Project description:The aim of untargeted metabolomics is to profile as many metabolites as possible, yet a major challenge is comparing experimental method performance on the basis of metabolome coverage. To date, most published approaches have compared experimental methods by counting the total number of features detected. Due to artifactual interference, however, this number is highly variable and therefore is a poor metric for comparing metabolomic methods. Here we introduce an alternative approach to benchmarking metabolome coverage which relies on mixed Escherichia coli extracts from cells cultured in regular and (13)C-enriched media. After mass spectrometry-based metabolomic analysis of these extracts, we "credential" features arising from E. coli metabolites on the basis of isotope spacing and intensity. This credentialing platform enables us to accurately compare the number of nonartifactual features yielded by different experimental approaches. We highlight the value of our platform by reoptimizing a published untargeted metabolomic method for XCMS data processing. Compared to the published parameters, the new XCMS parameters decrease the total number of features by 15% (a reduction in noise features) while increasing the number of true metabolites detected and grouped by 20%. Our credentialing platform relies on easily generated E. coli samples and a simple software algorithm that is freely available on our laboratory Web site (http://pattilab.wustl.edu/software/credential/). We have validated the credentialing platform with reversed-phase and hydrophilic interaction liquid chromatography as well as Agilent, Thermo Scientific, AB SCIEX, and LECO mass spectrometers. Thus, the credentialing platform can readily be applied by any laboratory to optimize their untargeted metabolomic pipeline for metabolite extraction, chromatographic separation, mass spectrometric detection, and bioinformatic processing.
Project description:Plants sense light and temperature changes to regulate flowering time. The expression of the florigen gene, FLOWERING LOCUS T (FT), peaks in the morning during spring, a different pattern than we observe in the lab. Providing our lab growth conditions with a red/far-red light ratio similar to open field conditions and average natural temperature oscillation is sufficient to mimic the FT expression and flowering time in natural long days. Here, we use RNA-seq to identify and understand the molecular differences between natural growth conditions, conventional lab growth conditions, and supplemented lab growth conditions that mimic natural conditions. Non-NIH grant(s): Grant ID: NSF 1656076 Grant title: Exploring Seasonal Flowering Mechanisms Affiliation: University of Washington Name: Takato Imaizumi
Project description:Three osteosarcoma (OS) cell lines (MG-63, Saos-2 and U-2 OS) and 1 osteoblastic cell line (hFOB1.19) were collected for this work. MG-63 was kindly provided by Dr. Agi Grigoriadis from University College London. Saos-2, U-2 OS and hFOB1.19 were purchased from ATCC. All cells used were kept in exponential phase of growth. Total RNA was extracted using the RNeasy Total RNA Isolation kit (QIAGEN). The quality and purity of the products were controlled by Agilent 2100. The final synthesized biotinylated cDNAs were hybridized to Affymetrix GeneChip® U133A 2.0 arrays following the protocol strictly. Arrays were scanned with the Affymetrix scanner 3000. Data analysis was performed by Microarray Suite 5.0 after pre-standard procedure. Link-test on datasets from both SELDI-TOF-MS and microarray high-throughput analysis platforms can accelerate the identification of tumor biomarkers. The results confirmed that CYC-1 with important biomedical function was an effective candidate biomarker for osteosarcoma early diagnosis.
Project description:Diagnosis of ovarian cancer is difficult due to the lack of clinical symptoms and effective screening algorithms. In this study, we aim to develop models for ovarian cancer diagnosis by detecting metabolites in urine and plasma samples. Ultra-high-performance liquid chromatography and quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) in positive ion mode was used for metabolome quantification in 235 urine samples and 331 plasma samples. Then, Urine and plasma metabolomic profiles were analyzed by univariate and multivariate statistics. Four groups of samples: normal control, benign, borderline and malignant ovarian tumors were enrolled in this study. A total of 1330 features and 1302 features were detected from urine and plasma samples respectively. Based on two urine putative metabolites, five plasma putative metabolites and five urine putative metabolites, three models for distinguishing normal-ovarian tumors, benign-malignant (borderline + malignant) and borderline-malignant ovarian tumors were developed respectively. The AUC (Area Under Curve) values were 0.987, 0876 and 0.943 in discovery set and 0.984, 0.896 and 0.836 in validation set for three models. Specially, the diagnostic model based on 5 plasma putative metabolites had better early-stage diagnosis performance than CA125 alone. The AUC values of the model were 0.847 and 0.988 in discovery and validation set respectively. Our results showed that normal and ovarian tumors have unique metabolic signature in urine and plasma samples, which shed light on the ovarian cancer diagnosis and classification.
Project description:Three osteosarcoma (OS) cell lines (MG-63, Saos-2 and U-2 OS) and 1 osteoblastic cell line (hFOB1.19) were collected for this work. MG-63 was kindly provided by Dr. Agi Grigoriadis from University College London. Saos-2, U-2 OS and hFOB1.19 were purchased from ATCC. All cells used were kept in exponential phase of growth. Total RNA was extracted using the RNeasy Total RNA Isolation kit (QIAGEN). The quality and purity of the products were controlled by Agilent 2100. The final synthesized biotinylated cDNAs were hybridized to Affymetrix GeneChip® U133A 2.0 arrays following the protocol strictly. Arrays were scanned with the Affymetrix scanner 3000. Data analysis was performed by Microarray Suite 5.0 after pre-standard procedure.
Project description:Breast cancer (BC) remains the second leading cause of death among women worldwide. An emerging approach based on the identification of endogenous metabolites (EMs) and the establishment of the metabolomic fingerprint of biological fluids constitutes a new frontier in medical diagnostics and a promising strategy to differentiate cancer patients from healthy individuals. In this work we aimed to establish the urinary metabolomic patterns from 40 BC patients and 38 healthy controls (CTL) using proton nuclear magnetic resonance spectroscopy (1H-NMR) as a powerful approach to identify a set of BC-specific metabolites which might be employed in the diagnosis of BC. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied to a 1H-NMR processed data matrix. Metabolomic patterns distinguished BC from CTL urine samples, suggesting a unique metabolite profile for each investigated group. A total of 10 metabolites exhibited the highest contribution towards discriminating BC patients from healthy controls (variable importance in projection (VIP) >1, p < 0.05). The discrimination efficiency and accuracy of the urinary EMs were ascertained by receiver operating characteristic curve (ROC) analysis that allowed the identification of some metabolites with the highest sensitivities and specificities to discriminate BC patients from healthy controls (e.g. creatine, glycine, trimethylamine N-oxide, and serine). The metabolomic pathway analysis indicated several metabolism pathway disruptions, including amino acid and carbohydrate metabolisms, in BC patients, namely, glycine and butanoate metabolisms. The obtained results support the high throughput potential of NMR-based urinary metabolomics patterns in discriminating BC patients from CTL. Further investigations could unravel novel mechanistic insights into disease pathophysiology, monitor disease recurrence, and predict patient response towards therapy.
Project description:Metabolomics is providing new dimensions into understanding the intracellular adaptive responses in plants to external stimuli. In this study, a multi-technology-metabolomic approach was used to investigate the effect of the fungal sterol, ergosterol, on the metabolome of cultured tobacco cells. Cell suspensions were treated with different concentrations (0-1000 nM) of ergosterol and incubated for different time periods (0-24 h). Intracellular metabolites were extracted with two methods: a selective dispersive liquid-liquid micro-extraction and a general methanol extraction. Chromatographic techniques (GC-FID, GC-MS, GC × GC-TOF-MS, UHPLC-MS) and (1)H NMR spectroscopy were used for quantitative and qualitative analyses. Multivariate data analyses (PCA and OPLS-DA models) were used to extract interpretable information from the multidimensional data generated from the analytical techniques. The results showed that ergosterol triggered differential changes in the metabolome of the cells, leading to variation in the biosynthesis of secondary metabolites. PCA scores plots revealed dose- and time-dependent metabolic variations, with optimal treatment conditions being found to be 300 nM ergosterol and an 18 h incubation period. The observed ergosterol-induced metabolic changes were correlated with changes in defence-related metabolites. The 'defensome' involved increases in terpenoid metabolites with five antimicrobial compounds (the bicyclic sesquiterpenoid phytoalexins: phytuberin, solavetivone, capsidiol, lubimin and rishitin) and other metabolites (abscisic acid and phytosterols) putatively identified. In addition, various phenylpropanoid precursors, cinnamic acid derivatives and - conjugates, coumarins and lignin monomers were annotated. These annotated metabolites revealed a dynamic reprogramming of metabolic networks that are functionally correlated, with a high complexity in their regulation.