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1. Pioglitazone Attenuates Acute Cocaine Toxicity in Rat Isolated Heart

Author

Sarah Bern, Guido Digregorio, Lucas B. Edelman, Mariann R. Piano, Douglas L. Feinstein, Bocheng Lin, Guy L. Weinberg, and Richard Ripper

Subjects
medicine.medical_specialty, Lusitropy, business.industry, Carbohydrate metabolism, Pharmacology, Contractility, Anesthesiology and Pain Medicine, medicine.anatomical_structure, Endocrinology, Internal medicine, Toxicity, Respiration, Vascular resistance, Medicine, business, Pioglitazone, Heart metabolism, medicine.drug
Abstract

Background The authors tested whether cocaine depresses mitochondrial acylcarnitine exchange and if a drug that enhances glucose metabolism could protect against cocaine-induced cardiac dysfunction. Methods Oxygen consumption with and without cocaine was compared in rat cardiac mitochondria using octanoylcarnitine (lipid) or pyruvate (nonlipid) substrates. Isolated hearts from rats with or without a pioglitazone-supplemented diet were exposed to cocaine. Results The 0.5 mM cocaine inhibited respiration supported by octanoylcarnitine (82 ± 10.4 and 45.7 ± 4.24 ngatomO min⁻¹ · mg⁻¹ · protein ± SEM, for control and cocaine treatment, respectively; P < 0.02) but not pyruvate-supported respiration (281 ± 12.5 and 267 ± 12.7 ngatomO min⁻¹ · mg⁻¹ · protein ± SEM; P = 0.45). Cocaine altered contractility, lusitropy, coronary resistance, and lactate production in isolated heart. These effects were each blunted in pioglitazone-treated hearts. The pioglitazone diet attenuated the drop in the rate-pressure product (P = 0.002), cocaine-induced diastolic dysfunction (P = 0.04), and myocardial vascular resistance (P = 0.05) compared with that of controls. Lactate production was higher in pretreated hearts (P = 0.008) and in ventricular myocytes cultured with pioglitazone (P = 0.0001). Conclusions Cocaine inhibited octanoylcarnitine-supported mitochondrial respiration. A pioglitazone diet significantly attenuated the effects of cocaine on isolated heart. The authors postulate that inhibition of acylcarnitine exchange could contribute to cocaine-induced cardiac dysfunction and that metabolic modulation warrants additional study.

Published
2011
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3. Contributors

Author

Michael J. Ackerman, Minhaz Uddin Ahmed, Sun Hee Ahn, R. Rand Allingham, Jude Alsarraj, Matthew L. Anderson, Brian H. Annex, Christina K. Augustine, Sergio E. Baranzini, Peter J. Barnes, Georgia M. Beasley, Richard C. Becker, Ivor J. Benjamin, D. Woodrow Benson, Jacob Fog Bentzon, Philip S. Bernard, Lars Bertram, Laura M. Beskow, Brigitta Bondy, J. Martijn Bos, Gabriella L. Boulting, Jerome S. Brody, April S. Brown, Kenneth H. Buetow, Lars Bullinger, Atul J. Butte, David J. Carey, George Carlson, David Chen, Maria Chahrour, E. Antonio Chiocca, Alex H. Cho, Carolyn Compton, Robert Cook-Deegan, Nicholas Craddock, Michael Coulter, Samir B. Damani, Jacqueline S. Danik, Sandeep Dave, Russell Dedrick, Frank R. DeLeo, Eleftherios P. Diamandis, Ayotunde O. Dokun, Gregory J. Downing, Stephen L. Eck, Lucas B. Edelman, Kevin C. Eggan, Erling Falk, Hawazin Faruki, Junjie Feng, Eleanora Anna Margaretha Festen, Jose C. Florez, Vance G. Fowler, Jennifer A. Freedman, Hudson H. Freeze, Stefan Fröhling, David J. Galas, Glenn S. Gerhard, Daniel H. Geschwind, Ad Geurts van Kessel, Robert B. Giffin, Geoffrey S. Ginsburg, Megan Goodall, Praveen Govender, Robert C. Green, Chris Gunter, Marta Gwinn, Susanne B. Haga, James R. Heath, Robert A. Hegele, John Holton, Leroy Hood, Kent W. Hunter, Daehee Hwang, Gail Javitt, Gina Jefferson, Sam John, Keith J. Johnson, Toby Johnson, Tisha R. Joy, Sekar Kathiresan, Hasmeena Kathuria, Arabindra B. Katwal, Kensaku Kawamoto, Andrea Kelly, Muin J. Khoury, Jin Woo Kim, Stephen F. Kingsmore, Tod Klingler, Scott D. Kobayashi, Isaac S. Kohane, Virginia Byers Kraus, Roland P. Kuiper, Myla Lai-Goldman, Neil E. Lamb, Sevdalina Nikolova Lambova, Matthew B. Lanktree, Sean E. Lawler, David H. Ledbetter, Charles Lee, Inyoul Lee, Karine G. Le Roch, Samuel Levy, Sophie Limou, Wennuan Liu, Yutao Liu, Barry London, Jeffrey P. MacKeigan, Elaine R. Mardis, Hector Marquez, Marco Marra, Christa Lese Martin, Lisa J. Martin, Steven A. McCarroll, John McHutchison, Howard L. McLeod, Asunción Mejías, Eric B. Meltzer, M. Anthony Moody, Ryan D. Morin, M. Arthur Moseley, Joanna L. Mountain, Ulf Müller-Ladner, Patricia B. Munroe, Bobby G. Ng, Paul W. Noble, Michael C. O’Donovan, Kunle Odunsi, Gilbert S. Omenn, Steve R. Ommen, Lori A. Orlando, Albert D.M.E. Osterhaus, Michael J. Owen, Neelroop N. Parikshak, Keyur Patel, Maria P. Pavlou, Nina P. Paynter, Tanja Pejovic, Michael X. Pham, Robert M. Plenge, Mihai V. Podgoreanu, Nadia Ponts, Nathan D. Price, Bruce Quinn, Francisco J. Quintana, Daniel J. Rader, Octavio Ramilo, Heidi L. Rehm, Benjamin Rhodes, Paul M. Ridker, Erin Rooney Riggs, Jeffrey S. Ross, Wendy S. Rubinstein, Carol J. Saunders, Joan Scott, Nicholas A. Shackel, Svati H. Shah, Richard Simon, Sanjay M. Sisodiya, Saskia L. Smits, John A. Stamatoyannopoulos, Glenn D. Steele, Mark P. Steele, Tessandra Stewart, Giovana R. Thomas, Eric J. Topol, Sean R. Tunis, Aubrey R. Turner, Douglas S. Tyler, David L. Veenstra, Ramprasath Venkatachalam, Deepak Voora, Timothy J. Vyse, Christopher A. Walsh, Katherine Walsh, Hao Wang, Jun Wang, Rinse K. Weersma, Howard L. Weiner, Scott T. Weiss, C. William Wester, David N. Wholley, Janey L. Wiggs, Cisca Wijmenga, Huntington F. Willard, Hywel J. Williams, Marc S. Williams, Cheryl A. Winkler, Janet Woodcock, Christopher W. Woods, Pae C. Wu, Jianfeng Xu, James Yee, Hyuntae Yoo, Timothy Yu, Jing Zhang, and Yurong Zhang

Published
2013
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4. Systems Biology of Embryogenesis

Author

Nathan D. Price, Sriram Chandrasekaran, and Lucas B. Edelman

Subjects
Systems biology, Organogenesis, Zoology, Embryonic Development, Reproductive technology, Biology, Models, Biological, Article, Endocrinology, Genetics, Animals, Humans, Computer Simulation, Orchestration (computing), Complex adaptive system, Molecular Biology, Organism, Cognitive science, Computational model, Systems Biology, Systems approaches, Reproductive Medicine, Animal Science and Zoology, Developmental biology, Developmental Biology, Biotechnology
Abstract

The development of a complete organism from a single cell involves extraordinarily complex orchestration of biological processes that vary intricately across space and time. Systems biology seeks to describe how all elements of a biological system interact in order to understand, model and ultimately predict aspects of emergent biological processes. Embryogenesis represents an extraordinary opportunity (and challenge) for the application of systems biology. Systems approaches have already been used successfully to study various aspects of development, from complex intracellular networks to four-dimensional models of organogenesis. Going forward, great advancements and discoveries can be expected from systems approaches applied to embryogenesis and developmental biology.

Published
2010

5. In silico models of cancer

Author

Lucas B. Edelman, Nathan D. Price, and James A. Eddy

Subjects
Biological data, Computational model, Models, Statistical, In silico, Complex disease, Medicine (miscellaneous), Computational Biology, Systems approaches, Computational biology, Overfitting, Biology, Bioinformatics, Biochemistry, Genetics and Molecular Biology (miscellaneous), Models, Biological, Article, Tumor progression, Neoplasms, Animals, Humans, Computer Simulation, Cancer biology
Abstract

Cancer is a complex disease that involves multiple types of biological interactions across diverse physical, temporal, and biological scales. This complexity presents substantial challenges for the characterization of cancer biology, and motivates the study of cancer in the context of molecular, cellular, and physiological systems. Computational models of cancer are being developed to aid both biological discovery and clinical medicine. The development of these in silico models is facilitated by rapidly advancing experimental and analytical tools that generate information-rich, high-throughput biological data. Statistical models of cancer at the genomic, transcriptomic, and pathway levels have proven effective in developing diagnostic and prognostic molecular signatures, as well as in identifying perturbed pathways. Statistically-inferred network models can prove useful in settings where data overfitting can be avoided, and provide an important means for biological discovery. Mechanistically-based signaling and metabolic models that apply a priori knowledge of biochemical processes derived from experiments can also be reconstructed where data are available, and can provide insight and predictive ability regarding the dynamical behavior of these systems. At longer length scales, continuum and agent-based models of the tumor microenvironment and other tissue-level interactions enable modeling of cancer cell populations and tumor progression. Even though cancer has been among the most-studied human diseases using systems approaches, significant challenges remain before the enormous potential of in silico cancer biology can be fully realized.

Published
2010

6. Systems Biology and Systems Medicine

Author

Nathan D. Price, George A. Carlson, Hyuntae Yoo, Daehee Hwang, Lucas B. Edelman, Leroy Hood, David Galas, James R. Heath, and Inyoul Lee

Subjects
Systems medicine, Computer science, Systems biology, Gene regulatory network, Computational biology, Genome, Gene, Protein network, Molecular machine, Biological network
Abstract

Publisher Summary A systems approach to medicine argues that disease arises from disease-perturbed biological networks and the dynamically changing, altered patterns of gene expression controlled by these perturbed networks give rise to the disease manifestations. This chapter presents a systems view of biology and disease, and recent advances in state-of-the-art in vitro and in vivo diagnostics technologies. As these technologies mature, they will move towards a future of predictive, personalized, preventive, and participatory medicine. Two primary domains of biological information lend themselves readily to systems-level analysis: the static, digital information of the genome, and the dynamic information arising from environmental interactions with the subcellular, cellular, and tissue levels of organization. Digital genome information encodes two types of biological networks–protein interactions and gene regulatory networks. Protein networks transmit biological information for development, physiology, and metabolism. Other RNAs interacting with one another receive information from signal-transduction networks, integrate and modulate it, and convey the processed information to networks of genes or molecular machines that execute developmental and physiological functions.

Published
2010
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7. Systems Biology and the Emergence of Systems Medicine

Author

Inyoul Lee, George A. Carlson, Daehee Hwang, David Galas, Leroy Hood, Lucas B. Edelman, Nathan D. Price, Hyuntae Yoo, and James R. Heath

Subjects
Systems medicine, GEORGE (programming language), Systems biology, Library science, Computational biology
Abstract

Systems Biology and the Emergence of Systems Medicine Nathan D. Price, Lucas B. Edelman, Inyoul Lee, Hyuntae Yoo, Daehee Hwang, George Carlson, David J. Galas, James R. Heath, and Leroy Hood Department of Chemical and Biomolecular Engineering, Department of Bioengineering, & Institute for Genomic Biology, University of Illinois, Urbana-Champaign 4 Institute for Systems Biology, Seattle, WA I-Bio Program and Department of Chemical Engineering, Pohang University of Science and Technology, Pohang, Korea McLaughlin Research Institute, Great Falls, MT Battelle Memorial Institute, Columbus, OH Department of Chemistry, California Institute of Technology To whom correspondence should be addressed: lhood@systemsbiology.org Prepared for Genomic and Personalized Medicine: From Principles to Practice (Ginsburg, G. and Willard, H., editors). Elsevier, in press.

Published
2009
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8. Contributors

Author

Michael J. Ackerman, Matthew L. Anderson, Felicita Andreotti, Brian H. Annex, Anthony Antonellis, Dmitri Artemov, James R. Bain, Peter J. Barnes, J.S. Barnholtz-Sloan, Richard C. Becker, Ivor J. Benjamin, Paul R. Billings, Simon C. Body, Mark Boguski, Stefano Bonassi, Brigitta Bondy, J. Martijn Bos, Roger E. Breitbart, Jerome Brody, Matthew P. Brown, Lars Bullinger, Shawn C. Burgess, Atul J. Butte, David J. Carey, George Carlson, Juan C. Celedón, Subhashini Chandrasekharan, Wing C. (John) Chang, Yan Chen, Antonio Chiocca, Theresa Puifun Chow, Wendy K. Chung, Robert Cook-Deegan, Dolores Corella, Susan Cottrell, Nancy J. Cox, Nigel P.S. Crawford, A. Jamie Cuticchia, Dieter Deforce, Mario C. Deng, Gayathri Devi, Theo deVos, Juergen Distler, Harmut Dohner, Ayotunde O. Dokun, Mark R. Edbrooke, Lucas B. Edelman, Dirk Elewaut, Charles J. Epstein, Ayman H. Fanous, Phillip G. Febbo, Robert J. Feezor, Yonmei Feng, Zeno Földes-Papp, Hidehiko Fujinaka, David J. Galas, Louis, P. Garrison, Glenn S. Gerhard, Geoffrey S. Ginsburg, Bjorn T. Gjertsen, Michael Glass, David B. Goldstein, Erynn S. Gordon, Tucker Gosnell, Peter Grass, Eric D. Green, Iris Grossman, Marta Gwinn, Carolina Haefliger, Susanne B. Haga, Per Hall, Joshua M. Hare, Ahmad Hariri, James R. Heath, Robert A. Hegele, Bettina Heidecker, Shona Hislop, Eric P. Hoffman, John Holton, Leroy Hood, Andrew T. Huang, Erich S. Huang, Carlos A. Hubbard, Kent Hunter, Courtney Hyland, Chunhwa Ihm, Olga Ilkayeva, Rafael Irizarry, Shushant Jain, Melissa D. Johnson, Philip W. Kantoff, Sekar Kathiresan, Hasmeena Kathuria, Kensaku Kawamoto, Filip De Keyser, Asif Khalid, Muin Khoury, Stephen F. Kingsmore, Michelle M. Kittleson, Beena T. Koshy, Ranga Krishnan, Robert S. Krouse, Vikas Kundra, Myla Lai-Goldman, Vipul Lakhani, Robert Langer, Sean E. Lawler, Inyoul Lee, Charles Lee, Arthur S. Lee, Stanely Leong, Ralf Lesche, Samuel Levy, Edison T. Liu, David F. Lobach, James B. Lorens, Aldons J. Lusis, H. Kim Lyerly, Nageswara R. Madamanchi, J. Alfredo Martinez, Vega Masignani, Kevin McGrath, John McHutchison, Frank Middleton, Herman Mielants, Michael J. Muehlbauer, Ulf Müller-Ladner, Mark A. Nelson, Monica Neri, Mihai Netea, L. Kristin Newby, Christopher B. Newgard, Maggie Ng, Paul W. Noble, Vasilis Ntziachristos, Robert L. Nussbaum, Meghan B. O'Donoghue, Kunle Odunsi, Kenneth Olden, Steve R. Ommen, Jose M. Ordovas, Roberto Patarca, Jeetendra Patel, K. Patel, Carlos N. Pato, Michele T. Pato, Tanja Pejovic, Noah C. Perin, Michael Pham, Robert M. Plenge, Achim Plum, Mihai V. Podgoreanu, Jonathan R. Pollack, Rebecca L. Pollex, Nathan D. Price, Thomas Quertermous, Francisco J. Quintana, Benjamin A. Raby, Daniel J. Rader, Sridhar Ramaswamy, Hans-Georg Rammensee, Scott D. Ramsey, Rino Rappuoli, Nader Rifai, Lisa Rimsza, Yu-Hui Rogers, Allen D. Roses, Jeffrey S. Ross, Ronenn Roubenoff, Marschall S. Runge, Maren T. Scheuner, Matthias Schuster, David A. Schwartz, Debra A. Schwinn, Chia Kee Seng, Nicholas A. Shackel, M. Frances Shannon, A. Dean Sherry, Jiaqi Shi, Kevin Shianna, Yelizaveta Shnayder, Andrew B. Singleton, T.P. Slavin, Desmond J. Smith, Rikkert L. Snoeckx, Ralph Snyderman, Avrum Spira, Colin F. Spraggs, Robert D. Stevens, Nicolas A. Stewart, Alison Stewart, F. Stewart, Robert L. Strausberg, Moshe Szyf, Weihong Tan, Robert I. Tepper, Hervé Tettelin, Giovana R. Thomas, John D. Thompson, Visith Thongboonkerd, Eric J. Topol, Jeffrey A. Towbin, Ad A. van Bodegraven, Kris Van Den Bogaert, Filip Van den Bosch, Matte Vatta, Timothy D. Veenstra, David L. Veenstra, Aleksandr E. Vendrov, Catherine M. Verfaillie, Nicole M. Walley, Ling Wang, Mike Weale, Michael E. Weinblatt, Howard L. Weiner, Scott T. Weiss, Ralph Weissleder, Samuel A. Wells, Brett R. Wenner, Ilse R. Wiechers, Georgia L. Wiesner, Janey L. Wiggs, Cisca Wijmenga, Huntington F. Willard, James M. Wilson, Nelson A. Wivel, Jay Wohlgemuth, Janet Woodcock, Christopher W. Woods, Paula W. Woon, Chana Yagil, Yoram Yagil, Tadashi Yamamoto, Gang Yao, Andrew J. Yee, Hyuntae Yoo, Y. Nancy You, Li-Rong Yu, Jean-François Zagury, Ron Zimmern, and Stephan Züchner

Published
2009
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9. Two-transcript gene expression classifiers in the diagnosis and prognosis of human diseases

Author

Donald Geman, Lucas B. Edelman, Nathan D. Price, Giuseppe Toia, and Wei Zhang

Subjects
lcsh:QH426-470, Transcription, Genetic, lcsh:Biotechnology, Disease, Biology, Proteomics, Bioinformatics, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, Transcription (biology), lcsh:TP248.13-248.65, Gene expression, Diagnosis, Research article, Genetics, Humans, Gene, 030304 developmental biology, 0303 health sciences, Prognosis, Phenotype, lcsh:Genetics, 030220 oncology & carcinogenesis, DNA microarray, Algorithms, Biotechnology
Abstract

Background Identification of molecular classifiers from genome-wide gene expression analysis is an important practice for the investigation of biological systems in the post-genomic era - and one with great potential for near-term clinical impact. The 'Top-Scoring Pair' (TSP) classification method identifies pairs of genes whose relative expression correlates strongly with phenotype. In this study, we sought to assess the effectiveness of the TSP approach in the identification of diagnostic classifiers for a number of human diseases including bacterial and viral infection, cardiomyopathy, diabetes, Crohn's disease, and transformed ulcerative colitis. We examined transcriptional profiles from both solid tissues and blood-borne leukocytes. Results The algorithm identified multiple predictive gene pairs for each phenotype, with cross-validation accuracy ranging from 70 to nearly 100 percent, and high sensitivity and specificity observed in most classification tasks. Performance compared favourably with that of pre-existing transcription-based classifiers, and in some cases was comparable to the accuracy of current clinical diagnostic procedures. Several diseases of solid tissues could be reliably diagnosed through classifiers based on the blood-borne leukocyte transcriptome. The TSP classifier thus represents a simple yet robust method to differentiate between diverse phenotypic states based on gene expression profiles. Conclusion Two-transcript classifiers have the potential to reliably classify diverse human diseases, through analysis of both local diseased tissue and the immunological response assayed through blood-borne leukocytes. The experimental simplicity of this method results in measurements that can be easily translated to clinical practice.

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