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Hu LZ, Douglass E, Turunen M, Pampou S, Grunn A, Realubit R, Antolin AA, Wang ALE, Li H, Subramaniam P, Mundi PS, Karan C, Alvarez M, Califano A. Elucidating Compound Mechanism of Action and Polypharmacology with a Large-scale Perturbational Profile Compendium. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.08.561457. [PMID: 37873470 PMCID: PMC10592689 DOI: 10.1101/2023.10.08.561457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
The Mechanism of Action (MoA) of a drug is generally represented as a small, non-tissue-specific repertoire of high-affinity binding targets. Yet, drug activity and polypharmacology are increasingly associated with a broad range of off-target and tissue-specific effector proteins. To address this challenge, we have leveraged a microfluidics-based Plate-Seq technology to survey drug perturbational profiles representing >700 FDA-approved and experimental oncology drugs, in cell lines selected as high-fidelity models of 23 aggressive tumor subtypes. Built on this dataset, we implemented an efficient computational framework to define a tissue-specific protein activity landscape of these drugs and reported almost 50 million differential protein activities derived from drug perturbations vs. vehicle controls. These analyses revealed thousands of highly reproducible and novel, drug-mediated modulation of tissue-specific targets, leading to generation of a proteome-wide drug functional network, characterization of MoA-related drug clusters and off-target effects, dramatical expansion of druggable human proteome, and identification and experimental validation of novel, tissue-specific inhibitors of undruggable oncoproteins, most never reported before. The drug perturbation profile resource described here represents the first, large-scale, whole-genome-wide, RNA-Seq based dataset assembled to date, with the proposed framework, which is easily extended to elucidating the MoA of novel small-molecule libraries, facilitates mechanistic exploration of drug functions, supports systematic and quantitative approaches to precision oncology, and serves as a rich data foundation for drug discovery.
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Huang S, Cole JM. BatteryBERT: A Pretrained Language Model for Battery Database Enhancement. J Chem Inf Model 2022; 62:6365-6377. [PMID: 35533012 PMCID: PMC9795558 DOI: 10.1021/acs.jcim.2c00035] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
A great number of scientific papers are published every year in the field of battery research, which forms a huge textual data source. However, it is difficult to explore and retrieve useful information efficiently from these large unstructured sets of text. The Bidirectional Encoder Representations from Transformers (BERT) model, trained on a large data set in an unsupervised way, provides a route to process the scientific text automatically with minimal human effort. To this end, we realized six battery-related BERT models, namely, BatteryBERT, BatteryOnlyBERT, and BatterySciBERT, each of which consists of both cased and uncased models. They have been trained specifically on a corpus of battery research papers. The pretrained BatteryBERT models were then fine-tuned on downstream tasks, including battery paper classification and extractive question-answering for battery device component classification that distinguishes anode, cathode, and electrolyte materials. Our BatteryBERT models were found to outperform the original BERT models on the specific battery tasks. The fine-tuned BatteryBERT was then used to perform battery database enhancement. We also provide a website application for its interactive use and visualization.
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Affiliation(s)
- Shu Huang
- Cavendish
Laboratory, Department of Physics, University
of Cambridge, J.J. Thomson Avenue, Cambridge CB3 0HE, U.K.
| | - Jacqueline M. Cole
- Cavendish
Laboratory, Department of Physics, University
of Cambridge, J.J. Thomson Avenue, Cambridge CB3 0HE, U.K.,ISIS
Neutron and Muon Source, Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0QX, U.K.,
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Clarke R, Kraikivski P, Jones BC, Sevigny CM, Sengupta S, Wang Y. A systems biology approach to discovering pathway signaling dysregulation in metastasis. Cancer Metastasis Rev 2020; 39:903-918. [PMID: 32776157 PMCID: PMC7487029 DOI: 10.1007/s10555-020-09921-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Accepted: 07/13/2020] [Indexed: 02/07/2023]
Abstract
Total metastatic burden is the primary cause of death for many cancer patients. While the process of metastasis has been studied widely, much remains to be understood. Moreover, few agents have been developed that specifically target the major steps of the metastatic cascade. Many individual genes and pathways have been implicated in metastasis but a holistic view of how these interact and cooperate to regulate and execute the process remains somewhat rudimentary. It is unclear whether all of the signaling features that regulate and execute metastasis are yet fully understood. Novel features of a complex system such as metastasis can often be discovered by taking a systems-based approach. We introduce the concepts of systems modeling and define some of the central challenges facing the application of a multidisciplinary systems-based approach to understanding metastasis and finding actionable targets therein. These challenges include appreciating the unique properties of the high-dimensional omics data often used for modeling, limitations in knowledge of the system (metastasis), tumor heterogeneity and sampling bias, and some of the issues key to understanding critical features of molecular signaling in the context of metastasis. We also provide a brief introduction to integrative modeling that focuses on both the nodes and edges of molecular signaling networks. Finally, we offer some observations on future directions as they relate to developing a systems-based model of the metastatic cascade.
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Affiliation(s)
- Robert Clarke
- Department of Oncology, Georgetown University Medical Center, 3970 Reservoir Rd NW, Washington, DC, 20057, USA.
- Hormel Institute and Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Austin, MN, 55912, USA.
| | - Pavel Kraikivski
- Academy of Integrated Science, Division of Systems Biology, Virginia Polytechnic and State University, Blacksburg, VA, 24061, USA
| | - Brandon C Jones
- Department of Oncology, Georgetown University Medical Center, 3970 Reservoir Rd NW, Washington, DC, 20057, USA
| | - Catherine M Sevigny
- Department of Oncology, Georgetown University Medical Center, 3970 Reservoir Rd NW, Washington, DC, 20057, USA
| | - Surojeet Sengupta
- Department of Oncology, Georgetown University Medical Center, 3970 Reservoir Rd NW, Washington, DC, 20057, USA
| | - Yue Wang
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, 22203, USA
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Kehoe PG. The Coming of Age of the Angiotensin Hypothesis in Alzheimer's Disease: Progress Toward Disease Prevention and Treatment? J Alzheimers Dis 2019; 62:1443-1466. [PMID: 29562545 PMCID: PMC5870007 DOI: 10.3233/jad-171119] [Citation(s) in RCA: 93] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
There is wide recognition of a complex association between midlife hypertension and cardiovascular disease and later development of Alzheimer’s disease (AD) and cognitive impairment. While significant progress has been made in reducing rates of mortality and morbidity due to cardiovascular disease over the last thirty years, progress towards effective treatments for AD has been slower. Despite the known association between hypertension and dementia, research into each disease has largely been undertaken in parallel and independently. Yet over the last decade and a half, the emergence of converging findings from pre-clinical and clinical research has shown how the renin angiotensin system (RAS), which is very important in blood pressure regulation and cardiovascular disease, warrants careful consideration in the pathogenesis of AD. Numerous components of the RAS have now been found to be altered in AD such that the multifunctional and potent vasoconstrictor angiotensin II, and similarly acting angiotensin III, are greatly altered at the expense of other RAS signaling peptides considered to contribute to neuronal and cognitive function. Collectively these changes may contribute to many of the neuropathological hallmarks of AD, as well as observed progressive deficiencies in cognitive function, while also linking elements of a number of the proposed hypotheses for the cause of AD. This review discusses the emergence of the RAS and its likely importance in AD, not only because of the multiple facets of its involvement, but also perhaps fortuitously because of the ready availability of numerous RAS-acting drugs, that could be repurposed as interventions in AD.
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Affiliation(s)
- Patrick Gavin Kehoe
- Dementia Research Group, Translational Health Sciences, Bristol Medical School, University of Bristol, Southmead Hospital, Bristol, UK
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Wang ZT, Tan CC, Tan L, Yu JT. Systems biology and gene networks in Alzheimer’s disease. Neurosci Biobehav Rev 2019; 96:31-44. [DOI: 10.1016/j.neubiorev.2018.11.007] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Revised: 11/18/2018] [Accepted: 11/18/2018] [Indexed: 12/25/2022]
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A review on iron chelators as potential therapeutic agents for the treatment of Alzheimer’s and Parkinson’s diseases. Mol Divers 2018; 23:509-526. [DOI: 10.1007/s11030-018-9878-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Accepted: 09/25/2018] [Indexed: 12/19/2022]
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Abstract
Recent studies across multiple tumour types are starting to reveal a recurrent regulatory architecture in which genomic alterations cluster upstream of functional master regulator (MR) proteins, the aberrant activity of which is both necessary and sufficient to maintain tumour cell state. These proteins form small, hyperconnected and autoregulated modules (termed tumour checkpoints) that are increasingly emerging as optimal biomarkers and therapeutic targets. Crucially, as their activity is mostly dysregulated in a post-translational manner, rather than by mutations in their corresponding genes or by differential expression, the identification of MR proteins by conventional methods is challenging. In this Opinion article, we discuss novel methods for the systematic analysis of MR proteins and of the modular regulatory architecture they implement, including their use as a valuable reductionist framework to study the genetic heterogeneity of human disease and to drive key translational applications.
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Affiliation(s)
- Andrea Califano
- Department of Systems Biology, Columbia University, and the Departments of Biomedical Informatics, Biochemistry and Molecular Biophysics, JP Sulzberger Columbia Genome Center, Herbert Irving Comprehensive Cancer Center, Columbia University, New York, New York 10032, USA
| | - Mariano J Alvarez
- DarwinHealth, Inc., 3960 Broadway, Suite 540, New York, New York 10032, USA
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Chen JC, Cerise JE, Jabbari A, Clynes R, Christiano AM. Master regulators of infiltrate recruitment in autoimmune disease identified through network-based molecular deconvolution. Cell Syst 2015; 1:326-337. [PMID: 26665180 DOI: 10.1016/j.cels.2015.11.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Network-based molecular modeling of physiological behaviors has proven invaluable in the study of complex diseases such as cancer, but these approaches remain largely untested in contexts involving interacting tissues such as autoimmunity. Here, using Alopecia Areata (AA) as a model, we have adapted regulatory network analysis to specifically isolate physiological behaviors in the skin that contribute to the recruitment of immune cells in autoimmune disease. We use context-specific regulatory networks to deconvolve and identify skin-specific regulatory modules with IKZF1 and DLX4 as master regulators (MRs). These MRs are sufficient to induce AA-like molecular states in vitro in three cultured cell lines, resulting in induced NKG2D-dependent cytotoxicity. This work demonstrates the feasibility of a network-based approach for compartmentalizing and targeting molecular behaviors contributing to interactions between tissues in autoimmune disease.
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Affiliation(s)
- James C Chen
- Department of Dermatology, Herbert Irving Pavilion, Columbia University, 161 Fort Washington Avenue, New York, NY, 10032, USA ; Department of Systems Biology, Columbia University, 1130 Saint Nicholas Avenue, New York, NY, 10032, USA
| | - Jane E Cerise
- Department of Dermatology, Herbert Irving Pavilion, Columbia University, 161 Fort Washington Avenue, New York, NY, 10032, USA
| | - Ali Jabbari
- Department of Dermatology, Herbert Irving Pavilion, Columbia University, 161 Fort Washington Avenue, New York, NY, 10032, USA
| | - Raphael Clynes
- Department of Dermatology, Herbert Irving Pavilion, Columbia University, 161 Fort Washington Avenue, New York, NY, 10032, USA
| | - Angela M Christiano
- Department of Dermatology, Herbert Irving Pavilion, Columbia University, 161 Fort Washington Avenue, New York, NY, 10032, USA ; Department of Genetics and Development, Columbia University, 701 West 168th Street, New York, NY, 10032, USA
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