Project description:The interplay between pathogens and hosts has been studied for decades using targeted approaches such as the analysis of mutants and host immunological responses. Although much has been learned from such studies, they focus on individual pathways and fail to reveal the global effects of infection on the host. To alleviate this issue, high-throughput methods such as transcriptomics and proteomics have been used to study host-pathogen interactions. Recently, metabolomics was established as a new method to study changes in the biochemical composition of host tissues. We report a metabolomics study of Salmonella enterica serovar Typhimurium infection. We used Fourier Transform Ion Cyclotron Resonance Mass Spectrometry with Direct Infusion to reveal that dozens of host metabolic pathways are affected by Salmonella in a murine infection model. In particular, multiple host hormone pathways are disrupted. Our results identify unappreciated effects of infection on host metabolism and shed light on mechanisms used by Salmonella to cause disease, and by the host to counter infection. Female C57BL/6 mice were infected with Salmonella enterica serovar Typhimurium SL1344 cells by oral gavage. Feces and livers were collected and metabolites extracted using acetonitrile. For experiments with feces, samples were collected from 4 mice before and after infection. For liver experiments, 11 uninfected and 11 infected mice were used. Samples were combined into 3 groups of 3-4 mice each, resulting in the analysis of 3 group samples of uninfected and 3 of infected mice. Extracts were infused into a 12-T Apex-Qe hybrid quadrupole-FT-ICR mass spectrometer equipped with an Apollo II electrospray ionization source, a quadrupole mass filter and a hexapole collision cell. Raw mass spectrometry data were processed as described elsewhere (Han et al. 2008. Metabolomics. 4:128-140 [PMID 19081807]). To identify differences in metabolite composition between uninfected and infected samples, we filtered the list of masses for metabolites which were present on one set of samples but not the other. Additionally, we calculated the ratios between averaged intensities of metabolites from uninfected and infected mice. To assign possible metabolite identities, monoisotopic neutral masses of interest were queried against MassTrix (http://masstrix.org). Masses were searched against the Mus musculus database within a mass error of 3 ppm. Data were analyzed by unpaired t tests with 95% confidence intervals.
Project description:Bulk high-resolution mass spectrometry can provide sensitive and global snapshots of metabolites involved in cancer metabolism. However, intra-tumor heterogeneity (IntraTH) convolutes the cellular origins and tumor pathologies associated with the detected metabolites, thus making the elucidation of reproducible metabolic pathways and/or biomarkers very challenging. Here, we present “Spatially-guided MEtabolomics (SgME) profiling”, a multi-modal metabolomics data analysis approach to delineate IntraTH by integrating spatial and bulk metabolomics profiles from the same tumors. We applied SgME profiling to 117 tumor and adjacent normal tissues from 26 surgically resected primary liver tumors, and constructed SgME maps of metabolic regions (MERs) associated with key histopathological features. We used these maps to survey IntraTH and train regression models that accurately predict the MER compositions of bulk tumor samples. We also discovered a group of putative metabolites that increase in low-grade tumor regions but abruptly decrease in necrotic regions. SgME profiling may also be applied to heterogeneous tissues from other cancer types or metabolic diseases and provide systems-level understandings of the roles of local cellular niches in cancer metabolism and tumorigenesis.
Project description:This is a prospective, multi-centered study to assess whether urine metabolomics can play a role in the screening of colorectal cancer (CRC). Urine samples will be collected from 1000 patients going through an established CRC screening program, and from a further 500 patients who already have a diagnosis of CRC. Using nuclear magnetic resonance (NMR) spectroscopy, the 1H NMR spectrum of urine samples will be analyzed for specific metabolites, and establish the metabolomic signature of colorectal cancer. The results from metabolomic urinalysis of this screening cohort will be compared with results from colonoscopy, histological descriptions, fecal occult blood testing (FOBT), and fecal immune testing (FIT) to assess the accuracy of urine metabolomics in identifying patients with polyps and malignancies. The urine metabolomic results from the colorectal cancer group will be correlated with operative, histological and clinical staging to define the role of urine metabolomics in assessing colorectal cancer type, location and stage. Additionally approximately 300 urine samples from breast cancer patients and 300 from prostate cancer patients will be collected to validate that the colorectal cancer signature is unique.
Project description:Metabolomics provides a direct functional readout of a tumor’s physiology. Yet, it is lagging behind other omics technologies in facilitating disease monitoring and prognostication. This stems partly from the scarcity of large-scale metabolomic studies, but also the analytical complexities of detecting diverse metabolites with varying physicochemical properties and concentrations. To address this, we developed a machine learning framework using both tumor tissue and cell line samples across multiple cancer types that allows prediction of metabolomics from gene expression data. Two different model types were selected and trained for tissues and cell lines with their generalization capacity validated on independent cohorts, accurately predicting as high as 70-80% of tested metabolites. This work offers a scalable and efficient machine learning pipeline to determine metabolic from transcriptomic signatures, opening avenues to reconstruct and study the metabolic landscape of samples across novel and existing datasets lacking direct metabolomics measurements.
Project description:Colorectal cancer (CRC) is the third most commonly diagnosed cancer in American men and women with ≥130,000 new cases each year. Several dietary patterns have been associated with CRC risk but underlying mechanisms are not fully understood. Researchers thus propose to integrate dietary patterns and metabolomics data to comprehensively investigate biological pathways linking dietary patterns and CRC risk.