Extracting biomedical information from large metabolomic datasets by multivariate data analysis is of considerable complexity. Common challenges include among others screening for differentially produced metabolites, estimation of fold changes, and sample classification. Prior to these analysis steps, it is important to minimize contributions from unwanted biases and experimental variance. This is the goal of data preprocessing. In this work, different data normalization methods were compared systematically employing two different datasets generated by means of nuclear magnetic resonance (NMR) spectroscopy. To this end, two different types of normalization methods were used, one aiming to remove unwanted sample-to-sample variation while the other adjusts the variance of the different metabolites by variable scaling and variance stabilization methods. The impact of all methods tested on sample classification was evaluated on urinary NMR fingerprints obtained from healthy volunteers and patients suffering from autosomal polycystic kidney disease (ADPKD). Performance in terms of screening for differentially produced metabolites was investigated on a dataset following a Latin-square design, where varied amounts of 8 different metabolites were spiked into a human urine matrix while keeping the total spike-in amount constant. In addition, specific tests were conducted to systematically investigate the influence of the different preprocessing methods on the structure of the analyzed data. In conclusion, preprocessing methods originally developed for DNA microarray analysis, in particular, Quantile and Cubic-Spline Normalization, performed best in reducing bias, accurately detecting fold changes, and classifying samples.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-011-0350-z) contains supplementary material, which is available to authorized users.

Familial myelodysplastic syndromes arise from haploinsufficiency of genes involved in hematopoiesis and are primarily associated with early-onset disease. Here we describe a familial syndrome in seven patients from four unrelated pedigrees presenting with myelodysplastic syndrome and loss of chromosome 7/7q. Their median age at diagnosis was 2.1 years (range, 1–42). All patients presented with thrombocytopenia with or without additional cytopenias and a hypocellular marrow without an increase of blasts. Genomic studies identified constitutional mutations (p.H880Q, p.R986H, p.R986C and p.V1512M) in the SAMD9L gene on 7q21, with decreased allele frequency in hematopoiesis. The non-random loss of mutated SAMD9L alleles was attained via monosomy 7, deletion 7q, UPD7q, or acquired truncating SAMD9L variants p.R1188X and p.S1317RfsX21. Incomplete penetrance was noted in 30% (3/10) of mutation carriers. Long-term observation revealed divergent outcomes with either progression to leukemia and/or accumulation of driver mutations (n=2), persistent monosomy 7 (n=4), and transient monosomy 7 followed by spontaneous recovery with SAMD9L-wildtype UPD7q (n=2). Dysmorphic features or neurological symptoms were absent in our patients, pointing to the notion that myelodysplasia with monosomy 7 can be a sole manifestation of SAMD9L disease. Collectively, our results define a new subtype of familial myelodysplastic syndrome and provide an explanation for the phenomenon of transient monosomy 7. Registered at: www.clinicaltrials.gov; #NCT00047268.

Lactate formation in highly proliferative tumors such as malignant gliomas is associated with poor survival and contributes to the suppression of local immunity. Here, we report that diclofenac used at nontoxic concentrations significantly decreased lactate production in murine glioma cells and inhibited the expression of lactate dehydrogenase-A in vitro. Lactate reduction was accompanied by a dose-dependent inhibition of cell growth and a cell cycle arrest at the G2/M checkpoint. In the presence of diclofenac, murine bone marrow-derived dendritic cells (DCs) showed enhanced IL-12, but decreased IL-10 secretion on Toll-like receptor stimulation with R848 that correlated with reduced lactate levels in the glioma cell coculture and a blockade of signal transducers and activators of transcription 3 phosphorylation. In vivo, diclofenac treatment diminished intratumoral lactate levels and resulted in a significant delay of glioma growth. Ex vivo analyses revealed that tumor-infiltrating DCs regained their capacity to produce IL-12 on R848 stimulation. Moreover, diclofenac reduced the number of tumor-infiltrating regulatory T cells and impaired the upregulation of the Treg activation marker CD25. Nevertheless, a single intratumoral injection of R848 combined with diclofenac failed to induce an additional survival advantage in glioma-bearing mice. Further analyses illustrated that the presence of diclofenac during T-cell activation compromised INF-c production and T-cell proliferation, indicating that immunotherapeutic approaches have to be carefully timed when combined with diclofenac. In summary, diclofenac appears as an attractive agent for targeting lactate production and counteracting local immune suppression in malignant gliomas.One of the most important adaptive responses of quickly proliferating tumors is the metabolic switch from the oxidative to the glycolytic pathway. The driving force, among others, is hypoxia, which induces the upregulation of glycolytic enzymes and glucose transporters via HIF-1 activation. 1,2 Alternatively, glycolysis can be stimulated by oncogenic transformation mediated by c-myc. 3 As a consequence of elevated glycolysis, tumors upregulate lactate dehydrogenase (LDH), leading to an increased production of lactate, which is released into the tumor stroma. 4 LDH is a tetrameric enzyme composed of 4 muscle (M) and/or heart (H) subunits, encoded by two distinct genes, LDH-A and LDH-B. An increase of LDH-A over LDH-B activation favors the production of a specific LDH isoenzyme (e.g., LDH5) that is more efficient in catalyzing the conversion of pyruvate to lactate. The LDH-A gene is under the direct transcriptional regulation of HIF-1 and c-myc. 5,6 Increased LDH5 expression and elevated lactate levels correlate with poorer prognosis, poor disease-free or metastasis-free survival and poor overall survival in several tumor entities, including malignant gliomas. [7][8][9][10] Accumulating data suggest that dendritic cells (DCs) play a central role in the initiation of immune responses in the ...

Data normalization is an essential step in NMR-based metabolomics. Conducted properly, it improves data quality and removes unwanted biases. The choice of the appropriate normalization method is critical and depends on the inherent properties of the data set in question. In particular, the presence of unbalanced metabolic regulation, where the different specimens and cohorts under investigation do not contain approximately equal shares of up- and down-regulated features, may strongly influence data normalization. Here, we demonstrate the suitability of the Shapiro-Wilk test to detect such unbalanced regulation. Next, employing a Latin-square design consisting of eight metabolites spiked into a urine specimen at eight different known concentrations, we show that commonly used normalization and scaling methods fail to retrieve true metabolite concentrations in the presence of increasing amounts of glucose added to simulate unbalanced regulation. However, by learning the normalization parameters on a subset of nonregulated features only, Linear Baseline Normalization, Probabilistic Quotient Normalization, and Variance Stabilization Normalization were found to account well for different dilutions of the samples without distorting the true spike-in levels even in the presence of marked unbalanced metabolic regulation. Finally, the methods described were applied successfully to a real world example of unbalanced regulation, namely, a set of plasma specimens collected from patients with and without acute kidney injury after cardiac surgery with cardiopulmonary bypass use.

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