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1. Biolink Model: A Universal Schema for Knowledge Graphs in Clinical, Biomedical, and Translational Science

Author

Unni, Deepak R., Moxon, Sierra A. T., Bada, Michael, Brush, Matthew, Bruskiewich, Richard, Clemons, Paul, Dancik, Vlado, Dumontier, Michel, Fecho, Karamarie, Glusman, Gustavo, Hadlock, Jennifer J., Harris, Nomi L., Joshi, Arpita, Putman, Tim, Qin, Guangrong, Ramsey, Stephen A., Shefchek, Kent A., Solbrig, Harold, Soman, Karthik, Thessen, Anne T., Haendel, Melissa A., Bizon, Chris, Mungall, Christopher J., and Consortium, the Biomedical Data Translator

Subjects
Computer Science - Databases
Abstract

Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness between core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these "knowledge graphs" (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally-accepted, open-access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object-oriented classification and graph-oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates), representing biomedical entities such as gene, disease, chemical, anatomical structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

Published
2022
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2. Progress toward a universal biomedical data translator

Author

Fecho, Karamarie, Thessen, Anne E, Baranzini, Sergio E, Bizon, Chris, Hadlock, Jennifer J, Huang, Sui, Roper, Ryan T, Southall, Noel, Ta, Casey, Watkins, Paul B, Williams, Mark D, Xu, Hao, Byrd, William, Dančík, Vlado, Duby, Marc P, Dumontier, Michel, Glusman, Gustavo, Harris, Nomi L, Hinderer, Eugene W, Hyde, Greg, Johs, Adam, Su, Andrew I, Qin, Guangrong, Zhu, Qian, Dougherty, Jennifer, Huang, Conrad, Magis, Andrew, Smith, Brett, Celebi, Remzi, Chen, Zhehuan, Azevedo, Ricardo De Miranda, Emonet, Vincent, Lee, Jay, Weng, Chunhua, Yilmaz, Arif, Kim, Keum Joo, Santos, Eugene, Tonstad, Lucas, Veenhuis, Luke, Yakaboski, Chase, Acevedo, Liliana, Carrell, Steven, Deutsch, Eric, Glen, Amy, Hoffman, Andrew, Koslicki, David, Kvarfordt, Lindsey, Liu, Zheng, Liu, Shaopeng, Ma, Chunyu, Mendoza, Luis, Muluka, Arun Teja, Womack, Finn, Wood, Erica, Roach, Jared, Goel, Prateek, Weber, Rosina, Williams, Andrew, Gormley, Joseph, Zisk, Tom, Hanspers, Kristina, Hoatlin, Maureen, Pico, Alexander, Riutta, Anders, Callaghan, Jackson, Xu, Colleen, Ahalt, Stanley C, Balhoff, Jim, Edwards, Stephen, Haaland, Perry, Knowles, Michael, Krishnamurthy, Ashok, Mandal, Meisha, Peden, David B, Pfaff, Emily, Schurman, Shepherd, Shrivastava, Shalki, Yi, Hong, Reilly, Jason, Kanwar, Richa, Cox, Steven, Vaidya, Gaurav, Wang, Max, Alkanaq, Ahmed, Costanzo, Maria, Koesterer, Ryan, Flannick, Jason, Burtt, Noel, Kluge, Alexandria, Rubin, Irit, Strasser, Michael Michi, Chung, Lawrence, Kang, Jimin, Mantilla, Michelle, Muller, Sandrine, Persaud, Bria, Wei, Qi, Baumgartner, Andrew, Dai, Cheng, and Duvvuri, Venkata

Subjects
Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Biomedical Data Translator Consortium, Cardiorespiratory Medicine and Haematology, Oncology and Carcinogenesis, Other Medical and Health Sciences, General Clinical Medicine, Cardiovascular medicine and haematology, Pharmacology and pharmaceutical sciences
Abstract

Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based "Translator" system capable of integrating existing biomedical data sets and "translating" those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system's architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.

Published
2022

3. Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science

Author

Unni, Deepak R, Moxon, Sierra AT, Bada, Michael, Brush, Matthew, Bruskiewich, Richard, Caufield, J Harry, Clemons, Paul A, Dancik, Vlado, Dumontier, Michel, Fecho, Karamarie, Glusman, Gustavo, Hadlock, Jennifer J, Harris, Nomi L, Joshi, Arpita, Putman, Tim, Qin, Guangrong, Ramsey, Stephen A, Shefchek, Kent A, Solbrig, Harold, Soman, Karthik, Thessen, Anne E, Haendel, Melissa A, Bizon, Chris, Mungall, Christopher J, Consortium, The Biomedical Data Translator, Acevedo, Liliana, Ahalt, Stanley C, Alden, John, Alkanaq, Ahmed, Amin, Nada, Avila, Ricardo, Balhoff, Jim, Baranzini, Sergio E, Baumgartner, Andrew, Baumgartner, William, Belhu, Basazin, Brandes, MacKenzie, Brandon, Namdi, Burtt, Noel, Byrd, William, Callaghan, Jackson, Cano, Marco Alvarado, Carrell, Steven, Celebi, Remzi, Champion, James, Chen, Zhehuan, Chen, Mei‐Jan, Chung, Lawrence, Cohen, Kevin, Conlin, Tom, Corkill, Dan, Costanzo, Maria, Cox, Steven, Crouse, Andrew, Crowder, Camerron, Crumbley, Mary E, Dai, Cheng, Dančík, Vlado, De Miranda Azevedo, Ricardo, Deutsch, Eric, Dougherty, Jennifer, Duby, Marc P, Duvvuri, Venkata, Edwards, Stephen, Emonet, Vincent, Fehrmann, Nathaniel, Flannick, Jason, Foksinska, Aleksandra M, Gardner, Vicki, Gatica, Edgar, Glen, Amy, Goel, Prateek, Gormley, Joseph, Greyber, Alon, Haaland, Perry, Hanspers, Kristina, He, Kaiwen, Henrickson, Jeff, Hinderer, Eugene W, Hoatlin, Maureen, Hoffman, Andrew, Huang, Sui, Huang, Conrad, Hubal, Robert, Huellas‐Bruskiewicz, Kenneth, Huls, Forest B, Hunter, Lawrence, Hyde, Greg, Issabekova, Tursynay, Jarrell, Matthew, Jenkins, Lindsay, Johs, Adam, Kang, Jimin, Kanwar, Richa, Kebede, Yaphet, Kim, Keum Joo, Kluge, Alexandria, Knowles, Michael, and Koesterer, Ryan

Subjects
Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Networking and Information Technology R&D (NITRD), Data Science, 2.6 Resources and infrastructure (aetiology), Aetiology, Generic health relevance, Knowledge, Pattern Recognition, Automated, Translational Science, Biomedical, Biomedical Data Translator Consortium, Cardiorespiratory Medicine and Haematology, Oncology and Carcinogenesis, Other Medical and Health Sciences, General Clinical Medicine, Cardiovascular medicine and haematology, Pharmacology and pharmaceutical sciences
Abstract

Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these "knowledge graphs" (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open-access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open-source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object-oriented classification and graph-oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

Published
2022

4. Multi-Omics Resolves a Sharp Disease-State Shift between Mild and Moderate COVID-19

Author

Su, Yapeng, Chen, Daniel, Yuan, Dan, Lausted, Christopher, Choi, Jongchan, Dai, Chengzhen L, Voillet, Valentin, Duvvuri, Venkata R, Scherler, Kelsey, Troisch, Pamela, Baloni, Priyanka, Qin, Guangrong, Smith, Brett, Kornilov, Sergey A, Rostomily, Clifford, Xu, Alex, Li, Jing, Dong, Shen, Rothchild, Alissa, Zhou, Jing, Murray, Kim, Edmark, Rick, Hong, Sunga, Heath, John E, Earls, John, Zhang, Rongyu, Xie, Jingyi, Li, Sarah, Roper, Ryan, Jones, Lesley, Zhou, Yong, Rowen, Lee, Liu, Rachel, Mackay, Sean, O’Mahony, D Shane, Dale, Christopher R, Wallick, Julie A, Algren, Heather A, Zager, Michael A, Unit, the ISB-Swedish COVID19 Biobanking, Wei, Wei, Price, Nathan D, Huang, Sui, Subramanian, Naeha, Wang, Kai, Magis, Andrew T, Hadlock, Jenn J, Hood, Leroy, Aderem, Alan, Bluestone, Jeffrey A, Lanier, Lewis L, Greenberg, Philip D, Gottardo, Raphael, Davis, Mark M, Goldman, Jason D, and Heath, James R

Subjects
Infectious Diseases, Clinical Research, Prevention, Inflammatory and immune system, Good Health and Well Being, Adolescent, Adult, Aged, Aged, 80 and over, COVID-19, Female, Genomics, Humans, Male, Middle Aged, RNA-Seq, SARS-CoV-2, Severity of Illness Index, Single-Cell Analysis, ISB-Swedish COVID19 Biobanking Unit, CITE-seq, immune response, infection, metabolomics, multi-omics, proteomics, single-cell RNA-seq, single-cell TCR-seq, single-cell secretome, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

We present an integrated analysis of the clinical measurements, immune cells, and plasma multi-omics of 139 COVID-19 patients representing all levels of disease severity, from serial blood draws collected during the first week of infection following diagnosis. We identify a major shift between mild and moderate disease, at which point elevated inflammatory signaling is accompanied by the loss of specific classes of metabolites and metabolic processes. Within this stressed plasma environment at moderate disease, multiple unusual immune cell phenotypes emerge and amplify with increasing disease severity. We condensed over 120,000 immune features into a single axis to capture how different immune cell classes coordinate in response to SARS-CoV-2. This immune-response axis independently aligns with the major plasma composition changes, with clinical metrics of blood clotting, and with the sharp transition between mild and moderate disease. This study suggests that moderate disease may provide the most effective setting for therapeutic intervention.

Published
2020

5. Multiomic Immunophenotyping of COVID-19 Patients Reveals Early Infection Trajectories

Author

Su, Yapeng, Chen, Daniel, Lausted, Christopher, Yuan, Dan, Choi, Jongchan, Dai, Cheng, Voillet, Valentin, Scherler, Kelsey, Troisch, Pamela, Duvvuri, Venkata R, Baloni, Priyanka, Qin, Guangrong, Smith, Brett, Kornilov, Sergey, Rostomily, Clifford, Xu, Alex, Li, Jing, Dong, Shen, Rothchild, Alissa, Zhou, Jing, Murray, Kim, Edmark, Rick, Hong, Sunga, Jones, Lesley, Zhou, Yong, Roper, Ryan, Mackay, Sean, O’Mahony, D Shane, Dale, Christopher R, Wallick, Julie A, Algren, Heather A, Michael, Zager A, Magis, Andrew, Wei, Wei, Price, Nathan D, Huang, Sui, Subramanian, Naeha, Wang, Kai, Hadlock, Jennifer, Hood, Leroy, Aderem, Alan, Bluestone, Jeffrey A, Lanier, Lewis L, Greenberg, Phil, Gottardo, Raphael, Davis, Mark M, Goldman, Jason D, Heath, James R, and Unit, the ISB-Swedish COVID19 Biobanking

Subjects
Emerging Infectious Diseases, Infectious Diseases, Infection, Inflammatory and immune system, Good Health and Well Being
Abstract

Host immune responses play central roles in controlling SARS-CoV2 infection, yet remain incompletely characterized and understood. Here, we present a comprehensive immune response map spanning 454 proteins and 847 metabolites in plasma integrated with single-cell multi-omic assays of PBMCs in which whole transcriptome, 192 surface proteins, and T and B cell receptor sequence were co-analyzed within the context of clinical measures from 50 COVID19 patient samples. Our study reveals novel cellular subpopulations, such as proliferative exhausted CD8 + and CD4 + T cells, and cytotoxic CD4 + T cells, that may be features of severe COVID-19 infection. We condensed over 1 million immune features into a single immune response axis that independently aligns with many clinical features and is also strongly associated with disease severity. Our study represents an important resource towards understanding the heterogeneous immune responses of COVID-19 patients and may provide key information for informing therapeutic development.

Published
2020

7. Mutation Patterns Predict Drug Sensitivity in Acute Myeloid Leukemia

Author

Qin, Guangrong, primary, Dai, Jin, additional, Chien, Sylvia, additional, Martins, Timothy J., additional, Loera, Brenda, additional, Nguyen, Quy H., additional, Oakes, Melanie L., additional, Tercan, Bahar, additional, Aguilar, Boris, additional, Hagen, Lauren, additional, McCune, Jeannine, additional, Gelinas, Richard, additional, Monnat, Raymond J., additional, Shmulevich, Ilya, additional, and Becker, Pamela S., additional

Published
2024
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9. A framework towards digital twins for type 2 diabetes

Author

Zhang, Yue, primary, Qin, Guangrong, additional, Aguilar, Boris, additional, Rappaport, Noa, additional, Yurkovich, James T., additional, Pflieger, Lance, additional, Huang, Sui, additional, Hood, Leroy, additional, and Shmulevich, Ilya, additional

Published
2024
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11. Proteomics identifies new therapeutic targets of early-stage hepatocellular carcinoma

Author

Jiang, Ying, Sun, Aihua, Zhao, Yang, Ying, Wantao, Sun, Huichuan, Yang, Xinrong, Xing, Baocai, Sun, Wei, Ren, Liangliang, Hu, Bo, Li, Chaoying, Zhang, Li, Qin, Guangrong, Zhang, Menghuan, Chen, Ning, Zhang, Manli, Huang, Yin, Zhou, Jinan, Zhao, Yan, Liu, Mingwei, Zhu, Xiaodong, Qiu, Yang, Sun, Yanjun, Huang, Cheng, Yan, Meng, Wang, Mingchao, Liu, Wei, Tian, Fang, Xu, Huali, Zhou, Jian, Wu, Zhenyu, Shi, Tieliu, Zhu, Weimin, Qin, Jun, Xie, Lu, Fan, Jia, Qian, Xiaohong, He, Fuchu, and Chinese Human Proteome Project (CNHPP) Consortium

Published
2019
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13. Mutation Patterns Predict Drug Sensitivity in Acute Myeloid Leukemia

Author

Qin, Guangrong, primary, Dai, Jin, additional, Chien, Sylvia, additional, Martins, Timothy J., additional, Loera, Brenda, additional, Nguyen, Quy, additional, Oakes, Melanie L., additional, Tercan, Bahar, additional, Aguilar, Boris, additional, Hagen, Lauren, additional, McCune, Jeannine, additional, Gelinas, Richard, additional, Monnat, Raymond J, additional, Shmulevich, Ilya, additional, and Becker, Pamela S., additional

Published
2023
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14. Targeting the Small- and Intermediate-Conductance Ca-Activated Potassium Channels: The Drug-Binding Pocket at the Channel/Calmodulin Interface

Author

Cui, Meng, Qin, Guangrong, Yu, Kunqian, Bowers, M. Scott, and Zhang, Miao

Abstract

The small- and intermediate-conductance Ca2+-activated potassium (SK/IK) channels play important roles in the regulation of excitable cells in both the central nervous and cardiovascular systems. Evidence from animal models has implicated SK/IK channels in neurological conditions such as ataxia and alcohol use disorders. Further, genome-wide association studies have suggested that cardiovascular abnormalities such as arrhythmias and hypertension are associated with single nucleotide polymorphisms that occur within the genes encoding the SK/IK channels. The Ca2+sensitivity of the SK/IK channels stems from a constitutively bound Ca2+-binding protein: calmodulin. Small-molecule positive modulators of SK/IK channels have been developed over the past decade, and recent structural studies have revealed that the binding pocket of these positive modulators is located at the interface between the channel and calmodulin. SK/IK channel positive modulators can potentiate channel activity by enhancing the coupling between Ca2+sensing via calmodulin and mechanical opening of the channel. Here, we review binding pocket studies that have provided structural insight into the mechanism of action for SK/IK channel positive modulators. These studies lay the foundation for structure-based drug discovery efforts that can identify novel SK/IK channel positive modulators. © 2014 S. Karger AG, Basel

Published
2024
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19. Long-Term Outcomes of a Clinical Trial of Molecular and Functional Drug Screening for Individualized Therapy in Relapsed and Refractory (R/R) Acute Leukemia (AL)

Author

Kongtim, Piyanuch, primary, Percival, Mary-Elizabeth M., additional, Martins, Timothy J, additional, Qin, Guangrong, additional, Curley, Niall, additional, Gardner, Kelda M., additional, Chien, Sylvia, additional, Dai, Jin, additional, Oehler, Vivian G., additional, Blau, C. Anthony, additional, Hendrie, Paul C., additional, Cassaday, Ryan D., additional, Halpern, Anna B., additional, Scott, Bart L., additional, Walter, Roland B., additional, Smith, Heather, additional, Marcucci, Guido, additional, and Becker, Pamela S., additional

Published
2022
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25. Characterization of peptide deformylase homologues from Staphylococcus epidermidis

Author

Lin, Penghui, Hu, Tiancen, Hu, Jian, Yu, Wenqi, Han, Cong, Zhang, Jian, Qin, Guangrong, Yu, Kunqian, Gotz, Friedrich, Shen, Xu, Jiang, Hualiang, and Qu, Di

Subjects
Peptides -- Analysis, Staphylococcus -- Genetic aspects, Staphylococcus -- Physiological aspects, Amino acid sequence -- Analysis, Biological sciences
Abstract

The emergence of multi-drug-resistant strains of Staphylococcus epidermidis emphasizes the need to develop new antibiotics. The unique and essential role of the peptide deformylase (PDF) in catalysing the removal of the N-terminal formyl group from newly synthesized polypeptides in eubacteria makes it an attractive antibacterial drug target. In the present study, both deformylase homologues from S. epidermidis (SePDF-1 and SePDF-2) were cloned and expressed, and their enzymic activities were characterized. [Co.sup.2+]-substituted SePDF-1 exhibited much higher enzymic activity ([k.sub.cat]/[K.sub.m] 6.3 x [10.sup.4] [M.sup.-1] [s.sup.-1]) than those of [Ni.sup.2+]-and [Zn.sup.2+]- substituted SePDF-1, and SePDF-1 showed much weaker binding ability towards [Ni.sup.2+] than towards [Co.sup.2+] and [Zn.sup.2+], which is different from PDF in Staphylococcus aureus (SaPDF), although they share 80% amino-acid sequence identity. The determined crystal structure of SePDF-1 was similar to that of (SaPDF), except for differences in the metal-binding sites. The other deformylase homologue, SePDF-2, was shown to have no peptide deformylase activity; the function of SePDF-2 needs to be further investigated. DOI 10.1099/mic.0.038174-0

Published
2010

28. Author Correction: Repopulated microglia are solely derived from the proliferation of residual microglia after acute depletion

Author

Huang, Yubin, primary, Xu, Zhen, additional, Xiong, Shanshan, additional, Sun, Fangfang, additional, Qin, Guangrong, additional, Hu, Guanglei, additional, Wang, Jingjing, additional, Zhao, Lei, additional, Liang, Yu-Xiang, additional, Wu, Tianzhun, additional, Lu, Zhonghua, additional, Humayun, Mark S., additional, So, Kwok-Fai, additional, Pan, Yihang, additional, Li, Ningning, additional, Yuan, Ti-Fei, additional, Rao, Yanxia, additional, and Peng, Bo, additional

Published
2020
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31. Multiomic Immunophenotyping of COVID-19 Patients Reveals Early Infection Trajectories

Author

Su, Yapeng, primary, Chen, Daniel, additional, Lausted, Christopher, additional, Yuan, Dan, additional, Choi, Jongchan, additional, Dai, Cheng, additional, Voillet, Valentin, additional, Scherler, Kelsey, additional, Troisch, Pamela, additional, Duvvuri, Venkata R, additional, Baloni, Priyanka, additional, Qin, Guangrong, additional, Smith, Brett, additional, Kornilov, Sergey, additional, Rostomily, Clifford, additional, Xu, Alex, additional, Li, Jing, additional, Dong, Shen, additional, Rothchild, Alissa, additional, Zhou, Jing, additional, Murray, Kim, additional, Edmark, Rick, additional, Hong, Sunga, additional, Jones, Lesley, additional, Zhou, Yong, additional, Roper, Ryan, additional, Mackay, Sean, additional, O'Mahony, Shane, additional, Dale, Christopher R., additional, Wallick, Julie A., additional, Algren, Heather A., additional, Michael, Zager A., additional, Magis, Andrew, additional, Wei, Wei, additional, Price, Nathan, additional, Huang, Sui, additional, Subramanian, Naeha, additional, Wang, Kai, additional, Hadlock, Jennifer, additional, Hood, Leroy, additional, Aderem, Alan, additional, Bluestone, Jeffrey A., additional, Lanier, Lewis L., additional, Greenberg, Phil, additional, Gottardo, Raphael, additional, Davis, Mark M., additional, Goldman, Jason D, additional, and Heath, James, additional

Published
2020
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33. Kinetic Inference Resolves Epigenetic Mechanism of Drug Resistance in Melanoma

Author

Su, Yapeng, primary, Lu, Xiang, additional, Li, Guideng, additional, Liu, Chunmei, additional, Kong, Yan, additional, Lee, Jihoon W., additional, Ng, Rachel, additional, Wong, Stephanie, additional, Robert, Lidia, additional, Warden, Charles, additional, Liu, Victoria, additional, Chen, Jie, additional, Wang, Zhuo, additional, Yang, Yezi, additional, Cheng, Hanjun, additional, Ng, Alphonsus H. C., additional, Qin, Guangrong, additional, Peng, Songming, additional, Xue, Min, additional, Johnson, Dazy, additional, Xu, Yu, additional, Wang, Jinhui, additional, Wu, Xiwei, additional, Shmulevich, Ilya, additional, Shi, Qihui, additional, Levine, Raphael, additional, Ribas, Antoni, additional, Baltimore, David, additional, Guo, Jun, additional, Heath, James R., additional, and Wei, Wei, additional

Published
2019
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35. Biomolecular Network-Based Synergistic Drug Combination Discovery

Author

Li, Xiangyi, Qin, Guangrong, Yang, Qingmin, Chen, Lanming, and Xie, Lu

Subjects
Article Subject
Abstract

Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing.

Published
2016
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36. Structure and mechanism of the essential two-component signal-transduction system WalKR in Staphylococcus aureus

Author

Ji, Quanjiang, primary, Chen, Peter J., additional, Qin, Guangrong, additional, Deng, Xin, additional, Hao, Ziyang, additional, Wawrzak, Zdzislaw, additional, Yeo, Won-Sik, additional, Quang, Jenny Winjing, additional, Cho, Hoonsik, additional, Luo, Guan-Zheng, additional, Weng, Xiaocheng, additional, You, Qiancheng, additional, Luan, Chi-Hao, additional, Yang, Xiaojing, additional, Bae, Taeok, additional, Yu, Kunqian, additional, Jiang, Hualiang, additional, and He, Chuan, additional

Published
2016
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37. Revealing Missing Human Protein Isoforms Based on Ab Initio Prediction, RNA-seq and Proteomics

Author

Hu, Zhiqiang, primary, Scott, Hamish S., additional, Qin, Guangrong, additional, Zheng, Guangyong, additional, Chu, Xixia, additional, Xie, Lu, additional, Adelson, David L., additional, Oftedal, Bergithe E., additional, Venugopal, Parvathy, additional, Babic, Milena, additional, Hahn, Christopher N., additional, Zhang, Bing, additional, Wang, Xiaojing, additional, Li, Nan, additional, and Wei, Chaochun, additional

Published
2015
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39. Revealing missing isoforms encoded in the human genome by integrating genomic, transcriptomic and proteomic data

Author

Hu, Zhiqiang, primary, Scott, Hamish S., additional, Qin, Guangrong, additional, Zheng, Guangyong, additional, Chu, Xixia, additional, Xie, Lu, additional, Adelson, David L., additional, Oftedal, Bergithe E., additional, Venugopal, Parvalthy, additional, Barbic, Milena, additional, Hahn, Christopher N., additional, Zhang, Bing, additional, Wang, Xiaojing, additional, Li, Nan, additional, and Wei, Chaochun, additional

Published
2014
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47. Targeting the Small- and Intermediate-Conductance Ca-Activated Potassium Channels: The Drug-Binding Pocket at the Channel/Calmodulin Interface.

Author

Cui, Meng, Qin, Guangrong, Yu, Kunqian, Bowers, M. Scott, and Zhang, Miao

Subjects
CALCIUM-dependent potassium channels, EFFECT of drugs on ion channels, CALMODULIN, ATAXIA, ALCOHOL-induced disorders, PARKINSON'S disease
Abstract

The small- and intermediate-conductance Ca2+-activated potassium (SK/IK) channels play important roles in the regulation of excitable cells in both the central nervous and cardiovascular systems. Evidence from animal models has implicated SK/IK channels in neurological conditions such as ataxia and alcohol use disorders. Further, genome-wide association studies have suggested that cardiovascular abnormalities such as arrhythmias and hypertension are associated with single nucleotide polymorphisms that occur within the genes encoding the SK/IK channels. The Ca2+ sensitivity of the SK/IK channels stems from a constitutively bound Ca2+-binding protein: calmodulin. Small-molecule positive modulators of SK/IK channels have been developed over the past decade, and recent structural studies have revealed that the binding pocket of these positive modulators is located at the interface between the channel and calmodulin. SK/IK channel positive modulators can potentiate channel activity by enhancing the coupling between Ca2+ sensing via calmodulin and mechanical opening of the channel. Here, we review binding pocket studies that have provided structural insight into the mechanism of action for SK/IK channel positive modulators. These studies lay the foundation for structure-based drug discovery efforts that can identify novel SK/IK channel positive modulators. © 2014 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Published
2015
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