Kristaps Klavins

Blood metabolite markers of preclinical Alzheimer's disease in two longitudinally followed cohorts of older individuals.

Recently, quantitative metabolomics identified a panel of 10 plasma lipids that were highly predictive of conversion to Alzheimers disease (AD) in cognitively normal older individuals (n = 28, area under the curve [AUC] = 0.92, sensitivity/specificity of 90%/90%).

Metabolic network failures in Alzheimer's disease—A biochemical road map

The Alzheimers Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimers disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance.

The ratio of phosphatidylcholines to lysophosphatidylcholines in plasma differentiates healthy controls from patients with Alzheimer's disease and mild cognitive impairment.

Metabolomic processes have been identified as being strongly linked to the development of Alzheimers disease (AD). Thus, lipid metabolites appear to be highly useful as diagnostic substrates for the diagnosis of AD and mild cognitive impairment (MCI) in plasma.

Harmonizing lipidomics: NIST interlaboratory comparison exercise for lipidomics using SRM 1950–Metabolites in Frozen Human Plasma

As the lipidomics field continues to advance, self-evaluation within the community is critical. Here, we performed an interlaboratory comparison exercise for lipidomics using Standard Reference Material (SRM) 1950–Metabolites in Frozen Human Plasma, a commercially available reference material. The interlaboratory study comprised 31 diverse laboratories, with each laboratory using a different lipidomics workflow. A total of 1,527 unique lipids were measured across all laboratories and consensus location estimates and associated uncertainties were determined for 339 of these lipids measured at the sum composition level by five or more participating laboratories. These evaluated lipids detected in SRM 1950 serve as community-wide benchmarks for intra- and interlaboratory quality control and method validation. These analyses were performed using nonstandardized laboratory-independent workflows. The consensus locations were also compared with a previous examination of SRM 1950 by the LIPID MAPS consortium. While the central theme of the interlaboratory study was to provide values to help harmonize lipids, lipid mediators, and precursor measurements across the community, it was also initiated to stimulate a discussion regarding areas in need of improvement.

Targeted metabolomics and medication classification data from participants in the ADNI1 cohort

Alzheimer’s disease (AD) is the most common neurodegenerative disease presenting major health and economic challenges that continue to grow. Mechanisms of disease are poorly understood but significant data point to metabolic defects that might contribute to disease pathogenesis. The Alzheimer Disease Metabolomics Consortium (ADMC) in partnership with Alzheimer Disease Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for AD. Using targeted and non- targeted metabolomics and lipidomics platforms we are mapping metabolic pathway and network failures across the trajectory of disease. In this report we present quantitative metabolomics data generated on serum from 199 control, 356 mild cognitive impairment and 175 AD subjects enrolled in ADNI1 using AbsoluteIDQ-p180 platform, along with the pipeline for data preprocessing and medication classification for confound correction. The dataset presented here is the first of eight metabolomics datasets being generated for broad biochemical investigation of the AD metabolome. We expect that these collective metabolomics datasets will provide valuable resources for researchers to identify novel molecular mechanisms contributing to AD pathogenesis and disease phenotypes.

Metabolomic profiling of ascending thoracic aortic aneurysms and dissections - Implications for pathophysiology and biomarker discovery

Objective Our basic understanding of ascending thoracic aortic aneurysm (ATAA) pathogenesis is still very limited, hampering early diagnosis, risk prediction, and development of treatment options. “Omics”-technologies, ideal to reveal tissue alterations from the normal physiological state due to disease have hardly been applied in the field. Using a metabolomic approach, with this study the authors seek to define tissue differences between controls and various forms of ATAAs. Methods Using a targeted FIA-MS/MS metabolomics approach, we analysed and compared the metabolic profiles of ascending thoracic aortic wall tissue of age-matched controls (n = 8), bicuspid aortic valve-associated aneurysms (BAV-A; n = 9), tricuspid aortic valve-associated aneurysms (TAV-A; n = 14), and tricuspid aortic valve-associated aortic dissections (TAV-Diss; n = 6). Results With sphingomyelin (SM) (OH) C22:2, SM C18:1, SM C22:1, and SM C24:1 only 4 out of 92 detectable metabolites differed significantly between controls and BAV-A samples. Between controls and TAV-Diss samples only phosphatidylcholine (PC) ae C32:1 differed. Importantly, our analyses revealed a general increase in the amount of total sphingomyelin levels in BAV-A and TAV-Diss samples compared to controls. Conclusions Significantly increased levels of sphingomyelins in BAV-A and TAV-Diss samples compared to controls may argue for a repression of sphingomyelinase activity and the sphingomyelinase-ceramide pathway, which may result in an inhibition of tissue regeneration; a potential basis for disease initiation and progression.

Increased urine acylcarnitines in diabetic ApoE-/- mice: Hydroxytetradecadienoylcarnitine (C14:2-OH) reflects diabetic nephropathy in a context of hyperlipidemia

Hyperlipidemia is a risk factor for initiation and progression of diabetic nephropathy but the metabolic pathways altered in the diabetic kidney in a context of hyperlipidemia remain incompletely described. Assuming that changes in urine composition reflect the alteration of renal metabolism and function, we analyzed the urine metabolite composition of diabetic (streptozotocin-treatment) and control (non diabetic) ApoE-/- mice fed a high cholesterol diet using targeted quantitative metabolomics. Urine metabolome was also compared to the plasma metabolome of the same animals. As previously shown, urine albuminuria/urine creatinine ratio (uACR) and glomerular area and plasma lipids (cholesterol, triglycerides) were more elevated in diabetic mice compared to control. After adjustment to urine creatinine, the abundance of 52 urine metabolites was significantly different in diabetic mice compared to control. Among them was a unique metabolite, C14:2-OH (3-hydroxytetradecadienoylcarnitine) that, in diabetic mice, was positively and significantly correlated with uACR, glomerular hypertrophy, blood glucose and plasma lipids. That metabolite was not detected in plasma. C14:2-OH is a long-chain acylcarnitine reminiscent of altered fatty acid beta oxidation. Other acylcarnitines, particularly the short chains C3-OH, C3-DC, C4:1, C5-DC, C5-M-DC, C5-OH that are reminiscent of altered oxidation of branched and aromatic amino acids were also exclusively detected in urine but were only correlated with plasma lipids. Finally, the renal gene expression of several enzymes involved in fatty acid and/or amino acid oxidation was significantly reduced in diabetic mice compared to control. This included the bifunctional enoyl-CoA hydratase/3-hydroxyacyl-CoA (Ehhadh) that might play a central role in C14:2-OH production. This study indicate that the development of diabetes in a context of hyperlipidemia is associated with a reduced capacity of kidney to oxidize fatty acids and amino acids with the consequence of an elevation of urinary acetylcarnitines including C14:2-OH that specifically reflects diabetic nephropathy.

THE ALZHEIMER’S METABOLOME: RELATIONSHIP TO PATHOLOGICAL MARKERS AND COGNITIVE DECLINE IN THE ALZHEIMER’S DISEASE NEUROIMAGING INITIATIVE (ADNI)

frequency, functional annotation, and other datasets such as the ADSP case-control dataset. Results:Four missense variants under 7q14 and 5 variants under 11q12 segregate in at least 1 CH family, including GHRHR and TMEM132A. We identified several intronic variants in the 3q29 region segregating with LOAD in multiple CH families. Further annotation of the non-coding variants suggests regulatory features. Analysis of the NHW families has identified 19 genic and 11 intergenic variants of high priority. Three of these are NOS1AP gene variants; the variants segregated with disease (including one with CADD score of 13.6), were absent from unaffecteds, and NOS1AP showed nominal disease association in the ADSP case-control dataset (p1⁄40.049). Additional intergenic variants segregated with disease and likely influence transcription factor binding (CATO annotation). Within the 15q14.3 region, 2 deleterious variants (PRDM9 and Drosha) and 5 intergenic variants with a CADD score>15 were found to be shared in by all relatives of the major DF. Additional analysis is ongoing. Conclusions: These results suggest that both coding and non-coding rare variants influence LOAD susceptibility, and show the continued utility of family-based approaches. Variants identified are currently being validated using orthogonal technologies, and follow-up analyses are being conducted.