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1. A data-driven architecture using natural language processing to improve phenotyping efficiency and accelerate genetic diagnoses of rare disorders

Author

Jignesh R. Parikh, Casie A. Genetti, Asli Aykanat, Catherine A. Brownstein, Klaus Schmitz-Abe, Morgan Danowski, Andrew Quitadomo, Jill A. Madden, Calum Yacoubian, Richard Gain, Tessa Williams, Mary Meskell, Andrew Brown, Alison Frith, Shira Rockowitz, Piotr Sliz, Pankaj B. Agrawal, Thomas Defay, Paul McDonagh, John Reynders, Sebastien Lefebvre, and Alan H. Beggs

Subjects
natural language processing, genetics, Human Phenotype Ontology, electronic health records, Genetics, QH426-470
Abstract

Summary: Effective genetic diagnosis requires the correlation of genetic variant data with detailed phenotypic information. However, manual encoding of clinical data into machine-readable forms is laborious and subject to observer bias. Natural language processing (NLP) of electronic health records has great potential to enhance reproducibility at scale but suffers from idiosyncrasies in physician notes and other medical records. We developed methods to optimize NLP outputs for automated diagnosis. We filtered NLP-extracted Human Phenotype Ontology (HPO) terms to more closely resemble manually extracted terms and identified filter parameters across a three-dimensional space for optimal gene prioritization. We then developed a tiered pipeline that reduces manual effort by prioritizing smaller subsets of genes to consider for genetic diagnosis. Our filtering pipeline enabled NLP-based extraction of HPO terms to serve as a sufficient replacement for manual extraction in 92% of prospectively evaluated cases. In 75% of cases, the correct causal gene was ranked higher with our applied filters than without any filters. We describe a framework that can maximize the utility of NLP-based phenotype extraction for gene prioritization and diagnosis. The framework is implemented within a cloud-based modular architecture that can be deployed across health and research institutions.

Published
2021
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2. A Bayesian mathematical model of motor and cognitive outcomes in Parkinson's disease.

Author

Boris Hayete, Diane Wuest, Jason Laramie, Paul McDonagh, Bruce Church, Shirley Eberly, Anthony Lang, Kenneth Marek, Karl Runge, Ira Shoulson, Andrew Singleton, Caroline Tanner, Iya Khalil, Ajay Verma, and Bernard Ravina

Subjects
Medicine, Science
Abstract

There are few established predictors of the clinical course of PD. Prognostic markers would be useful for clinical care and research.To identify predictors of long-term motor and cognitive outcomes and rate of progression in PD.Newly diagnosed PD participants were followed for 7 years in a prospective study, conducted at 55 centers in the United States and Canada. Analyses were conducted in 244 participants with complete demographic, clinical, genetic, and dopamine transporter imaging data. Machine learning dynamic Bayesian graphical models were used to identify and simulate predictors and outcomes. The outcomes rate of cognition changes are assessed by the Montreal Cognitive Assessment scores, and rate of motor changes are assessed by UPDRS part-III.The most robust and consistent longitudinal predictors of cognitive function included older age, baseline Unified Parkinson's Disease Rating Scale (UPDRS) parts I and II, Schwab and England activities of daily living scale, striatal dopamine transporter binding, and SNP rs11724635 in the gene BST1. The most consistent predictor of UPDRS part III was baseline level of activities of daily living (part II). Key findings were replicated using long-term data from an independent cohort study.Baseline function near the time of Parkinson's disease diagnosis, as measured by activities of daily living, is a consistent predictor of long-term motor and cognitive outcomes. Additional predictors identified may further characterize the expected course of Parkinson's disease and suggest mechanisms underlying disease progression. The prognostic model developed in this study can be used to simulate the effects of the prognostic variables on motor and cognitive outcomes, and can be replicated and refined with data from independent longitudinal studies.

Published
2017
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5. Data from Bayesian Network Inference Modeling Identifies TRIB1 as a Novel Regulator of Cell-Cycle Progression and Survival in Cancer Cells

Author

W. Michael Korn, Iya Khalil, Joe W. Gray, Paul McDonagh, Heidi S. Feiler, Christina Curtis, Preeti Sarde, Olga K. Mirzoeva, Heming Xing, and Rina Gendelman

Abstract

Molecular networks governing responses to targeted therapies in cancer cells are complex dynamic systems that demonstrate nonintuitive behaviors. We applied a novel computational strategy to infer probabilistic causal relationships between network components based on gene expression. We constructed a model comprised of an ensemble of networks using multidimensional data from cell line models of cell-cycle arrest caused by inhibition of MEK1/2. Through simulation of a reverse-engineered Bayesian network model, we generated predictions of G1–S transition. The model identified known components of the cell-cycle machinery, such as CCND1, CCNE2, and CDC25A, as well as revealed novel regulators of G1–S transition, IER2, TRIB1, TRIM27. Experimental validation of model predictions confirmed 10 of 12 predicted genes to have a role in G1–S progression. Further analysis showed that TRIB1 regulated the cyclin D1 promoter via NFκB and AP-1 sites and sensitized cells to TRAIL-induced apoptosis. In clinical specimens of breast cancer, TRIB1 levels correlated with expression of NFκB and its target genes (IL8, CSF2), and TRIB1 copy number and expression were predictive of clinical outcome. Together, our results establish a critical role of TRIB1 in cell cycle and survival that is mediated via the modulation of NFκB signaling. Cancer Res; 77(7); 1575–85. ©2017 AACR.

Published
2023
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7. Supplemental Figures 1 to 14 from Bayesian Network Inference Modeling Identifies TRIB1 as a Novel Regulator of Cell-Cycle Progression and Survival in Cancer Cells

Author

W. Michael Korn, Iya Khalil, Joe W. Gray, Paul McDonagh, Heidi S. Feiler, Christina Curtis, Preeti Sarde, Olga K. Mirzoeva, Heming Xing, and Rina Gendelman

Abstract

Legend Figure S1: Schematic of the experimental framework Legend Figure S2: Predicted phenotypic value and assessment of predictive power of the model. Legend Figure S3: Taget knock-down. Legend Figure S4: Treatment with individual siRNAs against TRIB1 leads to G1-S arrest. Legend Figure S5: RNA levels of cell cycle genes. Legend Figure S6: TRIB1 knockdown results in inhibition of NFκB-responsive promoter activity: comparison of individual siRNA duplexes. Legend Figure S7: TRIB1 resistant to siRNA (TRIB1-siR) rescues the phenotype induced by TRIB1-siRNA. Legend Figure S8: Downregulation of NFkB target genes after treatment with TRIB1 siRNA. Legend Figure S9: TRIB1 knock down leads to inhibition of NFκB pathway. Legend Figure S10: TRIB1 siRNA promotes TRAIL-induced apoptosis through activation of caspase-8 cleavage. Legend Figure S11: TRIB1 knockdown results in decrease of YY1gene expression. Legend Figure S12: Kaplan-Meier plots Legend Figure S13: Hazard Ratios for BCCS distribution. Legend Figure S14: Hazard Ratios for OS distribution.

Published
2023
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10. Extracting longitudinal anticancer treatments at scale using deep natural language processing and temporal reasoning

Author

Paul McDonagh, Tony Prentice, Minghao Li, Christopher Gilman, Arielle Redfern, Zongzhi Liu, Meng Ma, Yun Mai, Eric E. Schadt, Kyeryoung Lee, Qi Pan, Tommy Mullaney, Rong Chen, Mingwei Zhang, and Xiaoyan Wang

Subjects
Cancer Research, Oncology, business.industry, Scale (chemistry), Medicine, Artificial intelligence, business, computer.software_genre, computer, Natural language processing
Abstract

e18747 Background: Accurate longitudinal cancer treatments are vital for establishing primary endpoints such as outcome as well as for the investigation of adverse events. However, many longitudinal therapeutic regimens are not well captured in structured electronic health records (EHRs). Thus, their recognition in unstructured data such as clinical notes is critical to gain an accurate description of the real-world patient treatment journey. Here, we demonstrate a scalable approach to extract high-quality longitudinal cancer treatments from lung cancer patients' clinical notes using a Bidirectional Long Short Term Memory (BiLSTM) and Conditional Random Fields (CRF) based natural language processing (NLP) pipeline. Methods: The lung cancer (LC) cohort of 4,698 patients was curated from the Mount Sinai Healthcare system (2003-2020). Two domain experts developed a structured framework of entities and semantics that captured treatment and its temporality. The framework included therapy type (chemotherapy, targeted therapy, immunotherapy, etc.), status (on, off, hold, planned, etc.) and temporal reasoning entities and relations (admin_date, duration, etc.) We pre-annotated 149 FDA-approved cancer drugs and longitudinal timelines of treatment on the training corpus. A NLP pipeline was implemented with BiLSTM-CRF-based deep learning models to train and then apply the resulting models to the clinical notes of LC cohort. A postprocessor was developed to subsequently post-coordinate and refine the output. We performed both cross-evaluation and independent evaluation to assess the pipeline performance. Results: We applied the NLP pipeline to the 853,755 clinical notes, and identified 1,155 distinct entities for 194 cancer generic drugs, including 74 chemotherapy drugs, 21 immunotherapy drugs, and 99 targeted therapy drugs. We identified chemotherapy, immunotherapy, or targeted therapy data for 3,509 patients in the LC cohort from the clinical notes. Compared to only 2,395 patients with cancer treatments in structured EHR, this pipeline identified cancer treatments from notes for additional 2,303 patients who did not have any available cancer treatment data in the structured EHR. Our evaluation schema indicates that the longitudinal cancer drug recognition pipeline delivers strong performance (named entity recognization for drugs and temporal: F1 = 95%; drug-temporal relation recognition: F1 = 90%). Conclusions: We developed a high-performance BiLSTM-CRF based NLP pipeline to recognize longitudinal cancer treatments. The pipeline recovers and encodes as twice as many patients with cancer treatments compared with structured EHR. Our study indicates deep NLP with temporal reasoning could substantially accelerate the extraction of treatment profiles at scale. The pipeline is adjustable and can be applied across different cancers.

Published
2021
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11. Abstracts

Author

C. A. Botero, C. E. Smith, C. Holbrook, A. C. Pinchak, David Johnson, Dorothy Thomson, Taras Mycyk, M. Burbridge, Irvin Mayers, nR. W. M. Wahba, F. Belque, S. J. Kleiman, Steven Parker, Peter Cox, Helen Holtby, Lawrence Roy, Marc A. St-Amand, John M. Murkin, Donna Baird, Donal B. Downey, Alan H. Menkis, Fan Yang, Éric Troncy, Martin Francœur, Marc Charbonneau, Patrick Vinay, Gilbert Blaise, William M. Splinter, David J. Roberts, Elliot J. Rhine, Helen B. MacNeill, Craig W. Reid, William PS McKay, Miklavs Erjavec, Benjamin W. S. McKay, Peter H. Gregson, Travis Blanchet, Guy Kember, Josée Lavoie, Daniel Vischoff, Louise Cyrenne, Edith Villeneuve, Pierre Williot, A. K. Raghupathy, R. Haug, B. Punjabi, F. Ditzig, Howard Melnik, Michael J. Tessler, L. Jill Krasner, David M. Corda, Kal Solanki, A. Joseph Layon, T. James Gallagher, Daniel P. Stoltzfus, Shannon L. Rabuka, Carol A. Moote, Robert J. B. Chen, Doreen A. Yee, Ellen Harrington, Beverley A. Orser, D. Mitch Giffin, Kenneth W. Gow, P. Terry Phang, Keith R. Walley, C. Brian Warriner, Matthew H. Cohen, Andrew J. Klahsen, Deirdre O’Reilly, John McBride, Margaret Ballantyne, Blair D. Goranson, Scott Lang, William N. Dust, Jeff McKerrell, Guy Martin, René Martin, Daniel Martin, Philippe Valet, Jean-Pierre Tétrault, Caroline Dagenais, Martine Pirlet, Dominique Dansereau, Pedro D’Orléans-Justes, Agnès Jankowska, Yves Veillette, Angela L. Mathieson, Howard Intrater, Lionel Cruickshank, P. C. Duke, B. Y. Ong, Vincent Woo, Donna Schimnowski, Sharon Trosky, Linda Dalton, Ibrahim Zabani, Colin R. Chilvers, Himat Vaghadia, Pamela M. Merrick, Ibrahim Kashkari, Hossam Al-Oufi, D. Jolly, B. T. Finucane, Wolfgang Weyland, Ulrich Fritz, Heike Landmann, Ingrid Schumacher, Michael English, Dietrich Kettler, Catherine M. Duffy, Pirjo H. Manninen, Frances Chung, Shanthini Sundar, Emilio B. Lobato, Orlando Florete, Glenn B. Paige, Thierry Daloze, Daniel A. Chartrand, Denis St-Laurent, Gordon S. Fox, Murray L. Rice, D. John Doyle, George A. Volgyesi, Joseph A. Fisher, Arthur Slutsky, Igor Salazkin, Karen A. Brown, Pradeep Kulkarni, Bibiana Cujec, Randy McCuaig, Tom Hurst, David Antecol, François Bellemare, Jacques Couture, Manon Marchand, Peter McNeil, Orlando Hung, Lily M. Ho-Tai, J. Hugh Devitt, Alva G. Noel, Michael P. O’Donnell, Robert J. Greenhow, Frank W. Cervenko, Brian Milne, Mark D. Peterson, Ian R. Thomson, Robert J. Hudson, Morley Rosenbloom, Michael Moon, Jitender Sareen, H. Locke Bingham, Steven B. Backman, Reuben D. Stein, C. Polosa, Michael Tessler, Salvatore M. Spadafora, John G. Fuller, Lisa Kim, Keyvan Karkouti, D. Keith Rose, Lorraine E. Ferris, DK Rose, MM Cohen, F. E. Ralley, B. DeVarennes, M. Robitaille, Norman Searle, Raymond Martineau, Peter Conzen, A. Al-Hasani, Tom Ebert, Michael Muzi, Jean-François Hardy, Sylvain Bélisle, André Couturier, Danielle Robitaille, Micheline Roy, Lyne Gagnon, Elisabeth J. Avraamides, P. J. Dryden, J. P. O’Connor, W. R. E. Jamieson, I. Reid, D. Ansley, H. Sadeghi, L. H. Burr, A. I. Munro, P. M. Merrick, Mark Benaroia, Andrew Baker, C. David Mazer, Lee Errett, Luc Frenette, Jerry Cox, Donna Kerns, Steve Pearce, David Mark, Paul McDonagh, Lulz DeLlma, Howard Nathan, Jean-Yves Dupuls, J.Earl Wynands, G. C. Moudgil, J. G. Johnson, G. M. Moudgil, Richard I. Hall, Connie MacLaren, M. J. Ali, M. Ballantyne, D. Norris, Stephen D. Beed, Eugene A. Menard, Leon P. Noel, Gary G. Bonn, William Clarke, H. Marion Gould, Leslie E. Hall, Philippe Bernard, Juan Bass, Ramona A. Kearney, Cheryl A. Mack, Lucy M. Entwistle, Joan C. Bevan, Andrew J. Macnab, Guy Veall, Colin Marsland, Craig R. Ries, Shahnaz K. Hamid, Ian R. Selby, Nancy Sikich, Elizabeth Hsu, Patricia McCarthy, Ching-Yue Yang, Wun-Chin Wu, Jiunn-Jye Huang, Shyu-Yin Chen, Hsiang-Ning Luk, Chok-Yung Chai, Gina K. Lafreniere, Donald G. Brunet, Joel L. Parlow, Hossam El-Beheiry, Aviv Ouanounou, Mary Morris, Peter Carlen, Pamela J. Morgan, Roger Chapados, Marlene Gauthier, John W. D. Knox, Jacques LeLorier, Roddy Lin, Keith Rose, Bernadette Garvey, Robert McBrobm, L. C. McAdam, J. F. MacDonald, B. A. Orser, Georgios koutsoukos, Susan Belo, Christopher A. Chin, Brendan O’Hare, Jerrold Lerman, Junko Endo, Arthur E. Schwartz, Oktavijan Minanov, J. Gilbert Stone, David C. Adams, Aqeel A. Sandhu, Mark E. Pearson, William L. Young, Robert E. Michler, Ernest Cutz, Matt M. Kurrek, Marsha M. Cohen, Kevin Fish, Pamela Fish, Patricia Murphy, Donald Fung, Alva Noel, John-Paul Szalai, Ari Robicsek, Joshua Rucker, Joshua Kruger, Mark Slutsky, Leeor Sommer, Jeff Silverman, Jodi Dickstein, Viren Naik, Douglas J. Hemphill, Regina Kurian, Khursheed N. Jeejeebhoy, Osama A. Alahdal, N. H. Badner, W. E. Komar, R. Bhandari, R. Craen, D. Cuillerier, W. B. Dobkowski, M. H. Smith, A. N. Vannelli, R. B. Bourne, C. H. Rorabeck, J. A. Doyle, Antoinette Corvo, Richard M. Wahba, Nathalie Scheffer, John Y. C. Tsang, Brad A. Brush, N. Q. N’Guyen, C. Orain, S. Tougui, G. Lavenac, D. Milon, Ewan D. Ritchie, Doris Tong, Andrew Norris, Anthony Miniaci, Santhira D. Vairavanathan, Timothy FitzPatrick, Mark Stafford-Smith, Ken Kardash, Toula Trihas, Simcha J. Kleiman, Michel Rossignol, Dominique Bérard, Brent Martel, J. P. Tétrault, Peter G. Lunt, Dennis W. Coombs, Stephen Halpern, Elizabeth A. Peter, Patricia Janssen, Jill Mahy, M. Joanne Douglas, Caroline S. Grange, Timothy J. Adams, Louis Wadsworth, Holly Muir, Romesh Shukla, Desmond Writer, Richard McLaren, Robert Liston, Don Paetkau, Bill Y. Ong, Ron Segstro, Judy Littleford, Cristina Hurtado, Ananthan Krishnathas, Marcelo Lannes, Joanne Fortier, Jun Su, Rubini Jeganathan, and Suzanne Vaillancourt

Subjects
Anesthesiology and Pain Medicine, General Medicine
Published
1996
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12. Abstracts

Author

W. A. C. Mutch, I. W. C. White, N. Donen, I. R. Thomson, M. Rosenbloom, M. Cheang, M. West, Greg Bryson, Christina Mundi, Jean-Yves Dupuis, Michael Bourke, Paul McDonagh, Michael Curran, John Kitts, J. Earl Wynands, Alison S. Carr, Elizabeth J. Hartley, Helen M. Holtby, Peter Cox, Bruce A. Macpherson, James E. Baker, Andrew J. Baker, C. David Mazer, C. Peniston, T. David, D. C. H. Cheng, J. Karski, B. Asokumar, J. Carroll, H. Nierenberg, S. Roger, A. N. Sandier, J. Tong, C. M. Feindel, J. F. Boylan, S. J. Teasdale, J. Boylan, P. Harley, Jennifer E. Froelich, David P. Archer, Alastair Ewen, Naaznin Samanani, Sheldon H. Roth, Richard I. Hall, Michael Neumeister, Gwen Dawe, Cathy Cody, Randy O’Brien, Jan Shields-Thomson, Kenneth M. LeDez, Catherine Penney, Walter Snedden, John Tucker, Nicolas Fauvel, Mladen Glavinovic, François Donati, S. B. Backman, R. D. Stein, C. Polosa, C. Abdallah, S. Gal, A. John Clark, George A. Doig, Tunde Gondocz, E. A. Peter, A. Lopez, A. Mathieu, Pierre Couture, Daniel Boudreault, Marc Derouin, Martin Allard, Gilbert Blaise, Dominique Girard, Richard L. Knill, Teresa Novick, Margaret K. Vandervoort, Frances Chung, Shantha Paramanathar, Smita Parikh, Charles Cruise, Christina Michaloliakou, Brenda Dusek, D. K. Rose, M. M. Cohen, D. DeBoer, George Shorten, Earnest Cutz, Jerrold Lerman, Myrna Dolovich, Edward T. Crosby, Robert Cirone, Dennis Reid, Joanne Lind, Melanie Armstrong, Wanda Doyle, S. Halpern, P. Glanc, T. Myhr, M -L. Ryan, K. Fong, K. Amankwah, A. Ohlsson, R. Preston, Andor Petras, Michael J. Jacka, Brian Milne, Kanji Nakatsu, S. Pancham, Graeme Smith, Kush N. Duggal, M. Joanne Douglas, Pamela M. Merrick, Philip Blew, Donald Miller, Raymond Martineau, Kathryn Hull, C. M. Baron, S. Kowalskl, R. Greengrass, T. Horan, H. Unruh, C. L. Baron, Patricia M. Cruchley, K. Nakajima, Y. Sugiura, Y. Goto, K. Takakura, J. Harada, Robert M. K. W. Lee, Angelica M. Fargas-Babjak, Jin Ni, Eva S. Werstiuk, Joseph Woo, David H. Morison, Michael D. McHugh, Hanna M. Pappius, Hironori Ishihara, Yuki Shimodate, Hiroaki Koh, Akitomo Matsuki, John W. R. Mclntyre, Pierre Bergeron, Lulz G. R. DeLima, Jean-Yves Dupuls, James Enns, J. M. Murkin, F. N. McKenzie, S. White, N. A. Shannon, Wojciech B. Dobkowski, Judy L. Kutt, Bernard J. Mezon, David R. Grant, William J. Wall, Dennis D. Doblar, Yong C. Lim, Luc Frenette, Jaime R. Ronderos, Steve Poplawski, Dinesh Ranjan, L. Dubé, L. Van Obbergh, M. Francoeur, C. Blouin, R. Carrier, D. Doblar, J. Ronderos, D. Singer, J. Cox, B. Gosdin, M. Boatwright, Charles E. Smith, Aleksandr Rovner, Carlos Botero, Curt Holbrook, Nileshkumar Patel, Alfred Pinchak, Alfred C. Pinchak, Yin James Kao, Andrew Thio, Steven J. Barker, Patrick Sullivan, Matthew Posner, C. William Cole, Patty Lindsay, Paul B. Langevin, Paul A. Gulig, N. Gravenstein, David T. Wong, Manuel Gomez, Glenn P. McGuire, Robert J. Byrick, Shared K. Sharma, Frederick J. Carmicheal, Walter J. Montanera, Sharad Sharma, D. A. Yee, Basem I. Naser, G. L. Bryson, J. B. Kitts, D. R. Miller, R. J. Martineau, M. J. Curran, P. R. Bragg, Jacek M. Karski, Davy Cheng, Kevin Bailey, S. Levytam, R. Arellano, J. Katz, J. Doyle, Mitchel B. Sosis, William Blazek, G. Plourde, A. Malik, Tammy Peddle, James Au, Jeffrey Sloan, Mark Cleland, Donald E. Hancock, Nilesh Patel, Frank Costello, Louise Patterson, Masao Yamashita, Tsukasa Kondo, M. R. Graham, D. Thiessen, David F. Vener, Thomas Long, S. Marion, D. J. Steward, Berton Braverman, Mark Levine, Steve Yentis, Catherine R. Bachman, Murray Kopelow, Ann McNeill, R. Graham, Norbert Froese, Leena Patel, Heinz Reimer, Jo Swartz, Suzanne Ullyot, Harley Wong, Maria A. Markakis, Nancy Siklch, Blair D. Goranson, Scott A. Lang, Martin J. Stockwell, Bibiana Cujec, Raymond W. Yip, Lucy C. Southeriand, Tanya Duke B. Vet, Jeisane M. Gollagher, Lesley-Ann Crone, James G. Ferguson, Demetrius Litwin, Maria Bertlik, Beverley A. Orser, Lu-Wang Yang, John F. MacDonald, Gary F. Morris, Wendy L. Gore-Hickman, J. E. Zamora, O. P. Rosaeg, M. P. Lindsay, M. L. Crossan, Carol Pattee, Michael Adams, John P. Koller, Guy J. Lavoie, Wynn M. Rigal, Dylan A. Taylor, Michael G. Grace, Barry A. Flnegan, Christopher Hawkes, Harry Hopkins, Michael Tierney, David R. Drover, Gordon Whatley, J. W. Donald Knox, Jarmila Rausa, Hossam El-Beheiry, Ronald Seegobin, Georgia C. Hirst, William N. Dust, J. David Cassidy, D. Boisvert, H. Braden, M. L. Halperin, S. Cheema-Dhadli, D. J. McKnight, W. Singer, Thomas Elwood, Shirley Huchcroft, Charles MacAdams, R. Peter Farran, Gerald Goresky, Phillip LaLande, Gilles Lacroix, Martin Lessard, Claude Trépanier, Janet M. van Vlymen, Joel L. Parlow, Chikwendu Ibebunjo, Arnold H. Morscher, Gregory J. Gordon, H. P. Grocott, Susan E. Belo, Georgios Koutsoukos, Susan Belo, David Smith, Sarah Henderson, Adriene Gelb, G. Kantor, N. H. Badner, W. E. Komar, R. Bhandari, D. Cuillerier, W. Dobkowski, M. H. Smith, A. N. Vannelli, Sean Wharton, Mike Tierney, E. Redmond, E. Reddy, A. Gray, J. Flynn, R. B. Bourne, C. H. Rorabeck, S. J. MacDonald, J. A. Doyle, Peter T. Newton, Carol A. Moote, R. Joiner, M. F. X. Glynn, Vytas Zulys, M. Hennessy, T. Winton, W. Demajo, William P. S. McKay, Peter H. Gregson, Benjamin W. S. McKay, Julio Militzer, Eric Hollebone, Raymond Yee, George Klein, R. L. Garnett, J. Conway, F. E. Ralley, G. R. Robbins, James E. Brown, J. V. Frei, Edward Podufal, Norman J. Snow, Altagracia M. Chavez, Richard P. Kramer, D. Mickle, William A. Tweed, Bisharad M. Shrestha, Narendra B. Basnyat, Bhawan D. Lekhak, Susan D. O’Leary, J. K. Maryniak, John H. Tucker, Cameron B. Guest, J. Brendan Mullen, J. Colin Kay, Dan F. Wigglesworth, Mashallah Goodarzi, Nicte Ha Shier, John A. Ogden, O. R. Hung, S. Pytka, M. F. Murphy, B. Martin, and R. D. Stewart

Subjects
Anesthesiology and Pain Medicine, General Medicine
Published
1994
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13. Caspase inhibition decreases both platelet phosphatidylserine exposure and aggregation: caspase inhibition of platelets

Author

Zoë, Cohen, Jonathan, Wilson, Leslie, Ritter, and Paul, McDonagh

Subjects
Blood Platelets, Male, Rats, Sprague-Dawley, Platelet Aggregation, Animals, Phosphatidylserines, Cysteine Proteinase Inhibitors, Caspase Inhibitors, Cells, Cultured, Amino Acid Chloromethyl Ketones, Rats
Abstract

Apoptosis of nucleated cells is regulated by caspases, a group of cysteine proteases, and is characterized by phosphatidylserine expression on the outer leaflet of the plasma membrane. Reports indicate that platelets contain caspases. However, the role of caspases in platelet function is not well understood. When platelets become activated, they express phosphatidylserine (PS) on the outer leaflet of the plasma membrane. In addition, platelets aggregate when activated. The aims of this study were to determine if caspase inhibition (using the pan-caspase inhibitor zVAD-fmk): (1) decreased PS expression and (2) decreased platelet aggregation following activation. Flow cytometry was used to determine PS expression and a platelet aggregometer was used to assess aggregation. We found that platelets treated with zVAD-fmk significantly decreased both A23187-induced PS exposure (total fluorescence index, TFI: A23187=791.42+/-174; zVAD+A23187=92.97+/-57, p==0.05) and ADP-induced PS exposure (TFI: ADP=669.24+/-145, zVAD+ADP=174.6+/-151, p==0.05). Further, treatment with zVAD-fmk significantly decreased ADP-induced platelet aggregation (%: untreated=80+/-1.5, zVAD treated=69+/-3.0, p==0.05). These results indicate that caspases play a role in platelet activation, suggesting a unique physiologic role for these proteases.

Published
2003

14. Experimental annotation of the human genome using microarray technology

Author

B. Li, Jason R. King, Greg Schimmack, Paul McDonagh, Matthew J. Marton, Hongyue Dai, Yudong D. He, Stephen W. Scherer, Lani F. Wu, Peter S. Linsley, Pek Yee Lum, Patrick M. Loerch, J. S. Tsang, Philip W. Garrett-engele, P. Cundiff, J. Koch, Michael Ziman, Christopher D. Armour, M. S. Boguski, Thomas R. Ward, Janell M. Schelter, Seve Edwards, Steven J. Altschuler, Eric E. Schadt, Mao Mao, M. Krolewski, John C. Castle, Julja Burchard, Amy Leonardson, Roland Stoughton, John W. Phillips, Daniel D. Shoemaker, Guy Cavet, Miriam Meyer, and Matthew J. Kidd

Subjects
Genetics, Cancer genome sequencing, Multidisciplinary, DNA, Complementary, Genome, Human, Chromosomes, Human, Pair 22, Computational gene, Computational Biology, Genomics, Genome project, Exons, Biology, Genome, Cell Line, Alternative Splicing, Human Genome Project, Humans, Human genome, DNA microarray, Oligonucleotide Probes, Gene, Algorithms, Oligonucleotide Array Sequence Analysis
Abstract

The most important product of the sequencing of a genome is a complete, accurate catalogue of genes and their products, primarily messenger RNA transcripts and their cognate proteins. Such a catalogue cannot be constructed by computational annotation alone; it requires experimental validation on a genome scale. Using ‘exon’ and ‘tiling’ arrays fabricated by ink-jet oligonucleotide synthesis, we devised an experimental approach to validate and refine computational gene predictions and define full-length transcripts on the basis of co-regulated expression of their exons. These methods can provide more accurate gene numbers and allow the detection of mRNA splice variants and identification of the tissue- and disease-specific conditions under which genes are expressed. We apply our technique to chromosome 22q under 69 experimental condition pairs, and to the entire human genome under two experimental conditions. We discuss implications for more comprehensive, consistent and reliable genome annotation, more efficient, full-length complementary DNA cloning strategies and application to complex diseases.

Published
2001

15. Pharmacodynamics of doxacurium during cardiac surgery with hypothermic cardiopulmonary bypass

Author

Paul McDonagh, M Curran, J. Earl Wynands, John B. Kitts, and Jean-Yves Dupuis

Subjects
Male, medicine.medical_specialty, Time Factors, law.invention, Sufentanil, law, Internal medicine, medicine, Cardiopulmonary bypass, Humans, Prospective Studies, Ulnar nerve, Aged, Cardiopulmonary Bypass, business.industry, General Medicine, Middle Aged, Isoquinolines, Effective dose (pharmacology), Adductor pollicis muscle, Cardiac surgery, Anesthesiology and Pain Medicine, Pharmacodynamics, Anesthesia, Cardiology, Midazolam, Female, business, medicine.drug, Neuromuscular Nondepolarizing Agents
Abstract

To determine the characteristics of neuromuscular block produced by two and three times the 95% effective dose (ED95) of doxacurium in patients undergoing coronary artery surgery with hypothermic cardiopulmonary bypass.In a prospective non randomized study, ten patients received doxacurium 0.05 mg.kg-1 (Group 1) and ten others received 0.075 mg.kg-1 (Group 2) with midazolam and sufentanil. The mechanomyographic response of the adductor pollicis muscle after supramaximal train-of-four (TOF) stimulation of the ulnar nerve was recorded intraoperatively and postoperatively. Additional doxacurium (10% of the initial dose) was administered until sternal closure whenever the first twitch (T1) had recovered to 25% of control.The onset time (time to maximal T1 depression) of doxacurium was 390 +/- 148 sec in Group 1 and 370 +/- 74 sec in Group 2 (P = 0.71). The clinical duration of neuromuscular block (time to 25% T1 recovery) was 165 +/- 90 min in Group 1 and 258 +/- 86 in Group 2 (P = 0.03). On arrival to recovery room the mean T1 was 57 +/- 23% in Group 1 and 24 +/- 21% in Group 2 (P = 0.003); the mean T4/T1 ratio was 0.25 +/- 0.15 for five patients of Group 1 with four responses to TOF stimulation and 0.10 for the only patient of Group 2 with four twitches.In contrast with findings in patients without cardiac disease, this study shows comparable onset times of doxacurium with doses of two and three times ED95. The clinical duration of doxacurium is 60 to 100% longer than previously reported in noncardiac surgery.

Published
1996

16. Abstract A53: Bayesian Network Inference Modeling Reveals Novel Regulators of Cell Cycle Progression and Survival

Author

Heidi S. Feiler, Iya Khalil, Joe W. Gray, W. Michael Korn, Olga K. Mirzoeva, Paul McDonagh, Rina Gendelman, Heming Xing, and Preeti Sarde

Subjects
Cancer Research, CDC25A, Cell killing, Cell cycle checkpoint, Oncology, Cancer cell, Gene regulatory network, Regulator, Cell cycle, Biology, Signal transduction, Cell biology
Abstract

Molecular networks governing responses to targeted therapies in cancer cells are complex dynamic systems that demonstrate non-intuitive behaviors. We applied a novel computational strategy to decipher causal relationships between signaling network components. Gene networks were constructed from global gene expression profiles to model G1-S cell cycle arrest as a consequence of inhibition of MEK, a key component of the oncogenic RAF-MEK-ERK signal transduction pathway. Simulation of causally driven models generated a ranked list of 12 testable hypotheses, which confirmed role of known cell cycle regulators (CCND1, CCNE2, and CDC25A) and identified novel drivers of G1-S transition (IER2, TRIB1, C14ORF133). Validation confirmed 11 out 12 predictions. Second model which inferred causal relationships between genes lead to discovery of TRIB1 (tribbles homolog of Drosophila) as a regulator of NFkB-dependent gene expression. TRIB1 also mediates cross-talk to the NFκB pathway by enhancing p100 and IκBα-phosphorylation and degradation thus regulating cell survival and expression of cytokines crucial for tumor-stroma interaction. In agreement with this, knock-down of TRIB1 results in induction of apoptosis and sensitizes cells to cell killing induced by the death-receptor agonist TRAIL via upregulation of TRAIL receptor, DR5. Thus TRIB1 is a central regulator of cell cycle and survival and represents a potential therapeutic target. Therefore, applying REFS (Reverse Engineering Forward Simulation) to analyze gene expression profiles maximizes our ability to extract knowledge from experimental data.

Published
2012
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17. [Untitled]

Author

Kyle W. Hart, Dan Holder, Amy Leonardson, Christopher D. Armour, Jason M. Johnson, Guoya Li, Paul McDonagh, Debraj GuhaThakurta, Mihai Margarint, Zhengyan Kan, Nicholas F. Tsinoremas, Archie Russell, Vladimir Svetnik, Lisa Ying, Ronghua Chen, Guy Cavet, Stephen W. Edwards, Andrew Kasarskis, Ramon M Caceres, John C. Castle, Philip W. Garrett-engele, Eric E. Schadt, and Daniel D. Shoemaker

Subjects
Gene expression profiling, Genetics, Tiling array, Microarray analysis techniques, Human genome, Genome project, DNA microarray, Biology, Genome, Gene
Abstract

Computational and microarray-based experimental approaches were used to generate a comprehensive transcript index for the human genome. Oligonucleotide probes designed from approximately 50,000 known and predicted transcript sequences from the human genome were used to survey transcription from a diverse set of 60 tissues and cell lines using ink-jet microarrays. Further, expression activity over at least six conditions was more generally assessed using genomic tiling arrays consisting of probes tiled through a repeat-masked version of the genomic sequence making up chromosomes 20 and 22. The combination of microarray data with extensive genome annotations resulted in a set of 28,456 experimentally supported transcripts. This set of high-confidence transcripts represents the first experimentally driven annotation of the human genome. In addition, the results from genomic tiling suggest that a large amount of transcription exists outside of annotated regions of the genome and serves as an example of how this activity could be measured on a genome-wide scale. These data represent one of the most comprehensive assessments of transcriptional activity in the human genome and provide an atlas of human gene expression over a unique set of gene predictions. Before the annotation of the human genome is considered complete, however, the previously unannotated transcriptional activity throughout the genome must be fully characterized.

Published
2004
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