Project description:MicroRNAs are important negative regulators of protein coding gene expression, and have been studied intensively over the last few years. To this purpose, different measurement platforms to determine their RNA abundance levels in biological samples have been developed. In this study, we have systematically compared 12 commercially available microRNA expression platforms by measuring an identical set of 20 standardized positive and negative control samples, including human universal reference RNA, human brain RNA and titrations thereof, human serum samples, and synthetic spikes from homologous microRNA family members. We developed novel quality metrics in order to objectively assess platform performance of very different technologies such as small RNA sequencing, RT-qPCR and (microarray) hybridization. We assessed reproducibility, sensitivity, quantitative performance, and specificity. The results indicate that each method has its strengths and weaknesses, which helps guiding informed selection of a quantitative microRNA gene expression platform in function of particular study goals.
Project description:We evaluated whether targeted next-generation sequencing (NGS) using the Ion Torrent Personal Genome Sequencer of cfDNA could identify prognostic or predictive factors for overall survival (OS) or progression free survival (PFS) within a large cohort of patients with advanced lung adenocarcinoma enrolled in the GALAXY-1 trial.
Project description:The occurrence of systemic inflammatory response syndrome (SIRS) remains a major problem in intensive care units with high morbidity and mortality. The differentiation between noninfectious and infectious etiologies of this disorder is challenging in routine clinical practice. Many biomarkers have been suggested for this purpose; however, sensitivity and specificity even of high-ranking biomarkers remain insufficient. Recently, metabolic profiling has attracted interest for biomarker discovery. The objective of this study was to identify metabolic biomarkers for differentiation of SIRS/sepsis. A total of 186 meta-bolites comprising six analyte classes were determined in 143 patients (74 SIRS, 69 sepsis) by LC-MS/MS. Two markers (C10:1 and PCaaC32:0) revealed significantly higher concentrations in sepsis. A classification model comprising these markers resulted in 80% and 70% correct classifications in a training set and a test set, respectively.This study demonstrates that acylcarnitines and glycerophosphatidylcholines may be helpful for differentiation of infectious from noninfectious systemic inflammation due to their significantly higher concentration in sepsis patients. Considering the well known pathophysiological relevance of lipid induction by bacterial components, metabolites as identified in this study are promising biomarker candidates in the differential diagnosis of SIRS and sepsis.
Project description:Multiple reaction monitoring (MRM) is a powerful and popular technique used for metabolite quantification in targeted metabolomics. Accurate and consistent quantitation of metabolites from the MRM data is essential for subsequent analyses. Here, we developed an automated tool, MRMQuant, for targeted metabolomic quantitation using high-throughput liquid chromatography-tandem mass spectrometry MRM data to provide users with an easy-to-use tool for accurate MRM data quantitation with minimal human intervention. This tool has many user-friendly functions and features to inspect and correct the quantitation results as required. MRMQuant possesses the following features to ensure accurate quantitation: (1) dynamic signal smoothing, (2) automatic deconvolution of coeluted peaks, (3) absolute quantitation via standard curves and/or internal standards, (4) visualized inspection and correction, (5) corrections applicable to multiple samples, and (6) batch-effect correction.
Project description:A central aim in the evaluation of non-targeted metabolomics data is the detection of intensity patterns that differ between experimental conditions as well as the identification of the underlying metabolites and their association with metabolic pathways. In this context, the identification of metabolites based on non-targeted mass spectrometry data is a major bottleneck. In many applications, this identification needs to be guided by expert knowledge and interactive tools for exploratory data analysis can significantly support this process. Additionally, the integration of data from other omics platforms, such as DNA microarray-based transcriptomics, can provide valuable hints and thereby facilitate the identification of metabolites via the reconstruction of related metabolic pathways. We here introduce the MarVis-Pathway tool, which allows the user to identify metabolites by annotation of pathways from cross-omics data. The analysis is supported by an extensive framework for pathway enrichment and meta-analysis. The tool allows the mapping of data set features by ID, name, and accurate mass, and can incorporate information from adduct and isotope correction of mass spectrometry data. MarVis-Pathway was integrated in the MarVis-Suite (http://marvis.gobics.de), which features the seamless highly interactive filtering, combination, clustering, and visualization of omics data sets. The functionality of the new software tool is illustrated using combined mass spectrometry and DNA microarray data. This application confirms jasmonate biosynthesis as important metabolic pathway that is upregulated during the wound response of Arabidopsis plants.
Project description: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.
Project description:SummaryNon-targeted metabolomics technologies often yield data in which abundance for any given metabolite is observed and quantified for some samples and reported as missing for other samples. Apparent missingness can be due to true absence of the metabolite in the sample or presence at a level below detectability. Mixture-model analysis can formally account for metabolite 'missingness' due to absence or undetectability, but software for this type of analysis in the high-throughput setting is limited. The R package metabomxtr has been developed to facilitate mixture-model analysis of non-targeted metabolomics data in which only a portion of samples have quantifiable abundance for certain metabolites.Availability and implementationmetabomxtr is available through Bioconductor. It is released under the GPL-2 license.Contactdscholtens@northwestern.eduSupplementary informationSupplementary data are available at Bioinformatics online.
Project description:Better risk prediction and new molecular targets are key priorities in type 2 diabetes (T2D) research. Little is known about the role of the urine metabolome in predicting the risk of T2D. We aimed to use non-targeted urine metabolomics to discover biomarkers and improve risk prediction for T2D. Urine samples from two community cohorts of 1,424 adults were analyzed by ultra-performance liquid chromatography/mass spectrometry (UPLC-MS). In a discovery/replication design, three out of 62 annotated metabolites were associated with prevalent T2D, notably lower urine levels of 3-hydroxyundecanoyl-carnitine. In participants without diabetes at baseline, LASSO regression in the training set selected six metabolites that improved prediction of T2D beyond established risk factors risk over up to 12 years' follow-up in the test sample, from C-statistic 0.866 to 0.892. Our results in one of the largest non-targeted urinary metabolomics study to date demonstrate the role of the urine metabolome in identifying at-risk persons for T2D and suggest urine 3-hydroxyundecanoyl-carnitine as a biomarker candidate.