depression by leveraging genomics and Mendelian randomization.MethodsSummary statistics were obtained from large GWAS: the Fenland Study (N= 9,363), and the Psychiatric Genomics Consortium (246,363 depression cases and 561,190 controls). Two-sample Mendelian randomization analyses tested the potential causal link of 15 endogenous acylcarnitines with depression.ResultsIn univariable analyses, genetically-predicted lower levels of short-chain acylcarnitines C2 (Odds Ratio [OR] 0.97, 95% Confidence Intervals [CIs] 0.95-1.00) and C3 (OR 0.97, 95%CIs 0.96-0.99) and higher levels of medium-chain acylcarnitines C8 (OR 1.04, 95%CIs 1.01-1.06) and C10 (OR 1.04, 95%CIs 1.02-1.06) were associated with increased depression risk. No reverse potential causal role of depression genetic liability on acylcarnitines levels was found. Multivariable analyses showed that the association with depression was driven by the medium-chain acylcarnitines C8 (OR 1.04, 95%CIs 1.02-1.06) and C10 (OR 1.04, 95%CIs 1.02-1.06), suggesting a potential causal role in the risk of depression. Causal estimates for C8 (OR=1.05, 95%CIs=1.02-1.07) and C10 (OR=1.05, 95%CIs=1.02-1.08) were confirmed in follow-up analyses using genetic instruments derived from a GWAS meta-analysis including up to 16,841 samples.DiscussionAccumulation of medium-chain acylcarnitines is a signature of inborn errors of fatty acid metabolism and age-related metabolic conditions. Our findings point to a link between altered mitochondrial energy production and depression pathogenesis. Acylcarnitine metabolism represents a promising access point for the development of novel therapeutic approaches for depression.

Combination antidepressant pharmacotherapies are frequently used to treat major depressive disorder (MDD). However, there is no evidence that machine learning approaches combining multi-omics measures (e.g., genomics and plasma metabolomics) can achieve clinically meaningful predictions of outcomes to combination pharmacotherapy. This study examined data from 264 MDD outpatients treated with citalopram or escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) and 111 MDD outpatients treated with combination pharmacotherapies in the Combined Medications to Enhance Outcomes of Antidepressant Therapy (CO-MED) study to predict response to combination antidepressant therapies. To assess whether metabolomics with functionally validated single-nucleotide polymorphisms (SNPs) improves predictability over metabolomics alone, models were trained/tested with and without SNPs. Models trained with PGRN-AMPS’ and CO-MED’s escitalopram/citalopram patients predicted response in CO-MED’s combination pharmacotherapy patients with accuracies of 76.6% (p p p p

INTRODUCTIONEmbedding single-omics disease associations into the wider context of multi-level molecular changes in Alzheimer’s disease (AD) remains one central challenge in AD research.METHODSResults from numerous AD-specific omics studies from AMP-AD, NIAGADS, and other initiatives were integrated into a comprehensive network resource and complemented with molecular associations from large-scale population-based studies to provide a global view on AD.RESULTSWe present the AD Atlas, an online resource (www.adatlas.org) integrating over 20 large studies providing disease-relevant information on 20,353 protein-coding genes, 8,615 proteins, 997 metabolites and 31 AD-related phenotypes. Multiple showcases demonstrate the utility of this resource for contextualization of AD research results and subsequent downstream analyses, such as drug repositioning approaches.DISCUSSIONBy providing a global view on multi-omics results through a user-friendly interface, the AD Atlas enables the formulation of molecular hypotheses and retrieval of clinically relevant insights that can be validated in follow-up analyses or experiments.

We integrated lipidomics and genomics to unravel the genetic architecture of lipid metabolism and identify genetic variants associated with lipid species that are putatively in the mechanistic pathway to coronary artery disease (CAD). We quantified 596 lipid species in serum from 4,492 phenotyped individuals from the Busselton Health Study. In our discovery GWAS we identified 667 independent loci associations with these lipid species (479 novel), followed by meta-analysis and validation in two independent cohorts. Lipid endophenotypes (134) identified for CAD were associated with variation at 186 genomic loci. Associations between independent lipid-loci with coronary atherosclerosis were assessed in ∼456,000 individuals from the UK Biobank. Of the 53 lipid-loci that showed evidence of association (P−3), 43 loci were associated with at least one of the 134 lipid endophenotypes. The findings of this study illustrate the value of integrative biology to investigate the genetics and lipid metabolism in the aetiology of atherosclerosis and CAD, with implications for other complex diseases.

BackgroundMajor depressive disorder (MDD) is a highly heterogenous disease, both in terms of clinical profiles and pathobiological alterations. Recently, immunometabolic dysregulations were shown to be correlated with atypical, energy-related symptoms but less so with the Melancholic or Anxious distress symptom dimensions of depression in The Netherlands Study of Depression and Anxiety (NESDA) study. In this study, we aimed to replicate these immunometabolic associations and to characterize the metabolomic correlates of each of the three MDD dimensions.MethodsUsing three clinical rating scales, Melancholic, and Anxious distress, and Immunometabolic (IMD) dimensions were characterized in 158 patients who participated in the Predictors of Remission to Individual and Combined Treatments (PReDICT) study and from whom plasma and serum samples were available. The NESDA-defined inflammatory index, a composite measure of interleukin-6 and C-reactive protein, was measured from pre-treatment plasma samples and a metabolomic profile was defined using serum samples analyzed on three metabolomics platforms targeting fatty acids and complex lipids, amino acids, acylcarnitines, and gut microbiome-derived metabolites among other metabolites of central metabolism.ResultsThe IMD clinical dimension and the inflammatory index were positively correlated (r=0.19, p=.019) after controlling for age, sex, and body mass index, whereas the Melancholic and Anxious distress dimensions were not, replicating the previous NESDA findings. The three symptom dimensions had distinct metabolomic signatures using both univariate and set enrichment statistics. IMD severity correlated mainly with gut-derived metabolites and a few acylcarnitines and long chain saturated free fatty acids. Melancholia severity was significantly correlated with several phosphatidylcholines, primarily the ether-linked variety, lysophosphatidylcholines, as well as several amino acids. Anxious distress severity correlated with several medium and long chain free fatty acids, both saturated and polyunsaturated ones, sphingomyelins, as well as several amino acids and bile acids.ConclusionThe IMD dimension of depression is reliably associated with markers of inflammation. Metabolomics provides powerful tools to inform about depression heterogeneity and molecular mechanisms related to clinical dimensions in MDD, which include a link to gut microbiome and lipids implicated in membrane structure and function.

Dysregulation of sphingomyelin (SM) and ceramide metabolism have been implicated in Alzheimer’s Disease (AD). Genome-wide and transcriptome wide association studies have identified various genes and genetic variants in lipid metabolism that are associated with AD. However, the molecular mechanisms of sphingomyelin and ceramide disruption remain to be determined. Evaluation of peripheral lipidomic profiles is useful in providing perspective on metabolic dysregulation in preclinical and clinical AD states. In this study, we focused on the sphingolipid pathway and carried out multi-omic analyses to identify central and peripheral metabolic changes in AD patients and correlate them to imaging features and cognitive performance in amyloidogenic mouse models. Our multi-omic approach was based on (a) 2114 human post-mortem brain transcriptomics to identify differentially expressed genes; (b) in silico metabolic flux analysis on 1708 context-specific metabolic networks to identify differential reaction fluxes; (c) multimodal neuroimaging analysis on 1576 participants to associate genetic variants in SM pathway with AD pathogenesis; (d) plasma metabolomic and lipidomic analysis to identify associations of lipid species with dysregulation in AD; (e) metabolite genome-wide association studies (mGWAS) to define receptors within pathway as potential drug target. Our findings from complementary approaches suggested that depletion of S1P compensated for AD cellular pathology, likely by upregulating the SM pathway, suggesting that modulation of S1P signaling may have protective effects in AD. We tested this hypothesis in APP/PS1 mice and showed that prolonged exposure to fingolimod, an S1P signaling modulator approved for treatment of multiple sclerosis, alleviated the cognitive impairment in mice. Our multi-omic approach identified potential targets in the SM pathway and suggested modulators of S1P metabolism as possible candidates for AD treatment.

Human untargeted metabolomics studies succeed in annotating only ~10% of molecular features. We, therefore, introduce reference data-driven analysis that uses the source data as a pseudo-MS/MS reference library to match against human metabolomics MS/MS data. We demonstrate this approach with food source data, allowing an empirical assessment of dietary patterns from untargeted data but is broadly applicable and provides an additional layer of interpretability to metabolomics data.

Metabolomics provides valuable tools for the study of drug effects, unraveling the mechanism of action and variation in response due to treatment. In this study we used electrochemistry-based targeted metabolomics to gain insights into the mechanisms of action of escitalopram/citalopram focusing on a set of 31 metabolites from neurotransmitter-related pathways. Overall, 290 unipolar patients with major depressive disorder were profiled at baseline, after 4 and 8 weeks of drug treatment. The 17-item Hamilton Depression Rating Scale (HRSD17) scores gauged depressive symptom severity. More significant metabolic changes were found after 8 weeks than 4 weeks post baseline. Within the tryptophan pathway, we noted significant reductions in serotonin (5HT) and increases in indoles that are known to be influenced by human gut microbial cometabolism. 5HT, 5-hydroxyindoleacetate (5HIAA), and the ratio of 5HIAA/5HT showed significant correlations to temporal changes in HRSD17 scores. In the tyrosine pathway, changes were observed in the end products of the catecholamines, 3-methoxy-4-hydroxyphenylethyleneglycol and vinylmandelic acid. Furthermore, two phenolic acids, 4-hydroxyphenylacetic acid and 4-hydroxybenzoic acid, produced through noncanconical pathways, were increased with drug exposure. In the purine pathway, significant reductions in hypoxanthine and xanthine levels were observed. Examination of metabolite interactions through differential partial correlation networks revealed changes in guanosine–homogentisic acid and methionine–tyrosine interactions associated with HRSD17. Genetic association studies using the ratios of these interacting pairs of metabolites highlighted two genetic loci harboring genes previously linked to depression, neurotransmission, or neurodegeneration. Overall, exposure to escitalopram/citalopram results in shifts in metabolism through noncanonical pathways, which suggest possible roles for the gut microbiome, oxidative stress, and inflammation-related mechanisms.

Metabolomics and lipidomics play an important role in precision medicine. Global or targeted analyses of metabolites and lipids provide a specific metabolic phenotype, a “metabotype,” of an individual, which can provide important information on their propensity to develop certain diseases or their likely reaction to nutrition or pharmaceuticals. Metabolomics is increasingly being used in research to look at the etiology, prognosis, and outcomes of disease or provide more detailed information on normal human biochemistry. By combining metabolomics data sets with clinical, physiological, or other omics data sets, systems biology approaches can be used to provide context to the observed changes. For human populations in particular, the huge variation in genetics, lifestyle, and other extrinsic factors means that epidemiologic cohorts are often required to reach sufficient statistical power to draw meaningful conclusions. In addition, when disease entities are being studied, it can take years for enough representative samples to be collected. For these reasons, many metabolomics studies are reliant on samples collected and stored in either “official” biobanks (i.e., sample access available to other researchers on application) or privately stored sample repositories. Biobanks have grown and been increasingly professionalized in the past 20 to 30 years. Many biobanks were originally started for large-scale genetic analyses, and not all repositories have collected biofluids or tissues specifically with metabolomics analyses in mind. Measurements of individual metabolites can be highly dependent on collection and sample storage conditions. Thus, to reliably answer research questions, we need to have reliable collection and storage of samples in a form that makes them suitable for robust metabolomics analyses. To do otherwise invites the introduction of large technical variations in the data set and increases the risk of missing important metabolites that are contributing to the observed phenotypes or incorrectly attributing a metabolite change owing to technical error to …

In this pilot study (N = 9), we highlight new insights gained on ketamine's mechanism of action where we have mapped biochemical processes that are affected within 40 min of intravenous ketamine exposure. Targeting acylcarnitines, we demonstrated rapid utilization of short-chain acylcarnitines within 40 min of ketamine treatment followed by restoration within 24 h; this change in short chain acylcarnitine with rapid-acting antidepressant treatment is consistent with previous work identifying similar change but at 8-weeks with slower-acting SSRI treatment. Using a non-targeted metabolomics platform, we defined broader effects of ketamine on lipid metabolism and identified changes in triglyceride that correlate with ketamine response. This study provides novel insights into ketamine's action and highlighting a possible role for mitochondrial function and energy metabolism in ketamine's mechanism of action.

Background While Alzheimer disease (AD) and vascular dementia (VaD) may be accelerated by hypercholesterolemia, the mechanisms underlying this association are unclear. We tested whether dysregulation of cholesterol catabolism, through its conversion to primary bile acids (BAs), was associated with dementia pathogenesis. Methods and findings We used a 3-step study design to examine the role of the primary BAs, cholic acid (CA), and chenodeoxycholic acid (CDCA) as well as their principal biosynthetic precursor, 7α-hydroxycholesterol (7α-OHC), in dementia. In Step 1, we tested whether serum markers of cholesterol catabolism were associated with brain amyloid accumulation, white matter lesions (WMLs), and brain atrophy. In Step 2, we tested whether exposure to bile acid sequestrants (BAS) was associated with risk of dementia. In Step 3, we examined plausible mechanisms underlying these findings by testing whether brain levels of primary BAs and gene expression of their principal receptors are altered in AD. Step 1: We assayed serum concentrations CA, CDCA, and 7α-OHC and used linear regression and mixed effects models to test their associations with brain amyloid accumulation (N = 141), WMLs, and brain atrophy (N = 134) in the Baltimore Longitudinal Study of Aging (BLSA). The BLSA is an ongoing, community-based cohort study that began in 1958. Participants in the BLSA neuroimaging sample were approximately 46% male with a mean age of 76 years; longitudinal analyses included an average of 2.5 follow-up magnetic resonance imaging (MRI) visits. We used the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (N = 1,666) to validate longitudinal neuroimaging results in BLSA. ADNI is an ongoing, community-based cohort study that began in 2003. Participants were approximately 55% male with a mean age of 74 years; longitudinal analyses included an average of 5.2 follow-up MRI visits. Lower serum concentrations of 7α-OHC, CA, and CDCA were associated with higher brain amyloid deposition (p = 0.041), faster WML accumulation (p = 0.050), and faster brain atrophy mainly (false discovery rate [FDR] p = 4 million registrants from general practice clinics in the United Kingdom, we tested whether patients using BAS (BAS users; 3,208 with ≥2 prescriptions), which reduce circulating BAs and increase cholesterol catabolism, had altered dementia risk compared to those on non-statin lipid-modifying therapies (LMT users; 23,483 with ≥2 prescriptions). Patients in the study (BAS/LMT) were approximately 34%/38% male and with a mean age of 65/68 years; follow-up time was 4.7/5.7 years. We found that BAS use was not significantly associated with risk of all-cause dementia (hazard ratio (HR) = 1.03, 95% confidence interval (CI) = 0.72–1.46, p = 0.88) or its subtypes. We found a significant difference between the risk of VaD in males compared to females (p = 0.040) and a significant dose–response relationship between BAS use and risk of VaD (p-trend = 0.045) in males. Step 3: We assayed brain tissue concentrations of CA and CDCA comparing AD and control (CON) samples in the BLSA autopsy cohort (N = 29). Participants in the BLSA autopsy cohort (AD/CON) were approximately 50%/77% male with a mean age of 87/82 years. We analyzed single-cell RNA sequencing (scRNA-Seq) data to compare brain BA receptor gene expression between AD and CON samples from the Religious Orders Study and Memory and Aging Project (ROSMAP) cohort (N = 46). ROSMAP is an ongoing, community-based cohort study that began in 1994. Participants (AD/CON) were approximately 56%/36% male with a mean age of 85/85 years. In BLSA, we found that CA and CDCA were detectable in postmortem brain tissue samples and were marginally higher in AD samples compared to CON. In ROSMAP, we found sex-specific differences in altered neuronal gene expression of BA receptors in AD. Study limitations include the small sample sizes in the BLSA cohort and likely inaccuracies in the clinical diagnosis of dementia subtypes in primary care settings. Conclusions We combined targeted metabolomics in serum and amyloid positron emission tomography (PET) and MRI of the brain with pharmacoepidemiologic analysis to implicate dysregulation of cholesterol catabolism in dementia pathogenesis. We observed that lower serum BA concentration mainly in males is associated with neuroimaging markers of dementia, and pharmacological lowering of BA levels may be associated with higher risk of VaD in males. We hypothesize that dysregulation of BA signaling pathways in the brain may represent a plausible biologic mechanism underlying these results. Together, our observations suggest a novel mechanism relating abnormalities in cholesterol catabolism to risk of dementia., Vijay Varma and co-workers study cholesterol and bile acid metabolism in dementia., Author summary Why was this study done? Hypercholesterolemia is associated with increased risk of Alzheimer disease (AD) and vascular dementia (VaD). However, cholesterol is impermeable to the blood–brain barrier (BBB), and it is unclear how peripheral cholesterol mediates risk of dementia. While prior research has examined the relationship between de novo cholesterol biosynthesis and dementia, few studies have assessed the role of cholesterol catabolism and its principal breakdown products, oxysterols, and primary bile acids (BAs) in dementia. What did the researchers do and find? We examined the role of cholesterol catabolism in dementia pathogenesis by first testing the association between serum oxysterols and BAs and neuroimaging markers of dementia. We also tested whether exposure to bile acid sequestrants (BAS) was associated with risk of dementia in a large, real-world clinical dataset. Finally, we tested plausible mechanisms underlying these associations by examining whether primary BAs and mRNA of their receptors were altered in the brain in dementia. We found that lower serum levels of 7α-hydroxycholesterol (7α-OHC) and primary BAs were associated with higher brain amyloid deposition, faster WML accumulation, and faster brain atrophy mainly in males. Consistent with this finding, we observed a sex difference in the association between use of BAS and risk of VaD. We found that primary BAs were detectable in the brain, and levels of gene expression of BA receptors were altered in AD mainly in males. What do these findings mean? Our findings suggest that cholesterol catabolism and BA synthesis may impact dementia progression through sex-specific effects on signaling pathways in the brain. These results set the stage for experimental studies to test whether BA signaling in the brain may be a novel therapeutic target in dementia.

Background Alzheimer’s disease, cardiovascular disease, and other cardiometabolic disorders may share inflammatory origins. Lipid mediators, including oxylipins, endocannabinoids, bile acids, and steroids, regulate inflammation, energy metabolism, and cell proliferation with well-established involvement in cardiometabolic diseases. However, their role in Alzheimer’s disease is poorly understood. Here, we describe the analysis of plasma and cerebrospinal fluid lipid mediators in a case–control comparison of ~150 individuals with Alzheimer’s disease and ~135 healthy controls, to investigate this knowledge gap. Methods Lipid mediators were measured using targeted quantitative mass spectrometry. Data were analyzed using the analysis of covariates, adjusting for sex, age, and ethnicity. Partial least square discriminant analysis identified plasma and cerebrospinal fluid lipid mediator discriminates of Alzheimer’s disease. Alzheimer’s disease predictive models were constructed using machine learning combined with stepwise logistic regression. Results In both plasma and cerebrospinal fluid, individuals with Alzheimer’s disease had elevated cytochrome P450/soluble epoxide hydrolase pathway components and decreased fatty acid ethanolamides compared to healthy controls. Circulating metabolites of soluble epoxide hydrolase and ethanolamides provide Alzheimer’s disease predictors with areas under receiver operator characteristic curves ranging from 0.82 to 0.92 for cerebrospinal fluid and plasma metabolites, respectively. Conclusions Previous studies report Alzheimer’s disease-associated soluble epoxide hydrolase upregulation in the brain and that endocannabinoid metabolism provides an adaptive response to neuroinflammation. This study supports the involvement of P450-dependent and endocannabinoid metabolism in Alzheimer’s disease. The results further suggest that combined pharmacological intervention targeting both metabolic pathways may have therapeutic benefits for Alzheimer’s disease.

Selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for major depressive disorder (MDD), yet their mechanisms of action are not fully understood and their therapeutic benefit varies among individuals. We used a targeted metabolomics approach utilizing a panel of 180 metabolites to gain insights into mechanisms of action and response to citalopram/escitalopram. Plasma samples from 136 participants with MDD enrolled into the Mayo Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) were profiled at baseline and after 8 weeks of treatment. After treatment, we saw increased levels of short-chain acylcarnitines and decreased levels of medium-chain and long-chain acylcarnitines, suggesting an SSRI effect on β-oxidation and mitochondrial function. Amines—including arginine, proline, and methionine sulfoxide—were upregulated while serotonin and sarcosine were downregulated, suggesting an SSRI effect on urea cycle, one-carbon metabolism, and serotonin uptake. Eighteen lipids within the phosphatidylcholine (PC aa and ae) classes were upregulated. Changes in several lipid and amine levels correlated with changes in 17-item Hamilton Rating Scale for Depression scores (HRSD17). Differences in metabolic profiles at baseline and post-treatment were noted between participants who remitted (HRSD17 ≤ 7) and those who gained no meaningful benefits (17). Remitters exhibited (a) higher baseline levels of C3, C5, alpha-aminoadipic acid, sarcosine, and serotonin; and (b) higher week-8 levels of PC aa C34:1, PC aa C34:2, PC aa C36:2, and PC aa C36:4. These findings suggest that mitochondrial energetics—including acylcarnitine metabolism, transport, and its link to β-oxidation—and lipid membrane remodeling may play roles in SSRI treatment response.

INTRODUCTION: Analyses of cerebrospinal fluid (CSF) metabolites in large, healthy populations have been limited and potential demographic moderators of brain metabolism are largely unknown. OBJECTIVE: Our objective in this study was to examine sex and race differences in 33 CSF metabolites within a sample of 129 healthy individuals (37 African American women, 29 white women, 38 African American men, and 25 white men). METHODS: CSF metabolites were measured with a targeted electrochemistry-based metabolomics platform. Sex and race differences were quantified with both univariate and multivariate analyses. Type I error was controlled for by using a Bonferroni adjustment (.05/33 = .0015). RESULTS: Multivariate Canonical Variate Analysis (CVA) of the 33 metabolites showed correct classification of sex at an average rate of 80.6% and correct classification of race at an average rate of 88.4%. Univariate analyses revealed that men had significantly higher concentrations of cysteine (p

The inaugural “Microbiome for Mars” virtual workshop took place on July 13, 2020. This event assembled leaders in microbiome research and development to discuss their work and how it may relate to long-duration human space travel. The conference focused on surveying current microbiome research, future endeavors, and how this growing field could broadly impact human health and space exploration. This report summarizes each speaker’s presentation in the order presented at the workshop.

Metabolomics in the Alzheimer’s Disease Neuroimaging Initiative cohort provides a powerful tool for mapping biochemical changes in Alzheimer’s disease, and a unique opportunity to learn about the association between circulating blood metabolites and brain amyloid-β deposition in Alzheimer’s disease. We examined 140 serum metabolites and their associations with brain amyloid-β deposition, cognition and conversion from mild cognitive impairment to Alzheimer’s disease in the Alzheimer’s Disease Neuroimaging Initiative. Processed [18F] Florbetapir PET images were used to perform a voxel-wise statistical analysis of the effect of metabolite levels on amyloid-β accumulation across the whole brain. We performed a multivariable regression analysis using age, sex, body mass index, apolipoprotein E ε4 status and study phase as covariates. We identified nine metabolites as significantly associated with amyloid-β deposition after multiple comparison correction. Higher levels of one acylcarnitine (C3; propionylcarnitine) and one biogenic amine (kynurenine) were associated with decreased amyloid-β accumulation and higher memory scores. However, higher levels of seven phosphatidylcholines (lysoPC a C18:2, PC aa C42:0, PC ae C42:3, PC ae C44:3, PC ae C44:4, PC ae C44:5 and PC ae C44:6) were associated with increased brain amyloid-β deposition. In addition, higher levels of PC ae C44:4 were significantly associated with lower memory and executive function scores and conversion from mild cognitive impairment to Alzheimer’s disease dementia. Our findings suggest that dysregulation of peripheral phosphatidylcholine metabolism is associated with earlier pathological changes noted in Alzheimer’s disease as measured by brain amyloid-β deposition as well as later clinical features including changes in memory and executive functioning. Perturbations in phosphatidylcholine metabolism may point to issues with membrane restructuring leading to the accumulation of amyloid-β in the brain. Additional studies are needed to explore whether these metabolites play a causal role in the pathogenesis of Alzheimer’s disease or if they are biomarkers for systemic changes during preclinical phases of the disease., Nho et al. report that serum-based metabolites are associated with brain amyloid-β deposition, cognition and disease progression from mild cognitive impairment to Alzheimer’s disease in a large cohort of older adults. Results suggest that dysregulation of very long chain peripheral phospholipid metabolism is associated with earlier pathological changes in Alzheimer’s disease., Graphical Abstract Graphical Abstract

RATIONALEMetabolomics in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort provides a powerful tool for mapping biochemical changes in AD, and a unique opportunity to learn about the association between circulating blood metabolites and brain amyloid-β deposition in AD.OBJECTIVESWe examined 140 serum metabolites and their associations with brain amyloid-β deposition, cognition, and conversion from mild cognitive impairment (MCI) to AD.FINDINGSSerum-based targeted metabolite levels were measured in 1,531 ADNI participants. We performed association analysis of metabolites with brain amyloid-β deposition measured from [18F] Florbetapir PET scans. We identified nine metabolites as significantly associated with amyloid-β deposition after FDR-based multiple comparison correction. Higher levels of one acylcarnitine (C3; propionylcarnitine) and one biogenic amine (kynurenine) were associated with decreased amyloid-β accumulation. However, higher levels of seven phosphatidylcholines (PC) were associated with increased amyloid deposition. In addition, PC ae C44:4 was significantly associated with cognition and conversion from MCI to AD dementia.CONCLUSIONPerturbations in PC and acylcarnitine metabolism may play a role in features intrinsic to AD including amyloid-β deposition and cognitive performance.

Cognitive decline is associated with both normal aging and early pathologies leading to dementia. Here we used quantitative profiling of metabolites involved in the regulation of inflammation, vascular function, neuronal function and energy metabolism, including oxylipins, endocannabinoids, bile acids, and steroid hormones to identify metabolic biomarkers of mild cognitive impairment (MCI). Serum samples (n = 212) were obtained from subjects with or without MCI opportunistically collected with incomplete fasting state information. To maximize power and stratify the analysis of metabolite associations with MCI by the fasting state, we developed an algorithm to predict subject fasting state when unknown (n = 73). In non-fasted subjects, linoleic acid and palmitoleoyl ethanolamide levels were positively associated with perceptual speed. In fasted subjects, soluble epoxide hydrolase activity and tauro-alpha-muricholic acid levels were negatively associated with perceptual speed. Other cognitive domains showed associations with bile acid metabolism, but only in the non-fasted state. Importantly, this study shows unique associations between serum metabolites and cognitive function in the fasted and non-fasted states and provides a fasting state prediction algorithm based on measurable metabolites.

INTRODUCTION: Altered lipid metabolism is implicated in Alzheimer’s disease (AD), but the mechanisms remain obscure. Aging-related declines in circulating plasmalogens containing omega-3 fatty acids may increase AD risk by reducing plasmalogen availability. METHODS: We measured four ethanolamine plasmalogens (PlsEtns) and four closely related phosphatidylethanolamines (PtdEtns) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI; n = 1547 serum) and University of Pennsylvania (UPenn; n = 112 serum) cohorts, and derived indices reflecting PlsEtn and PtdEtn metabolism: PLPX (PlsEtns), PL/PE (PlsEtn/PtdEtn ratios), and PBV (plasmalogen biosynthesis value; a composite index). We tested associations with baseline diagnosis, cognition, and cerebrospinal fluid (CSF) AD biomarkers. RESULTS: Results revealed statistically significant negative relationships in ADNI between AD versus CN with PL-PX (P = 0.007) and PBV (P = 0.005), late mild cognitive impairment (LMCI) versus cognitively normal (CN) with PL-PX (P = 2.89 × 10(−5)) and PBV (P = 1.99 × 10(−4)), and AD versus LMCI with PL/PE (P = 1.85 × 10(−4)). In the UPenn cohort, AD versus CN diagnosis associated negatively with PL/PE (P = 0.0191) and PBV (P = 0.0296). In ADNI, cognition was negatively associated with plasmalogen indices, including Alzheimer’s Disease Assessment Scale 13-item cognitive subscale (ADAS-Cog13; PL-PX: P = 3.24 × 10(−6); PBV: P = 6.92 × 10(−5)) and Mini-Mental State Examination (MMSE; PL-PX: P = 1.28 × 10(−9); PBV: P = 6.50 × 10(−9)). In the UPenn cohort, there was a trend toward a similar relationship of MMSE with PL/PE (P = 0.0949). In ADNI, CSF total-tau was negatively associated with PL-PX (P = 5.55 × 10(−6)) and PBV (P = 7.77 × 10(−6)). Additionally, CSF t-tau/Aβ(1–42) ratio was negatively associated with these same indices (PL-PX, P = 2.73 × 10(−6); PBV, P = 4.39 × 10(−6)). In the UPenn cohort, PL/PE was negatively associated with CSF total-tau (P = 0.031) and t-tau/Aβ(1–42) (P = 0.021). CSF Aβ(1–42) was not significant associated with any of these indices in either cohort. DISCUSSION: These data extend previous studies by showing an association of decreased plasmalogen indices with AD, mild cognitive impairment (MCI), cognition, and CSF tau. Future studies are needed to better define mechanistic relationships, and to test the effects of interventions designed to replete serum plasmalogens.

Changes to lipid metabolism are tightly associated with the onset and pathology of Alzheimer’s disease (AD). Lipids are complex molecules comprising of many isomeric and isobaric species, necessitating detailed analysis to enable interpretation of biological significance. Our expanded targeted lipidomics platform (569 lipid species across 32 lipid (sub)classes) allows for detailed isomeric and isobaric lipid separation. We applied the methodology to examine plasma samples from the Australian Imaging, Biomarkers and Lifestyle flagship study of aging (AIBL, n = 1112) and serum from the Alzheimer’s Disease Neuroimaging Initiative (ADNI, n = 800) studies. Cross sectional analysis using both cohorts identified concordant unique peripheral signatures associated with AD. Specific pathways include; sphingolipids, including GM3gangliosides, where their acyl composition drove the major associations, and lipids previously associated with dysfunctional lipid metabolism in cardiometabolic disease including the phosphatidylethanolamine and triglyceride classes. Infomation derived from improved isomeric seperation highlighted pathway-specific changes with ether lipids including plasmalogens implicating perixosmal dysfunction in disease pathology. Longitudinal analysis revealed similar lipid signitures in both AIBL and ADNI cohorts with future disease onset. We utilised the two independent studies to train and validate multivariate lipid models that significantly improved disease classification and prediction. Together our results provide a holistic view of the lipidome and its relationship with AD using a comprehensive lipidomics approach, providing targets for further mechanistic investigation.

Pharmacometabolomics (PMx) studies use information contained in metabolic profiles (or metabolome) to inform about how a subject will respond to drug treatment. Genome, gut microbiome, sex, nutrition, age, stress, health status, and other factors can impact the metabolic profile of an individual. Some of these factors are known to influence the individual response to pharmaceutical compounds. An individual’s metabolic profile has been referred to as his or her “metabotype.” As such, metabolomic profiles obtained prior to, during, or after drug treatment could provide insights about drug mechanism of action and variation of response to treatment. Furthermore, there are several types of PMx studies that are used to discover and inform patterns associated with varied drug responses (i.e., responders vs. non-responders; slow or fast metabolizers). The PMx efforts could simultaneously provide information related to an individual’s pharmacokinetic response during clinical trials and be used to predict patient response to drugs making pharmacometabolomic clinical research valuable for precision medicine. PMx biomarkers can also be discovered and validated during FDA clinical trials. Using biomarkers during medical development is described in US Law under the 21st Century Cures Act. Information on how to submit biomarkers to the FDA and their context of use is defined herein.

Selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for major depressive disorder (MDD), yet their mechanisms of action are not fully understood and their therapeutic benefit varies among individuals. We used a targeted metabolomics approach utilizing a panel of 180 metabolites to gain insights into mechanisms of action and response to citalopram/escitalopram. Plasma samples from 136 participants with MDD enrolled into the Mayo Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) were profiled at baseline and after 8 weeks of treatment. After treatment, we saw increased levels of short-chain acylcarnitines and decreased levels of medium- and long-chain acylcarnitines, suggesting an SSRI effect on β-oxidation and mitochondrial function. Amines – including arginine, proline, and methionine-sulfoxide – were upregulated while serotonin and sarcosine were downregulated, suggesting an SSRI effect on urea cycle, one-carbon metabolism and serotonin uptake. Eighteen lipids within the phosphatidylcholine (PC aa and ae) classes were upregulated. Changes in several lipid and amine levels correlated with changes in 17-item Hamilton Rating Scale for Depression scores (HRSD17). Differences in metabolic profiles at baseline and post-treatment were noted between participants who remitted (HRSD17≤7) and those who gained no meaningful benefits (17). Remitters exhibited a) higher baseline levels of C3, C5, alpha-aminoadipic acid, sarcosine and serotonin; and b) higher week-8 levels of PC aa C34:1, PC aa C34:2, PC aa C36:2 and PC aa C36:4. These findings suggest that mitochondrial energetics – including acylcarnitine metabolism, transport and its link to β-oxidation – and lipid membrane remodeling may play roles in SSRI treatment response.

Large-scale genome wide association studies (GWASs) have been performed in search for risk genes for Alzheimer’s disease (AD). Despite the significant progress, replicability of genetic findings and their translation into targetable mechanisms related to the disease pathogenesis remains a challenge. Given that bile acids have been suggested in recent metabolic studies as potential age-related metabolic factors associated with AD, we integrated genomic and metabolomic data together with heterogeneous biological networks and investigated the potential cascade of effect of genetic variations to proteins, bile acids and ultimately AD brain phenotypes. Particularly, we leveraged functional protein interaction networks and metabolic networks and focused on the genes directly interacting with AD-altered bile acids and their functional regulators. We examined the association of all the SNPs located in those candidate genes with AD brain imaging phenotypes, and identified multiple AD risk SNPs whose downstream genes and bile acids were also found to be altered in AD. These AD related markers span from genetics to metabolomics, forming functional biological paths connecting across multiple-omics layers, and give valuable insights into the underlying mechanism of AD.

Alzheimer’s disease (AD) is the leading cause of dementia, with metabolic dysfunction seen years before the emergence of clinical symptoms. Increasing evidence suggests a role for primary and secondary bile acids, the end-product of cholesterol metabolism, influencing pathophysiology in AD. In this study, we analyzed transcriptomes from 2114 post-mortem brain samples from three independent cohorts and identified that the genes involved in the alternative bile acid synthesis pathway were expressed in the brain compared to the classical pathway. These results were supported by targeted metabolomic analysis of primary and secondary bile acids measured from post-mortem brain samples of 111 individuals. We reconstructed brain region-specific metabolic networks using data from three independent cohorts to assess the role of bile acid metabolism in AD pathophysiology. Our metabolic network analysis suggested that taurine transport, bile acid synthesis and cholesterol metabolism differed in AD and cognitively normal individuals. Using the brain transcriptional regulatory network, we identified putative transcription factors regulating these metabolic genes and influencing altered metabolism in AD. Intriguingly, we find bile acids from the brain metabolomics whose synthesis cannot be explained by enzymes we find in the brain, suggesting they may originate from an external source such as the gut microbiome. These findings motivate further research into bile acid metabolism and transport in AD to elucidate their possible connection to cognitive decline.

The incidence of Alzheimer's disease (AD) increases with age and is becoming a significant cause of worldwide morbidity and mortality. However, the metabolic perturbation behind the onset of AD remains unclear. In this study, we performed metabolite profiling in both brain (n = 109) and matching serum samples (n = 566) to identify differentially expressed metabolites and metabolic pathways associated with neuropathology and cognitive performance and to identify individuals at high risk of developing cognitive impairment. The abundances of 6 metabolites, glycolithocholate (GLCA), petroselinic acid, linoleic acid, myristic acid, palmitic acid, palmitoleic acid and the deoxycholate/cholate (DCA/CA) ratio, along with the dysregulation scores of 3 metabolic pathways, primary bile acid biosynthesis, fatty acid biosynthesis, and biosynthesis of unsaturated fatty acids showed significant differences across both brain and serum diagnostic groups (P-value

Evidence has shown that the kynurenine pathway (KP) plays a role in the onset of oxidative stress and also in the pathophysiology of schizophrenia. The aim of this study was to use a pharmacological animal model of schizophrenia induced by ketamine to investigate if KP inhibitors could protect the brains of Wistar rats against oxidative stress and behavioral changes. Ketamine, injected at the dose of 25mg/kg, increased spontaneous locomotor activity. However, the inhibitors of tryptophan 2,3-dioxygenase (TDO), indoleamine 2,3-dioxygenase (IDO) and kynurenine-3-monooxygenase (KMO) were able to reverse these changes. In addition, the IDO inhibitor prevented lipid peroxidation, and decreased the levels of protein carbonyl in the prefrontal cortex (PFC), hippocampus and striatum. It also increased the activity of superoxide dismutase (SOD) in the hippocampus, as well as increasing the levels of catalase activity in the PFC and hippocampus. The TDO inhibitor prevented lipid damage in the striatum and reduced the levels of protein carbonyl in the hippocampus and striatum. Also, the TDO inhibitor increased the levels of SOD activity in the striatum and CAT activity in the hippocampus of ketamine-induced pro-oxidant effects. Lipid damage was not reversed by the KMO inhibitor. The KMO inhibitor increased the levels of SOD activity in the hippocampus, and reduced the levels of protein carbonyl while elevating the levels of CAT activity in the striatum of rats that had been injected with ketamine. Our findings revealed that the KP pathway could be a potential mechanism by which a schizophrenia animal model induced by ketamine could cause interference by producing behavioral disturbance and inducing oxidative stress in the brain, suggesting that the inhibition of the KP pathway could be a potential target in treating schizophrenia.