Lipidomics Reveals Early Metabolic Changes in Subjects with Schizophrenia: Effects of Atypical Antipsychotics

There is a critical need for mapping early metabolic changes in schizophrenia to capture failures in regulation of biochemical pathways and networks. This information could provide valuable insights about disease mechanisms, trajectory of disease progression, and diagnostic biomarkers. We used a lipidomics platform to measure individual lipid species in 20 drug-naïve patients with a first episode of schizophrenia (FE group), 20 patients with chronic schizophrenia that had not adhered to prescribed medications (RE group), and 29 race-matched control subjects without schizophrenia. Lipid metabolic profiles were evaluated and compared between study groups and within groups before and after treatment with atypical antipsychotics, risperidone and aripiprazole. Finally, we mapped lipid profiles to n3 and n6 fatty acid synthesis pathways to elucidate which enzymes might be affected by disease and treatment. Compared to controls, the FE group showed significant down-regulation of several n3 polyunsaturated fatty acids (PUFAs), including 20:5n3, 22:5n3, and 22:6n3 within the phosphatidylcholine and phosphatidylethanolamine lipid classes. Differences between FE and controls were only observed in the n3 class PUFAs; no differences where noted in n6 class PUFAs. The RE group was not significantly different from controls, although some compositional differences within PUFAs were noted. Drug treatment was able to correct the aberrant PUFA levels noted in FE patients, but changes in re patients were not corrective. Treatment caused increases in both n3 and n6 class lipids. These results supported the hypothesis that phospholipid n3 fatty acid deficits are present early in the course of schizophrenia and tend not to persist throughout its course. These changes in lipid metabolism could indicate a metabolic vulnerability in patients with schizophrenia that occurs early in development of the disease. © 2013 McEvoy et al

Generation and quality control of lipidomics data for the alzheimers disease neuroimaging initiative cohort.

Alzheimers disease (AD) is a major public health priority with a large socioeconomic burden and complex etiology. The Alzheimer Disease Metabolomics Consortium (ADMC) and the Alzheimer Disease Neuroimaging Initiative (ADNI) aim to gain new biological insights in the disease etiology. We report here an untargeted lipidomics of serum specimens of 806 subjects within the ADNI1 cohort (188 AD, 392 mild cognitive impairment and 226 cognitively normal subjects) along with 83 quality control samples. Lipids were detected and measured using an ultra-high-performance liquid chromatography quadruple/time-of-flight mass spectrometry (UHPLC-QTOF MS) instrument operated in both negative and positive electrospray ionization modes. The dataset includes a total 513 unique lipid species out of which 341 are known lipids. For over 95% of the detected lipids, a relative standard deviation of better than 20% was achieved in the quality control samples, indicating high technical reproducibility. Association modeling of this dataset and available clinical, metabolomics and drug-use data will provide novel insights into the AD etiology. These datasets are available at the ADNI repository at http://adni.loni.usc.edu/

A critical evaluation of methods for the reconstruction of tissue-specific models

Under the framework of constraint based modeling, genome-scale metabolic models (GSMMs) have been used for several tasks, such as metabolic engineering and phenotype prediction. More recently, their application in health related research has spanned drug discovery, biomarker identification and host-pathogen interactions, targeting diseases such as cancer, Alzheimer, obesity or diabetes. In the last years, the development of novel techniques for genome sequencing and other high-throughput methods, together with advances in Bioinformatics, allowed the reconstruction of GSMMs for human cells. Considering the diversity of cell types and tissues present in the human body, it is imperative to develop tissue-specific metabolic models. Methods to automatically generate these models, based on generic human metabolic models and a plethora of omics data, have been proposed. However, their results have not yet been adequately and critically evaluated and compared. This work presents a survey of the most important tissue or cell type specific metabolic model reconstruction methods, which use literature, transcriptomics, proteomics and metabolomics data, together with a global template model. As a case study, we analyzed the consistency between several omics data sources and reconstructed distinct metabolic models of hepatocytes using different methods and data sources as inputs. The results show that omics data sources have a poor overlapping and, in some cases, are even contradictory. Additionally, the hepatocyte metabolic models generated are in many cases not able to perform metabolic functions known to be present in the liver tissue. We conclude that reliable methods for a priori omics data integration are required to support the reconstruction of complex models of human cells.Acknowledgments. S.C. thanks the FCT for the Ph.D. Grant SFRH/BD/ 80925/2011. The authors thank the FCT Strategic Project of UID/BIO/04469/2013 unit, the project RECI/BBB-EBI/0179/2012 (FCOMP-01-0124-FEDER-027462) and the project “BioInd - Biotechnology and Bioengineering for improved Industrial and Agro-Food processes”, REF. NORTE-07-0124-FEDER-000028 Co-funded by the Programa Operacional Regional do Norte (ON.2 - O Novo Norte), QREN, FEDER

Using Cognitive Interviewing for the Semantic Enhancement of Multi-Lingual Versions of Personality Questionnaires

We discuss the use of cognitive interviewing with bilinguals as an integral part of cross-cultural adaptation of personality questionnaires. The aim is to maximize semantic equivalence to increase the likelihood of items maintaining the intended structure and meaning in the target language. We refer to this part of adaptation as semantic enhancement, and integrate cognitive interviewing within it as a tool for scrutinizing translations, the connotative meaning, and the psychological impact of items across languages. During the adaptation of a work-based personality questionnaire from English to Arabic, Chinese (Mandarin), and Spanish, we cognitively interviewed 12 bilingual participants about 136 items in different languages (17% of all items), of which 67 were changed. A content analysis categorizing the reasons for amending items elicited eleven errors that affect two identified forms of semantic equivalence. We provide the resultant coding scheme as a framework for designing cognitive interviewing protocols and propose a procedure for implementing them. We discuss implications for theory and practic

Metabolic Network Analysis Reveals Altered Bile Acid Synthesis and Metabolism in Alzheimer\u27s Disease.

Increasing evidence suggests Alzheimer\u27s disease (AD) pathophysiology is influenced by primary and secondary bile acids, the end product of cholesterol metabolism. We analyze 2,114 post-mortem brain transcriptomes and identify genes in the alternative bile acid synthesis pathway to be expressed in the brain. A targeted metabolomic analysis of primary and secondary bile acids measured from post-mortem brain samples of 111 individuals supports these results. Our metabolic network analysis suggests that taurine transport, bile acid synthesis, and cholesterol metabolism differ in AD and cognitively normal individuals. We also identify putative transcription factors regulating metabolic genes and influencing altered metabolism in AD. Intriguingly, some bile acids measured in brain tissue cannot be explained by the presence of enzymes responsible for their synthesis, suggesting that they may originate from the gut microbiome and are transported to the brain. These findings motivate further research into bile acid metabolism in AD to elucidate their possible connection to cognitive decline

Lipidomic analysis of variation in response to simvastatin in the Cholesterol and Pharmacogenetics Study

Statins are commonly used for reducing cardiovascular disease risk but therapeutic benefit and reductions in levels of low-density lipoprotein cholesterol (LDL-C) vary among individuals. Other effects, including reductions in C-reactive protein (CRP), also contribute to treatment response. Metabolomics provides powerful tools to map pathways implicated in variation in response to statin treatment. This could lead to mechanistic hypotheses that provide insight into the underlying basis for individual variation in drug response. Using a targeted lipidomics platform, we defined lipid changes in blood samples from the upper and lower tails of the LDL-C response distribution in the Cholesterol and Pharmacogenetics study. Metabolic changes in responders are more comprehensive than those seen in non-responders. Baseline cholesterol ester and phospholipid metabolites correlated with LDL-C response to treatment. CRP response to therapy correlated with baseline plasmalogens, lipids involved in inflammation. There was no overlap of lipids whose changes correlated with LDL-C or CRP responses to simvastatin suggesting that distinct metabolic pathways govern statin effects on these two biomarkers. Metabolic signatures could provide insights about variability in response and mechanisms of action of statins

Diabetic ketoacidosis and hyperglycemic hyperosmolar syndrome after renal transplantation in the United States

BACKGROUND: The incidence and risk factors for diabetic ketoacidosis (diabetic ketoacidosis) and hyperglycemic hyperosmolar syndrome (hyperglycemic hyperosmolar syndrome, previously called non-ketotic hyperosmolar coma) have not been reported in a national population of renal transplant (renal transplantation) recipients. METHODS: We performed a historical cohort study of 39,628 renal transplantation recipients in the United States Renal Data System between 1 July 1994 and 30 June 1998, followed until 31 Dec 1999. Outcomes were hospitalizations for a primary diagnosis of diabetic ketoacidosis (ICD-9 code 250.1x) and hyperglycemic hyperosmolar syndrome (code 250.2x). Cox Regression analysis was used to calculate adjusted hazard ratios for time to hospitalization for diabetic ketoacidosis or hyperglycemic hyperosmolar syndrome. RESULTS: The incidence of diabetic ketoacidosis and hyperglycemic hyperosmolar syndrome were 33.2/1000 person years (PY) and 2.7/1000 PY respectively for recipients with a prior diagnosis of diabetes mellitus (DM), and 2.0/1000 PY and 1.1/1000 PY in patients without DM. In Cox Regression analysis, African Americans (AHR, 2.71, 95 %CI, 1.96–3.75), females, recipients of cadaver kidneys, patients age 33–44 (vs. >55), more recent year of transplant, and patients with maintenance TAC (tacrolimus, vs. cyclosporine) had significantly higher risk of diabetic ketoacidosis. However, the rate of diabetic ketoacidosis decreased more over time in TAC users than overall. Risk factors for hyperglycemic hyperosmolar syndrome were similar except for the significance of positive recipient hepatitis C serology and non-significance of female gender. Both diabetic ketoacidosis (AHR, 2.44, 95% CI, 2.10–2.85, p < 0.0001) and hyperglycemic hyperosmolar syndrome (AHR 1.87, 95% CI, 1.22–2.88, p = 0.004) were independently associated with increased mortality. CONCLUSIONS: We conclude that diabetic ketoacidosis and hyperglycemic hyperosmolar syndrome were associated with increased risk of mortality and were not uncommon after renal transplantation. High-risk groups were identified

Concordant peripheral lipidome signatures in two large clinical studies of Alzheimer’s disease

© 2020, The Author(s). 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

Metabolomic analyses of Leishmania reveal multiple species differences and large differences in amino acid metabolism

Comparative genomic analyses of Leishmania species have revealed relatively minor heterogeneity amongst recognised housekeeping genes and yet the species cause distinct infections and pathogenesis in their mammalian hosts. To gain greater information on the biochemical variation between species, and insights into possible metabolic mechanisms underpinning visceral and cutaneous leishmaniasis, we have undertaken in this study a comparative analysis of the metabolomes of promastigotes of L. donovani, L. major and L. mexicana. The analysis revealed 64 metabolites with confirmed identity differing 3-fold or more between the cell extracts of species, with 161 putatively identified metabolites differing similarly. Analysis of the media from cultures revealed an at least 3-fold difference in use or excretion of 43 metabolites of confirmed identity and 87 putatively identified metabolites that differed to a similar extent. Strikingly large differences were detected in their extent of amino acid use and metabolism, especially for tryptophan, aspartate, arginine and proline. Major pathways of tryptophan and arginine catabolism were shown to be to indole-3-lactate and arginic acid, respectively, which were excreted. The data presented provide clear evidence on the value of global metabolomic analyses in detecting species-specific metabolic features, thus application of this technology should be a major contributor to gaining greater understanding of how pathogens are adapted to infecting their hosts

APOE ε2 resilience for Alzheimer’s disease is mediated by plasma lipid species: Analysis of three independent cohort studies

Introduction The apolipoprotein E (APOE) genotype is the strongest genetic risk factor for late-onset Alzheimer\u27s disease. However, its effect on lipid metabolic pathways, and their mediating effect on disease risk, is poorly understood. Methods We performed lipidomic analysis on three independent cohorts (the Australian Imaging, Biomarkers and Lifestyle [AIBL] flagship study, n = 1087; the Alzheimer\u27s 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. Results 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\u27s disease. Individual lipid species or lipidomic models of APOE genotypes mediated up to 30% and 10% of APOE ε2 and ε4 treatment effect, respectively. Discussion Plasma lipid species mediate the treatment effect of APOE genotypes on Alzheimer\u27s disease and as such represent a potential therapeutic target