Theodore R. Sana

Rapid quantitative profiling of complex microbial populations

Diverse and complex microbial ecosystems are found in virtually every environment on earth, yet we know very little about their composition and ecology. Comprehensive identification and quantification of the constituents of these microbial communities—a ‘census’—is an essential foundation for understanding their biology. To address this problem, we developed, tested and optimized a DNA oligonucleotide microarray composed of 10 462 small subunit (SSU) ribosomal DNA (rDNA) probes (7167 unique sequences) selected to provide quantitative information on the taxonomic composition of diverse microbial populations. Using our optimized experimental approach, this microarray enabled detection and quantification of individual bacterial species present at fractional abundances of <0.1% in complex synthetic mixtures. The estimates of bacterial species abundance obtained using this microarray are similar to those obtained by phylogenetic analysis of SSU rDNA sequences from the same samples—the current ‘gold standard’ method for profiling microbial communities. Furthermore, probes designed to represent higher order taxonomic groups of bacterial species reliably detected microbes for which there were no species-specific probes. This simple, rapid microarray procedure can be used to explore and systematically characterize complex microbial communities, such as those found within the human body.

Analysis of hydrophilic metabolites by high-performance liquid chromatography–mass spectrometry using a silica hydride-based stationary phase

A novel silica hydride-based stationary phase was used to evaluate the retention behavior in the aqueous normal-phase (ANP) mode of standards representing three classes of metabolites. The effects on retention behavior of amino acids, carbohydrates and small organic acids were examined by altering the column temperature, and by adding different additives to both the mobile phase and sample solvent. Gradient mode results revealed the repeatability of retention times to be very stable for these compound classes. At both 15 and 30 degrees C, excellent RSD values were obtained with less than 1% variation for over 50 injections of an amino acid mixture. The ability to separate the 19 nonderivatized amino acid standards, organic acids and carbohydrates was demonstrated as well as the potential for this material to separate polar metabolites in complex fluids such as urine.

Metabolomic and transcriptomic analysis of the rice response to the bacterial blight pathogen Xanthomonas oryzae pv. oryzae

Bacterial leaf blight (BLB), caused by Xanthomonas oryzae pv. oryzae (Xoo), gives rise to devastating crop losses in rice. Disease resistant rice cultivars are the most economical way to combat the disease. The TP309 cultivar is susceptible to infection by Xoo strain PXO99. A transgenic variety, TP309_Xa21, expresses the pattern recognition receptor Xa21, and is resistant. PXO99△raxST, a strain lacking the raxST gene, is able to overcome Xa21-mediated immunity. We used a single extraction solvent to demonstrate comprehensive metabolomics and transcriptomics profiling under sample limited conditions, and analyze the molecular responses of two rice lines challenged with either PXO99 or PXO99△raxST. LC–TOF raw data file filtering resulted in better within group reproducibility of replicate samples for statistical analyses. Accurate mass match compound identification with molecular formula generation (MFG) ranking of 355 masses was achieved with the METLIN database. GC–TOF analysis yielded an additional 441 compounds after BinBase database processing, of which 154 were structurally identified by retention index/MS library matching. Multivariate statistics revealed that the susceptible and resistant genotypes possess distinct profiles. Although few mRNA and metabolite differences were detected in PXO99 challenged TP309 compared to mock, many differential changes occurred in the Xa21-mediated response to PXO99 and PXO99△raxST. Acetophenone, xanthophylls, fatty acids, alkaloids, glutathione, carbohydrate and lipid biosynthetic pathways were affected. Significant transcriptional induction of several pathogenesis related genes in Xa21 challenged strains, as well as differential changes to GAD, PAL, ICL1 and Glutathione-S-transferase transcripts indicated limited correlation with metabolite changes under single time point global profiling conditions.

A sample extraction and chromatographic strategy for increasing LC/MS detection coverage of the erythrocyte metabolome

Reproducible and comprehensive sample extraction and detection of metabolites with a broad range of physico-chemical properties from biological matrices can be a highly challenging process. A single LC/MS separation method was developed for a 2.1 mm x 100 mm, 1.8 microm ZORBAX SB-Aq column that was used to separate human erythrocyte metabolites extracted under sample extraction solvent conditions where the pH was neutral or had been adjusted to either, pH 2, 6 or 9. Internal standards were included and evaluated for tracking sample extraction efficiency. Through the combination of electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) techniques in both positive (+) and negative (-) ion modes, a total of 2370 features (compounds and associated compound related components: isotopes, adducts and dimers) were detected across all pHs. Broader coverage of the detected metabolome was achieved by observing that (1) performing extractions at pH 2 and 9, leads to a combined 92% increase in detected features over pH 7 alone; and (2) including APCI in the analysis results in a 34% increase in detected features, across all pHs, than the total number detected by ESI. A significant dependency of extraction solvent pH on the recovery of heme and other compounds was observed in erythrocytes and underscores the need for a comprehensive sample extraction strategy and LC/MS analysis in metabolomics profiling experiments.

Analysis of hydrophilic metabolites in physiological fluids by HPLC‐MS using a silica hydride‐based stationary phase

Aqueous normal-phase chromatography was used for the analysis of metabolites in human saliva and urine samples. The column was packed with a silica hydride type separation material. Several gradients were tested with different mobile phase additives in order to produce retention for amino acids, small organic acids, and carbohydrates. Detection was done by TOF MS. In some cases the relative concentration levels of various metabolites in human saliva were compared for normal patients and patients with pancreatic cancer or pancreatitis. The reproducibility of retention of individual metabolites in these complex matrices was tested for several compounds.

Metabolomic Profiling Reveals a Role for Androgen in Activating Amino Acid Metabolism and Methylation in Prostate Cancer Cells

Prostate cancer is the second leading cause of cancer related death in American men. Development and progression of clinically localized prostate cancer is highly dependent on androgen signaling. Metastatic tumors are initially responsive to anti-androgen therapy, however become resistant to this regimen upon progression. Genomic and proteomic studies have implicated a role for androgen in regulating metabolic processes in prostate cancer. However, there have been no metabolomic profiling studies conducted thus far that have examined androgen-regulated biochemical processes in prostate cancer. Here, we have used unbiased metabolomic profiling coupled with enrichment-based bioprocess mapping to obtain insights into the biochemical alterations mediated by androgen in prostate cancer cell lines. Our findings indicate that androgen exposure results in elevation of amino acid metabolism and alteration of methylation potential in prostate cancer cells. Further, metabolic phenotyping studies confirm higher flux through pathways associated with amino acid metabolism in prostate cancer cells treated with androgen. These findings provide insight into the potential biochemical processes regulated by androgen signaling in prostate cancer. Clinically, if validated, these pathways could be exploited to develop therapeutic strategies that supplement current androgen ablative treatments while the observed androgen-regulated metabolic signatures could be employed as biomarkers that presage the development of castrate-resistant prostate cancer.

Global Mass Spectrometry Based Metabolomics Profiling of Erythrocytes Infected with Plasmodium falciparum

Malaria is a global infectious disease that threatens the lives of millions of people. Transcriptomics, proteomics and functional genomics studies, as well as sequencing of the Plasmodium falciparum and Homo sapiens genomes, have shed new light on this host-parasite relationship. Recent advances in accurate mass measurement mass spectrometry, sophisticated data analysis software, and availability of biological pathway databases, have converged to facilitate our global, untargeted biochemical profiling study of in vitro P. falciparum-infected (IRBC) and uninfected (NRBC) erythrocytes. In order to expand the number of detectable metabolites, several key analytical steps in our workflows were optimized. Untargeted and targeted data mining resulted in detection of over one thousand features or chemical entities. Untargeted features were annotated via matching to the METLIN metabolite database. For targeted data mining, we queried the data using a compound database derived from a metabolic reconstruction of the P. falciparum genome. In total, over one hundred and fifty differential annotated metabolites were observed. To corroborate the representation of known biochemical pathways from our data, an inferential pathway analysis strategy was used to map annotated metabolites onto the BioCyc pathway collection. This hypothesis-generating approach resulted in over-representation of many metabolites onto several IRBC pathways, most prominently glycolysis. In addition, components of the “branched” TCA cycle, partial urea cycle, and nucleotide, amino acid, chorismate, sphingolipid and fatty acid metabolism were found to be altered in IRBCs. Interestingly, we detected and confirmed elevated levels for cyclic ADP ribose and phosphoribosyl AMP in IRBCs, a novel observation. These metabolites may play a role in regulating the release of intracellular Ca2+ during P. falciparum infection. Our results support a strategy of global metabolite profiling by untargeted data acquisition. Untargeted and targeted data mining workflows, when used together to perform pathway-inferred metabolomics, have the benefit of obviating MS/MS confirmation for every detected compound.

Abstract A52: Metabolomic profiling reveals impaired xenobiotic metabolism in bladder cancer

Introduction: Bladder cancer (BCa) is the second most prevalent urological malignancy and the fourth highest cause of cancer-related death in the United States. Earlier studies have linked BCa development to alterations in metabolic pathways. Significant among these are decreased activity of N-acetyl transferases causing a slow-acetylator phenotype leading to inefficient detoxification of aromatic hydrocarbons causal to onset of BCa. Interestingly, Afro-American patients inherently exhibit such slow acetylator phenotype and are known to have a more aggressive form of the tumor compared to Caucasians. This indicates existence of a metabolic niche that governs the racial disparity in BCa, which is not well understood. Also, there is an imminent need to develop non-invasive markers for early detection and prognosis of BCa, since urine cytology which is the current clinical standard is not specific to the tumor. Using mass spectrometry we report metabolic alterations in BCa and delineate bioprocesses that are altered during its progression. Our data for the first-time demonstrates role of methylation in attenuation of xenotbiotic metabolism in BCa. Furthermore, the metabolic profiles seed further analysis to examine the racial disparity in these tumors. Methods: Total metabolome from flash frozen clinically annotated bladder-derived tissues (n=58,31 benign adjacent and 27 BCa, 26 matched pairs) were examined using a combination of Q-TOF (unbiased) and triple-quadrupole (targeted) mass spectrometry. Panel of well-defined standards were used to ensure reproducibility of the profiling process. The metabolites were pre-fractionated using liquid chromatography prior to mass spectrometry in both the positive and negative ionization mode. The unbiased mass spectral data was searched using Metlin library to identify the compounds. The metabolomic profiles thus generated were analyzed to delineate class-specific signatures which were interrogated for altered bioprocesses using Molecular Concept Map (OCM, www.oncomine.org). The altered bioprocesses were validated in cell line models using a combination of Q-PCR, immunoblot analysis and functional assays. Results and discussion: A total of 2019 compounds were detected across the 58 bladder-derived specimens of which, 423 compounds were significantly altered in BCa compared to adjacent benign. 50 of the differential compounds were named and used for developing a classificatory signature and bioprocess mapping. Included among these were polycylic compounds like aniline, catechols, aromatic amino acids, polyamines and S-adenosyl methionine (SAM). Interestingly this BCa-specific metabolic signature in tissues was able to delineate tumor from benign with an accuracy of 75 %. Importantly the functional mapping of the metabolic data revealed enhanced methylation potential in tumors as being one of the factors de-regulating the xenobiotic metabolism. In vitro experiments using bisulfite sequencing and methyltransferase inhibitor 5-Aza-cytidine confirmed this methylation-induced attenuation of phase I/II metabolic genes namely CYP1A1, CYP1B1, EPHX1 and GSTT1 in BCa. In summary, using unbiased metabolomic profiling report metabolic fingerprint for bladder cancer. Importantly our data for the first time reveals methylation-induced silencing of xenobiotic metabolism in bladder tumors. Citation Information: Cancer Epidemiol Biomarkers Prev 2010;19(10 Suppl):A52.