Kaddurah‐Daouk, Rima

AbstractBackgroundBrain glucose hypometabolism is among the earliest pathogenic changes in Alzheimer’s disease (AD). This metabolic dysfunction points to the personal bioenergetic capacity, defined as the ability to maintain energy homeostasis under all circumstances including deregulated glucose uptake, as a potential source of resilience to the disease. Fasting blood acylcarnitine profiles are a central readout for this capacity in the absence of dietary glucose and capture the activity and efficiency of glucose‐independent routes of mitochondrial energy metabolism.MethodWe used fasting serum acylcarnitine profiles of 1,531 participants (465 with normal cognition, 762 with mild cognitive impairment, and 304 with clinical AD) from the AD Neuroimaging Initiative to perform unsupervised subgroup identification using hierarchical clustering. Identified subgroups were investigated for differences in A/T/N biomarker profiles and cognitive status. The contributions of genetic and potentially modifiable factors defining the subgroups were quantified using analysis of explained variance. The influence of the strongest determining factors on longitudinal cognitive trajectories was estimated using linear mixed‐effects models and gene‐by‐environment interaction analysis.ResultWe found several bioenergetically distinct subgroups with significant differences in AD biomarker profiles and cognitive function. The strongest genetic contribution to this bioenergetic endophenotype seems to be specifically linked to succinylcarnitine metabolism and significantly modulates the rate of future cognitive decline. In contrast, potentially modifiable sustainment of beta‐oxidation efficiency seems to decelerate bioenergetic aging, thus creating a bioenergetic reserve that delays progression of cognitive decline. Using gene‐by‐environment interaction analysis, we demonstrate that this molecular framework identifies a subgroup of individuals that is likely to benefit significantly from personalized therapeutic, dietary or lifestyle interventions tailored to increase resilience against bioenergetic disturbances in AD.ConclusionOur study reports on a set of genetic and metabolic markers that define bioenergetically distinct subgroups with significant differences on the AD biomarker and cognitive level. Longitudinal data suggest that targeting the modifiable fraction of this endophenotype might be a promising strategy to slow down disease progression in individuals with specific allelic configurations.

Abstract INTRODUCTION The ketogenic diet (KD) is an intriguing therapeutic candidate for Alzheimer's disease (AD) given its protective effects against metabolic dysregulation and seizures. Gut microbiota are essential for KD‐mediated neuroprotection against seizures as well as modulation of bile acids, which play a major role in cholesterol metabolism. These relationships motivated our analysis of gut microbiota and metabolites related to cognitive status following a controlled KD intervention compared with a low‐fat‐diet intervention. METHODS Prediabetic adults, either with mild cognitive impairment (MCI) or cognitively normal (CN), were placed on either a low‐fat American Heart Association diet or high‐fat modified Mediterranean KD (MMKD) for 6 weeks; then, after a 6‐week washout period, they crossed over to the alternate diet. We collected stool samples for shotgun metagenomics and untargeted metabolomics at five time points to investigate individuals’ microbiome and metabolome throughout the dietary interventions. RESULTS Participants with MCI on the MMKD had lower levels of GABA‐producing microbes Alistipes sp. CAG:514 and GABA, and higher levels of GABA‐regulating microbes Akkermansia muciniphila . MCI individuals with curcumin in their diet had lower levels of bile salt hydrolase‐containing microbes and an altered bile acid pool, suggesting reduced gut motility. DISCUSSION Our results suggest that the MMKD may benefit adults with MCI through modulation of GABA levels and gut‐transit time.

AbstractBackgroundMetabolic dysregulation is one of the hallmarks of Alzheimer’s disease (AD). Alterations in metabolism have been associated with AD‐related comorbidities, cognitive decline, and AD brain pathology. Brain regions are differentially affected with AD neuropathology and exhibit metabolic heterogeneity. Therefore, to build a more comprehensive picture of metabolic impact of AD, region‐specific AD‐associated metabolic changes need to be determined.MethodWe profiled 344 brain samples from the Mayo Clinic brain bank using Metabolon’s untargeted metabolomics platform, covering 182 samples from the cerebellum (CER) and 162 samples from the temporal cortex (TCX). Generalized linear models were used to assess the association of metabolic profiles with AD. Models included confounder correction for age at death, sex, and number of APOE‐ε4 alleles. To extend this study to four more brain regions, we included results from previously published brain metabolomics studies based on samples from three independent cohorts, including dorsolateral prefrontal cortex (DLPFC) from ROS/MAP, parietal lobe (PTL) from Washington University School of Medicine (WUSM), inferior temporal gyrus (ITG) and middle frontal gyrus (MFG) from Baltimore Longitudinal Study of Aging (BLSA).ResultIn the Mayo cohort, a total of 190 out of the 658 metabolites were significantly associated with AD. Out of the 190, 127 metabolites associated only in the CER region, 37 metabolites associated only in the TCX region, and 26 metabolites were associated in both brain regions. There were no metabolites commonly regulated in all the 6 brain regions under investigation. DLPFC and CER had the highest overlap of 70 metabolites, followed by DLPFC and PTL with 64 metabolites, and CER and PTL with 47 metabolites. For functional interpretation of our findings, each metabolite was further categorized with a sub‐pathway annotation provided by Metabolon. 3 of the 88 pathways including methionine cycle, phospholipid metabolism, and glutathione metabolism, were commonly regulated in the six brain regions under investigation.ConclusionWe present a first global perspective on AD‐associated metabolic alterations across six brain regions. This study will pave the way to fill the gap in our understanding of region‐specific metabolic deregulations associated with AD pathophysiology.

Abstract Introduction Alzheimer'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 the brain has been missing. Methods We 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. Results We 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 amyloid beta pathology. Discussion This work provides a comprehensive reference map of metabolic brain changes in AD that lays the foundation for future mechanistic follow‐up studies.

Abstract Introduction 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. Methods 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. Results 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. Discussion 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.

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 (<age 18) and adult-onset depression. The most significant variant (p = 8.77 × 10−8) localized to an intron of SAMD3. In silico functional annotation of top signals (p < 1 × 10−5) demonstrated gene expression enrichment in the brain and during embryonic development. Network analysis identified differential associations between four variants (in/near INTU, FAT1, CNTN6, and TM9SF2) and plasma metabolites (phosphatidylcholines, carnitines, biogenic amines, and amino acids) in early- compared with adult-onset MDD. Multi-omics integration identified differential biosignatures of early- and adult-onset MDD. These biosignatures call for future studies to follow participants from childhood through adulthood and collect repeated -omics and neuroimaging measures to validate and deeply characterize the biomarkers of susceptibility and/or resistance to MDD development.

Introduction: β-blockers, including atenolol, are among the most widely prescribed antihypertensive drug classes. However, their use has been associated with hyperglycemia and incident diabetes, which poses concern for prescribers and patients. The Pharmacogenomic Evaluation of Antihypertensive Responses (PEAR) study is a multicenter clinical trial with one of its objectives to identify genetic influences on adverse metabolic events in patients treated with atenolol. Here we report a genomics-metabolomics integrative approach to identify biomarkers associated with atenolol-induced glucose change. Methods: PEAR participants were genotyped using the Illumina Omni 1M-Quad Chip. Metabolite profiles were acquired through GC-TOF-MS. Association between baseline levels of metabolites and glucose change was conducted using a multivariate linear regression model adjusted for age, gender and baseline fasting glucose. Genomics-metabolomics integration included selecting SNPs within genes involved in the synthesis and degradation of top metabolites and testing their effect on glucose change in 234 atenolol-treated Caucasians in PEAR, adjusting for age, gender, baseline glucose and principal components 1 and 2. A false discovery rate (FDR) of .05 was used to adjust for multiple comparisons. Replication of significant SNPs was conducted in an additional 228 participants treated with atenolol in PEAR. Results: Metabolomics analysis revealed 3 metabolites associated with glucose change that passed FDR adjustment. The top metabolite signal was beta-alanine. Increasing levels of baseline beta-alanine was significantly correlated with greater glucose increases after atenolol treatment (p=.0003). A total of 79 SNPs in 12 genes in the beta-alanine pathway were explored. SNP rs2669429 in DPYS was significantly associated with glucose change, after FDR adjustment. We found that G allele carriers had significantly greater glucose change compared with non-carriers (p=.0006; β=+2.76 mg/dL). This finding was successfully replicated in the other 228 patients who received atenolol (p=.04; β=+1.86 mg/dL). Conclusions: These results suggest that beta alanine and DPYS rs2669429 SNP might be an important predictor of atenolol-induced dysglycemia.

Major depressive disorder (MDD), the number one psychiatric disease and a major cause of medical disability worldwide, is a heterogeneous disease. Efforts to identify biomarkers for antidepressant therapy response by the use of genome‐wide association studies (GWAS) have generally yielded disappointing results, e.g. only a few markers have been replicated across studies. By applying a “pharmacometabolomics‐informed pharmacogenomic” research strategy, we previously identified a genome‐wide significant single‐nucleotide polymorphism (SNP) signal in the glutamate‐rich 3 ( ERICH3 ) gene that was associated with plasma serotonin concentrations which were themselves associated with selective serotonin reuptake inhibitor (SSRI) response in MDD patients enrolled in the Mayo Clinic PGRN‐AMPS SSRI trial. The same ERICH3 SNPs were associated with SSRI response in other GWAS, including the STAR*D and ISPC studies. However, the function of ERICH3 and the molecular mechanism(s) by which it might be associated with plasma serotonin concentrations and with SSRI response remain unknown. Functional characterization of ERICH3 and its encoded protein might make it possible to identify novel mechanisms of antidepressant treatment response. In the present study, ERICH3 mRNA and protein were studied in cell lines from human peripheral blood and brain. ERICH3 cDNA constructs and specific antibodies were generated to make it possible to study its function. Two ERICH3 mRNA splice variants and protein isoforms, one “full‐length” and the other encoding a C‐terminal protein, were identified in a variety of human cell lines and in human brain tissue. ERICH3 mRNA expression and immunofluorescent staining suggested both cytosolic and vesicular localizations for ERICH3 as well as co‐localization with serotonin and dopamine in both mouse and human neurons. Co‐immunoprecipitation studies identified several ERICH3 interacting proteins, including PIK3C2A, CLTC and AP2A2, proteins known to play a role in vesicular trafficking, as well as ALK and CUX1, both of which display genetic polymorphisms that have been associated with SSRI response in GWAS studies of antidepressant treatment response in MDD patients. This series of experiments suggests that ERICH3 may play a role in vesicular trafficking and, as a result, may influence neurotransmitter actions in the brain that are associated with antidepressant treatment response. Support or Funding Information This work was supported by the NIH grants U19 GM61388, R01 GM28157 and T32 GM008685. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Background Although hydrochlorothiazide ( HCTZ ) is a well‐established first‐line antihypertensive in the United States, <50% of HCTZ treated patients achieve blood pressure ( BP ) control. Thus, identifying biomarkers that could predict the BP response to HCTZ is critically important. In this study, we utilized metabolomics, genomics, and lipidomics to identify novel pathways and biomarkers associated with HCTZ BP response. Methods and Results First, we conducted a pathway analysis for 13 metabolites we recently identified to be significantly associated with HCTZ BP response. From this analysis, we found the sphingolipid metabolic pathway as the most significant pathway ( P =5.8E‐05). Testing 78 variants, within 14 genes involved in the sphingolipid metabolic canonical pathway, with the BP response to HCTZ identified variant rs6078905, within the SPTLC 3 gene, as a novel biomarker significantly associated with the BP response to HCTZ in whites (n=228). We found that rs6078905 C‐allele carriers had a better BP response to HCTZ versus noncarriers (∆ SBP /∆ DBP : −11.4/−6.9 versus −6.8/−3.5 mm Hg; ∆ SBP P =6.7E‐04; ∆ DBP P =4.8E‐04). Additionally, in blacks (n=148), we found genetic signals in the SPTLC 3 genomic region significantly associated with the BP response to HCTZ ( P <0.05). Last, we observed that rs6078905 significantly affects the baseline level of 4 sphingomyelins (N24:2, N24:3, N16:1, and N22:1; false discovery rate <0.05), from which N24:2 sphingomyelin has a significant correlation with both HCTZ DBP ‐response ( r =−0.42; P =7E‐03) and SBP ‐response ( r =−0.36; P =2E‐02). Conclusions This study provides insight into potential pharmacometabolomic and genetic mechanisms underlying HCTZ BP response and suggests that SPTLC 3 is a potential determinant of the BP response to HCTZ . Clinical Trial Registration URL : http://www.clinicaltrials.gov . Unique identifier: NCT 00246519.

The scaling up of data in clinical pharmacology and the merger of systems biology and pharmacology has led to the emergence of a new discipline of Quantitative and Systems Pharmacology (QSP). This new research direction might significantly advance the discovery, development, and clinical use of therapeutic drugs. Research communities from computational biology, systems biology, and biological engineering—working collaboratively with pharmacologists, geneticists, biochemists, and analytical chemists—are creating and modeling large data on drug effects that is transforming our understanding of how these drugs work at a network level. In this review, we highlight developments in a new and rapidly growing field—pharmacometabolomics—in which large biochemical data‐capturing effects of genome, gut microbiome, and environment exposures is revealing information about metabotypes and treatment outcomes, and creating metabolic signatures as new potential biomarkers. Pharmacometabolomics informs and complements pharmacogenomics and together they provide building blocks for QSP.