Browsing by Subject "Mass spectrometry (MS)"

Now showing 1 - 5 of 5
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    Canine-Inspired Chemometric Analysis of Volatile Organic Compounds in Urine Headspace to Distinguish Prostate Cancer in Mice and Men
    (MDPI, 2023-02-20) Woollam, Mark; Siegel, Amanda P.; Munshi, Adam; Liu, Shengzhi; Tholpady, Sunil; Gardner, Thomas; Li, Bai-Yan; Yokota, Hiroki; Agarwal, Mangilal; Chemistry and Chemical Biology, School of Science
    Canines can identify prostate cancer with high accuracy by smelling volatile organic compounds (VOCs) in urine. Previous studies have identified VOC biomarkers for prostate cancer utilizing solid phase microextraction (SPME) gas chromatography-mass spectrometry (GC-MS) but have not assessed the ability of VOCs to distinguish aggressive cancers. Additionally, previous investigations have utilized murine models to identify biomarkers but have not determined if the results are translatable to humans. To address these challenges, urine was collected from mice with prostate cancer and men undergoing prostate cancer biopsy and VOCs were analyzed by SPME GC-MS. Prior to analysis, SPME fibers/arrows were compared, and the fibers had enhanced sensitivity toward VOCs with a low molecular weight. The analysis of mouse urine demonstrated that VOCs could distinguish tumor-bearing mice with 100% accuracy. Linear discriminant analysis of six VOCs in human urine distinguished prostate cancer with sensitivity = 75% and specificity = 69%. Another panel of seven VOCs could classify aggressive cancer with sensitivity = 78% and specificity = 85%. These results show that VOCs have moderate accuracy in detecting prostate cancer and a superior ability to stratify aggressive tumors. Furthermore, the overlap in the structure of VOCs identified in humans and mice shows the merit of murine models for identifying biomarker candidates.
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    Combining NMR and LC/MS Using Backward Variable Elimination: Metabolomics Analysis of Colorectal Cancer, Polyps, and Healthy Controls
    (ACS Publications, 2016-08-16) Deng, Lingli; Gu, Haiwei; Zhu, Jiangjiang; Gowda, G. A. Nagana; Djukovic, Danijel; Chiorean, Gabriela; Raftery, Daniel; Department of Medicine, School of Medicine
    Both nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) play important roles in metabolomics. The complementary features of NMR and MS make their combination very attractive; however, currently the vast majority of metabolomics studies use either NMR or MS separately, and variable selection that combines NMR and MS for biomarker identification and statistical modeling is still not well developed. In this study focused on methodology, we developed a backward variable elimination partial least-squares discriminant analysis algorithm embedded with Monte Carlo cross validation (MCCV-BVE-PLSDA), to combine NMR and targeted liquid chromatography (LC)/MS data. Using the metabolomics analysis of serum for the detection of colorectal cancer (CRC) and polyps as an example, we demonstrate that variable selection is vitally important in combining NMR and MS data. The combined approach was better than using NMR or LC/MS data alone in providing significantly improved predictive accuracy in all the pairwise comparisons among CRC, polyps, and healthy controls. Using this approach, we selected a subset of metabolites responsible for the improved separation for each pairwise comparison, and we achieved a comprehensive profile of altered metabolite levels, including those in glycolysis, the TCA cycle, amino acid metabolism, and other pathways that were related to CRC and polyps. MCCV-BVE-PLSDA is straightforward, easy to implement, and highly useful for studying the contribution of each individual variable to multivariate statistical models. On the basis of these results, we recommend using an appropriate variable selection step, such as MCCV-BVE-PLSDA, when analyzing data from multiple analytical platforms to obtain improved statistical performance and a more accurate biological interpretation, especially for biomarker discovery. Importantly, the approach described here is relatively universal and can be easily expanded for combination with other analytical technologies.
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    Detection of lipid-induced structural changes of the Marburg virus matrix protein VP40 using hydrogen/deuterium exchange-mass spectrometry
    (American Society for Biochemistry and Molecular Biology, 2017-04-14) Wijesinghe, Kaveesha J.; Urata, Sarah; Bhattarai, Nisha; Kooijman, Edgar E.; Gerstman, Bernard S.; Chapagain, Prem P.; Li, Sheng; Stahelin, Robert V.; Biochemistry and Molecular Biology, School of Medicine
    Marburg virus (MARV) is a lipid-enveloped virus from the Filoviridae family containing a negative sense RNA genome. One of the seven MARV genes encodes the matrix protein VP40, which forms a matrix layer beneath the plasma membrane inner leaflet to facilitate budding from the host cell. MARV VP40 (mVP40) has been shown to be a dimeric peripheral protein with a broad and flat basic surface that can associate with anionic phospholipids such as phosphatidylserine. Although a number of mVP40 cationic residues have been shown to facilitate binding to membranes containing anionic lipids, much less is known on how mVP40 assembles to form the matrix layer following membrane binding. Here we have used hydrogen/deuterium exchange (HDX) mass spectrometry to determine the solvent accessibility of mVP40 residues in the absence and presence of phosphatidylserine and phosphatidylinositol 4,5-bisphosphate. HDX analysis demonstrates that two basic loops in the mVP40 C-terminal domain make important contributions to anionic membrane binding and also reveals a potential oligomerization interface in the C-terminal domain as well as a conserved oligomerization interface in the mVP40 N-terminal domain. Lipid binding assays confirm the role of the two basic patches elucidated with HD/X measurements, whereas molecular dynamics simulations and membrane insertion measurements complement these studies to demonstrate that mVP40 does not appreciably insert into the hydrocarbon region of anionic membranes in contrast to the matrix protein from Ebola virus. Taken together, we propose a model by which association of the mVP40 dimer with the anionic plasma membrane facilitates assembly of mVP40 oligomers.
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    Role of Novel Serine 316 Phosphorylation of the p65 Subunit of NF-κB in Differential Gene Regulation
    (American Society for Biochemistry & Molecular Biology, 2015-06-16) Wang, Benlian; Prabhu, Lakshmi; Zhao, Wei; Martin, Matthew; Hartley, Antja-Voy; Lu, Tao; Wei, Han; Department of Pharmacology and Toxicology, IU School of Medicine
    Nuclear factor κB (NF-κB) is a central coordinator in immune and inflammatory responses. Constitutive NF-κB is often found in some types of cancers, contributing to oncogenesis and tumor progression. Therefore, knowing how NF-κB is regulated is important for its therapeutic control. Post-translational modification of the p65 subunit of NF-κB is a well known approach for its regulation. Here, we reported that in response to interleukin 1β, the p65 subunit of NF-κB is phosphorylated on the novel serine 316. Overexpression of S316A (serine 316 → alanine) mutant exhibited significantly reduced ability to activate NF-κB and decreased cell growth as compared with wtp65 (wild type p65). Moreover, conditioned media from cells expressing the S316A-p65 mutant had a considerably lower ability to induce NF-κB than that of wtp65. Our data suggested that phosphorylation of p65 on Ser-316 controls the activity and function of NF-κB. Importantly, we found that phosphorylation at the novel Ser-316 site and other two known phosphorylation sites, Ser-529 and Ser-536, either individually or cooperatively, regulated distinct groups of NF-κB-dependent genes, suggesting the unique role of each individual phosphorylation site on NF-κB-dependent gene regulation. Our novel findings provide an important piece of evidence regarding differential regulation of NF-κB-dependent genes through phosphorylation of different p65 serine residues, thus shedding light on novel mechanisms for the pathway-specific control of NF-κB. This knowledge is key to develop strategies for prevention and treatment of constitutive NF-κB-driven inflammatory diseases and cancers.
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    Tracking the Progression of Triple Negative Mammary Tumors over Time by Chemometric Analysis of Urinary Volatile Organic Compounds
    (Cancers, 2021-01) Woollam, Mark; Wang, Luqi; Grocki, Paul; Liu, Shengzhi; Siegel, Amanda P.; Kalra, Maitri; Goodpaster, John V.; Yokota, Hiroki; Agarwal, Mangilal
    Previous studies have shown that volatile organic compounds (VOCs) are potential biomarkers of breast cancer. An unanswered question is how urinary VOCs change over time as tumors progress. To explore this, BALB/c mice were injected with 4T1.2 triple negative murine tumor cells in the tibia. This typically causes tumor progression and osteolysis in 1–2 weeks. Samples were collected prior to tumor injection and from days 2–19. Samples were analyzed by headspace solid phase microextraction coupled to gas chromatography–mass spectrometry. Univariate analysis identified VOCs that were biomarkers for breast cancer; some of these varied significantly over time and others did not. Principal component analysis was used to distinguish Cancer (all Weeks) from Control and Cancer Week 1 from Cancer Week 3 with over 90% accuracy. Forward feature selection and linear discriminant analysis identified a unique panel that could identify tumor presence with 94% accuracy and distinguish progression (Cancer Week 1 from Cancer Week 3) with 97% accuracy. Principal component regression analysis also demonstrated that a VOC panel could predict number of days since tumor injection (R2 = 0.71 and adjusted R2 = 0.63). VOC biomarkers identified by these analyses were associated with metabolic pathways relevant to breast cancer.