Guido Dallmann

Urine and plasma metabolites predict the development of diabetic nephropathy in individuals with Type 2 diabetes mellitus.

Early detection of individuals with Type 2 diabetes mellitus or hypertension at risk for micro‐ or macroalbuminuria may facilitate prevention and treatment of renal disease. We aimed to discover plasma and urine metabolites that predict the development of micro‐ or macroalbuminuria.

Complexity and pitfalls of mass spectrometry-based targeted metabolomics in brain research.

Current quantitative metabolomic research in brain tissue is challenged by several analytical issues. To compare data of metabolite pattern, ratios of individual metabolite concentrations and composed classifiers characterizing a distinct state, standardized workup conditions, and extraction medium are crucial. Differences in physicochemical properties of individual compounds and compound classes such as polarity determine extraction yields and, thus, ratios of compounds with varying properties. Also, variations in suppressive effects related to coextracted matrix components affect standards or references and their concentration-dependent responses.The selection of a common tissue extraction protocol is an ill-posed problem because it can be regarded as a multiple objective decision depending on factors such as sample handling practicability, measurement precision, control of matrix effects, and relevance of the chemical assay. This study systematically evaluates the impact of extraction solvents and the impact of the complex brain tissue on measured metabolite levels, taking into account ionization efficiency as well as challenges encountered in the trace-level quantification of the analytes in brain matrices. In comparison with previous studies that relied on nontargeted platforms, consequently emphasizing the global behavior of the metabolomic fingerprint, here we focus on several series of metabolites spanning over extensive polarity, concentration, and molecular mass ranges.

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.

Prognostic clinical and molecular biomarkers of renal disease in type 2 diabetes

Diabetic kidney disease occurs in ∼ 25-40% of patients with type 2 diabetes. Given the high risk of progressive renal function loss and end-stage renal disease, early identification of patients with a renal risk is important. Novel biomarkers may aid in improving renal risk stratification. In this review, we first focus on the classical panel of albuminuria and estimated glomerular filtration rate as the primary clinical predictors of renal disease and then move our attention to novel biomarkers, primarily concentrating on assay-based multiple/panel biomarkers, proteomics biomarkers and metabolomics biomarkers. We focus on multiple biomarker panels since the molecular processes of renal disease progression in type 2 diabetes are heterogeneous, rendering it unlikely that a single biomarker significantly adds to clinical risk prediction. A limited number of prospective studies of multiple biomarkers address the predictive performance of novel biomarker panels in addition to the classical panel in type 2 diabetes. However, the prospective studies conducted so far have small sample sizes, are insufficiently powered and lack external validation. Adequately sized validation studies of multiple biomarker panels are thus required. There is also a paucity of studies that assess the effect of treatments on novel biomarker panels and determine whether initial treatment-induced changes in novel biomarkers predict changes in long-term renal outcomes. Such studies can not only improve our healthcare but also our understanding of the mechanisms of actions of existing and novel drugs and may yield biomarkers that can be used to monitor drug response. We conclude that this will be an area to focus research on in the future.

Model-driven discovery of long-chain fatty acid metabolic reprogramming in heterogeneous prostate cancer cells.

Epithelial-mesenchymal-transition promotes intra-tumoral heterogeneity, by enhancing tumor cell invasiveness and promoting drug resistance. We integrated transcriptomic data for two clonal subpopulations from a prostate cancer cell line (PC-3) into a genome-scale metabolic network model to explore their metabolic differences and potential vulnerabilities. In this dual cell model, PC-3/S cells express Epithelial-mesenchymal-transition markers and display high invasiveness and low metastatic potential, while PC-3/M cells present the opposite phenotype and higher proliferative rate. Model-driven analysis and experimental validations unveiled a marked metabolic reprogramming in long-chain fatty acids metabolism. While PC-3/M cells showed an enhanced entry of long-chain fatty acids into the mitochondria, PC-3/S cells used long-chain fatty acids as precursors of eicosanoid metabolism. We suggest that this metabolic reprogramming endows PC-3/M cells with augmented energy metabolism for fast proliferation and PC-3/S cells with increased eicosanoid production impacting angiogenesis, cell adhesion and invasion. PC-3/S metabolism also promotes the accumulation of docosahexaenoic acid, a long-chain fatty acid with antiproliferative effects. The potential therapeutic significance of our model was supported by a differential sensitivity of PC-3/M cells to etomoxir, an inhibitor of long-chain fatty acid transport to the mitochondria.

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.