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22 results on '"Raskind, Alexander"'

4. An IROA Workflow for correction and normalization of ion suppression in mass spectrometry-based metabolomic profiling data

Author

Mahmud, Iqbal, primary, Wei, Bo, additional, Veillon, Lucas, additional, Tan, Lin, additional, Martinez, Sara, additional, Tran, Bao, additional, Raskind, Alexander, additional, de Jong, Felice, additional, Akbani, Rehan, additional, Weinstein, John N., additional, Beecher, Chris, additional, and Lorenzi, Philip L., additional

Published
2024
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5. Offline Two-Dimensional Liquid Chromatography–Mass Spectrometry for Deep Annotation of the Fecal Metabolome Following Fecal Microbiota Transplantation

Author

Anderson, Brady G., Raskind, Alexander, Hissong, Rylan, Dougherty, Michael K., McGill, Sarah K., Gulati, Ajay S., Theriot, Casey M., Kennedy, Robert T., and Evans, Charles R.

Abstract

Biological interpretation of untargeted LC-MS-based metabolomics data depends on accurate compound identification, but current techniques fall short of identifying most features that can be detected. The human fecal metabolome is complex, variable, incompletely annotated, and serves as an ideal matrix to evaluate novel compound identification methods. We devised an experimental strategy for compound annotation using multidimensional chromatography and semiautomated feature alignment and applied these methods to study the fecal metabolome in the context of fecal microbiota transplantation (FMT) for recurrent C. difficileinfection. Pooled fecal samples were fractionated using semipreparative liquid chromatography and analyzed by an orthogonal LC-MS/MS method. The resulting spectra were searched against commercial, public, and local spectral libraries, and annotations were vetted using retention time alignment and prediction. Multidimensional chromatography yielded more than a 2-fold improvement in identified compounds compared to conventional LC-MS/MS and successfully identified several rare and previously unreported compounds, including novel fatty-acid conjugated bile acid species. Using an automated software-based feature alignment strategy, most metabolites identified by the new approach could be matched to features that were detected but not identified in single-dimensional LC-MS/MS data. Overall, our approach represents a powerful strategy to enhance compound identification and biological insight from untargeted metabolomics data.

Published
2024
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8. Offline Two-dimensional Liquid Chromatography-Mass Spectrometry for Deep Annotation of the Fecal Metabolome

Author

Anderson, Brady G., Raskind, Alexander, Hissong, Rylan, Dougherty, Michael K., McGill, Sarah K., Gulati, Ajay, Theriot, Casey M., Kennedy, Robert T., and Evans, Charles R.

Subjects
Article
Abstract

Compound identification is an essential task in the workflow of untargeted metabolomics since the interpretation of the data in a biological context depends on the correct assignment of chemical identities to the features it contains. Current techniques fall short of identifying all or even most observable features in untargeted metabolomics data, even after rigorous data cleaning approaches to remove degenerate features are applied. Hence, new strategies are required to annotate the metabolome more deeply and accurately. The human fecal metabolome, which is the focus of substantial biomedical interest, is a more complex, more variable, yet lesser-investigated sample matrix compared to widely studied sample types like human plasma. This manuscript describes a novel experimental strategy using multidimensional chromatography to facilitate compound identification in untargeted metabolomics. Pooled fecal metabolite extract samples were fractionated using offline semi-preparative liquid chromatography. The resulting fractions were analyzed by an orthogonal LC-MS/MS method, and the data were searched against commercial, public, and local spectral libraries. Multidimensional chromatography yielded more than a 3-fold improvement in identified compounds compared to the typical single-dimensional LC-MS/MS approach and successfully identified several rare and novel compounds, including atypical conjugated bile acid species. Most features identified by the new approach could be matched to features that were detectable but not identifiable in the original single-dimension LC-MS data. Overall, our approach represents a powerful strategy for deeper annotation of the metabolome that can be implemented with commercially-available instrumentation, and should apply to any dataset requiring deeper annotation of the metabolome.

Published
2023

9. Integrated phosphoproteomic and metabolomic profiling reveals NPM-ALK–mediated phosphorylation of PKM2 and metabolic reprogramming in anaplastic large cell lymphoma

Author

McDonnell, Scott R.P., Hwang, Steven R., Rolland, Delphine, Murga-Zamalloa, Carlos, Basrur, Venkatesha, Conlon, Kevin P., Fermin, Damian, Wolfe, Thomas, Raskind, Alexander, Ruan, Chunhai, Jiang, Jian-Kang, Thomas, Craig J., Hogaboam, Cory M., Burant, Charles F., Elenitoba-Johnson, Kojo S.J., and Lim, Megan S.

Published
2013
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12. Expression in multigene families. Analysis of chloroplast and mitochondrial proteases (1)

Author

Sinvany-Villalobo, Galit, Davydov, Olga, Ben-Ari, Giora, Zaltsman, Adi, Raskind, Alexander, and Adam, Zach

Subjects
Adenosine triphosphatase -- Research, Arabidopsis thaliana -- Research, Arabidopsis thaliana -- Genetic aspects, Gene expression -- Research, Mitochondria -- Research, Biological sciences, Science and technology
Published
2004

13. Molecular Transducers of Physical Activity Consortium (MoTrPAC): Mapping the Dynamic Responses to Exercise

Author

Sanford, James A., primary, Nogiec, Christopher D., additional, Lindholm, Malene E., additional, Adkins, Joshua N., additional, Amar, David, additional, Dasari, Surendra, additional, Drugan, Jonelle K., additional, Fernández, Facundo M., additional, Radom-Aizik, Shlomit, additional, Schenk, Simon, additional, Snyder, Michael P., additional, Tracy, Russell P., additional, Vanderboom, Patrick, additional, Trappe, Scott, additional, Walsh, Martin J., additional, Evans, Charles R., additional, Fernandez, Facundo M., additional, Li, Yafeng, additional, Sanford, James A., additional, Tomlinson, Lyl, additional, Lee Alekel, D., additional, Bekirov, Iddil, additional, Boyce, Amanda T., additional, Boyington, Josephine, additional, Fleg, Jerome L., additional, Joseph, Lyndon J.O., additional, Laughlin, Maren R., additional, Maruvada, Padma, additional, Morris, Stephanie A., additional, McGowan, Joan A., additional, Nierras, Concepcion, additional, Pai, Vinay, additional, Peterson, Charlotte, additional, Ramos, Ed, additional, Roary, Mary C., additional, Williams, John P., additional, Xia, Ashley, additional, Cornell, Elaine, additional, Rooney, Jessica, additional, Miller, Michael E., additional, Ambrosius, Walter T., additional, Rushing, Scott, additional, Stowe, Cynthia L., additional, Jack Rejeski, W., additional, Nicklas, Barbara J., additional, Pahor, Marco, additional, Lu, Ching-ju, additional, Trappe, Todd, additional, Chambers, Toby, additional, Raue, Ulrika, additional, Lester, Bridget, additional, Bergman, Bryan C., additional, Bessesen, David H., additional, Jankowski, Catherine M., additional, Kohrt, Wendy M., additional, Melanson, Edward L., additional, Moreau, Kerrie L., additional, Schauer, Irene E., additional, Schwartz, Robert S., additional, Kraus, William E., additional, Slentz, Cris A., additional, Huffman, Kim M., additional, Johnson, Johanna L., additional, Willis, Leslie H., additional, Kelly, Leslie, additional, Houmard, Joseph A., additional, Dubis, Gabriel, additional, Broskey, Nick, additional, Goodpaster, Bret H., additional, Sparks, Lauren M., additional, Coen, Paul M., additional, Cooper, Dan M., additional, Haddad, Fadia, additional, Rankinen, Tuomo, additional, Ravussin, Eric, additional, Johannsen, Neil, additional, Harris, Melissa, additional, Jakicic, John M., additional, Newman, Anne B., additional, Forman, Daniel D., additional, Kershaw, Erin, additional, Rogers, Renee J., additional, Nindl, Bradley C., additional, Page, Lindsay C., additional, Stefanovic-Racic, Maja, additional, Barr, Susan L., additional, Rasmussen, Blake B., additional, Moro, Tatiana, additional, Paddon-Jones, Doug, additional, Volpi, Elena, additional, Spratt, Heidi, additional, Musi, Nicolas, additional, Espinoza, Sara, additional, Patel, Darpan, additional, Serra, Monica, additional, Gelfond, Jonathan, additional, Burns, Aisling, additional, Bamman, Marcas M., additional, Buford, Thomas W., additional, Cutter, Gary R., additional, Bodine, Sue C., additional, Esser, Karyn, additional, Farrar, Rodger P., additional, Goodyear, Laurie J., additional, Hirshman, Michael F., additional, Albertson, Brent G., additional, Qian, Wei-Jun, additional, Piehowski, Paul, additional, Gritsenko, Marina A., additional, Monore, Matthew E., additional, Petyuk, Vladislav A., additional, McDermott, Jason E., additional, Hansen, Joshua N., additional, Hutchison, Chelsea, additional, Moore, Samuel, additional, Gaul, David A., additional, Clish, Clary B., additional, Avila-Pacheco, Julian, additional, Dennis, Courtney, additional, Kellis, Manolis, additional, Carr, Steve, additional, Jean-Beltran, Pierre M., additional, Keshishian, Hasmik, additional, Mani, D.R., additional, Clauser, Karl, additional, Krug, Karsten, additional, Mundorff, Charlie, additional, Pearce, Cadence, additional, Ivanova, Anna A., additional, Ortlund, Eric A., additional, Maner-Smith, Kristal, additional, Uppal, Karan, additional, Zhang, Tiantian, additional, Sealfon, Stuart C., additional, Zaslavsky, Elena, additional, Nair, Venugopalan, additional, Li, SiDe, additional, Jain, Nimisha, additional, Ge, YongChao, additional, Sun, Yifei, additional, Nudelman, German, additional, Ruf-zamojski, Frederique, additional, Smith, Gregory, additional, Pincas, Nhanna, additional, Rubenstein, Aliza, additional, Anne Amper, Mary, additional, Seenarine, Nitish, additional, Lappalainen, Tuuli, additional, Lanza, Ian R., additional, Sreekumaran Nair, K., additional, Klaus, Katherine, additional, Montgomery, Stephen B., additional, Smith, Kevin S., additional, Gay, Nicole R., additional, Zhao, Bingqing, additional, Hung, Chia-Jiu, additional, Zebarjadi, Navid, additional, Balliu, Brunilda, additional, Fresard, Laure, additional, Burant, Charles F., additional, Li, Jun Z., additional, Kachman, Maureen, additional, Soni, Tanu, additional, Raskind, Alexander B., additional, Gerszten, Robert, additional, Robbins, Jeremy, additional, Ilkayeva, Olga, additional, Muehlbauer, Michael J., additional, Newgard, Christopher B., additional, Ashley, Euan A., additional, Wheeler, Matthew T., additional, Jimenez-Morales, David, additional, Raja, Archana, additional, Dalton, Karen P., additional, Zhen, Jimmy, additional, Suk Kim, Young, additional, Christle, Jeffrey W., additional, Marwaha, Shruti, additional, Chin, Elizabeth T., additional, Hershman, Steven G., additional, Hastie, Trevor, additional, Tibshirani, Robert, additional, and Rivas, Manuel A., additional

Published
2020
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18. Expression of the barley psbA gene in Escherichia coliyields a functional in vitro photosystem II protein D1

Author

Efimov, Vladimir A., Fradkov, Arkady F., Raskind, Alexander B., Khristin, Michael S., Klimov, Vyacheslav V., and Chakhmakhcheva, Oksana G.

Abstract

The barley chloroplast psbA gene encoding D1 protein, one of the main photosystem II components, has been over-expressed in E. colicells. The existance of two in vivo expression products, a protein with Mrabout 33.5 kDa, corresponding to the full-length precursor of the 32 kDa D1 mature form, and a truncated 29 kDa polypeptide was revealed. A modified D1 protein containing six histidine residues at the carboxy-terminus was also obtained. After isolation and renaturation, the ability of the recombinant D1 protein to bind atrazine and pigments from barley thylakoids was demonstrated.

Published
1994
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19. An IROA Workflow for correction and normalization of ion suppression in mass spectrometry-based metabolomic profiling data.

Author

Mahmud I, Wei B, Veillon L, Tan L, Martinez S, Tran B, Raskind A, de Jong F, Akbani R, Weinstein JN, Beecher C, and Lorenzi PL

Abstract

Ion suppression is a major problem in mass spectrometry (MS)-based metabolomics; it can dramatically decrease measurement accuracy, precision, and signal-to-noise sensitivity. Here we report a new method, the IROA TruQuant Workflow, that uses a stable isotope-labeled internal standard (IROA-IS) plus novel companion algorithms to 1) measure and correct for ion suppression, and 2) perform Dual MSTUS normalization of MS metabolomic data. We have evaluated the method across ion chromatography (IC), hydrophilic interaction liquid chromatography (HILIC), and reverse phase liquid chromatography (RPLC)-MS systems in both positive and negative ionization modes, with clean and unclean ion sources, and across different biological matrices. Across the broad range of conditions tested, all detected metabolites exhibited ion suppression ranging from 1% to 90+% and coefficient of variations ranging from 1% to 20%, but the Workflow and companion algorithms were highly effective at nulling out that suppression and error. Overall, the Workflow corrects ion suppression across diverse analytical conditions and produces robust normalization of non-targeted metabolomic data.

Published
2024
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20. Offline Two-dimensional Liquid Chromatography-Mass Spectrometry for Deep Annotation of the Fecal Metabolome.

Author

Anderson BG, Raskind A, Hissong R, Dougherty MK, McGill SK, Gulati A, Theriot CM, Kennedy RT, and Evans CR

Abstract

Compound identification is an essential task in the workflow of untargeted metabolomics since the interpretation of the data in a biological context depends on the correct assignment of chemical identities to the features it contains. Current techniques fall short of identifying all or even most observable features in untargeted metabolomics data, even after rigorous data cleaning approaches to remove degenerate features are applied. Hence, new strategies are required to annotate the metabolome more deeply and accurately. The human fecal metabolome, which is the focus of substantial biomedical interest, is a more complex, more variable, yet lesser-investigated sample matrix compared to widely studied sample types like human plasma. This manuscript describes a novel experimental strategy using multidimensional chromatography to facilitate compound identification in untargeted metabolomics. Pooled fecal metabolite extract samples were fractionated using offline semi-preparative liquid chromatography. The resulting fractions were analyzed by an orthogonal LC-MS/MS method, and the data were searched against commercial, public, and local spectral libraries. Multidimensional chromatography yielded more than a 3-fold improvement in identified compounds compared to the typical single-dimensional LC-MS/MS approach and successfully identified several rare and novel compounds, including atypical conjugated bile acid species. Most features identified by the new approach could be matched to features that were detectable but not identifiable in the original single-dimension LC-MS data. Overall, our approach represents a powerful strategy for deeper annotation of the metabolome that can be implemented with commercially-available instrumentation, and should apply to any dataset requiring deeper annotation of the metabolome.

Published
2023
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21. Quantitative Metabolomics Using Isotope Residue Outlier Analysis (IROA ® ) with Internal Standards.

Author

Mendez R, Del Carmen Piqueras M, Raskind A, de Jong FA, Beecher C, Bhattacharya SK, and Banerjee S

Subjects
Animals, Chromatography, High Pressure Liquid methods, Chromatography, High Pressure Liquid standards, Liver metabolism, Mass Spectrometry methods, Mass Spectrometry standards, Metabolomics standards, Mice, Reference Standards, Reproducibility of Results, Software, Metabolomics methods, Radioisotopes analysis
Abstract

Various research strategies involving biomarker discovery and mechanistic studies in system biology depend on reproducible and reliable quantification of all metabolites from tissue(s) of interest. Contemporary analytical methods rely on mass spectrometry-based targeted and/or untargeted metabolomics platforms. The robustness of these analyses depends on the cleanliness of the samples, accuracy of the database, resolution of the instrument, and, the most variable of the list, the personal preferences of the researcher and the instrument operator. In this chapter, we introduce a simple method to prepare murine liver samples and carry it through the Isotope Ratio Outlier Analysis (IROA ® ) pipeline. This pipeline encompasses sample preparation, LC-MS-based peak acquisition, proprietary software-based library creation, normalization, and quantification of metabolites. IROA ® offers a unique platform to create and normalize a local library and account for run-to-run variability over years of acquisition using the internal standards (IROA ® -IS) and long-term reference standards (IROA ® -LTRS).

Published
2019
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22. Statistical methods in metabolomics.

Author

Korman A, Oh A, Raskind A, and Banks D

Subjects
Animals, Diagnosis, Disease genetics, Humans, Metabolomics standards, Metabolomics statistics & numerical data, Quality Control, Support Vector Machine, Metabolomics methods, Statistics as Topic methods
Abstract

Metabolomics is the relatively new field in bioinformatics that uses measurements on metabolite abundance as a tool for disease diagnosis and other medical purposes. Although closely related to proteomics, the statistical analysis is potentially simpler since biochemists have significantly more domain knowledge about metabolites. This chapter reviews the challenges that metabolomics poses in the areas of quality control, statistical metrology, and data mining.

Published
2012
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