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Liu X, Zhang Y, Ward LD, Yan Q, Bohnuud T, Hernandez R, Lao S, Yuan J, Fan F. A proteomic platform to identify off-target proteins associated with therapeutic modalities that induce protein degradation or gene silencing. Sci Rep 2021; 11:15856. [PMID: 34349202 PMCID: PMC8338952 DOI: 10.1038/s41598-021-95354-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 07/12/2021] [Indexed: 12/31/2022] Open
Abstract
Novel modalities such as PROTAC and RNAi have the ability to inadvertently alter the abundance of endogenous proteins. Currently available in vitro secondary pharmacology assays, which evaluate off-target binding or activity of small molecules, do not fully assess the off-target effects of PROTAC and are not applicable to RNAi. To address this gap, we developed a proteomics-based platform to comprehensively evaluate the abundance of off-target proteins. First, we selected off-target proteins using genetics and pharmacology evidence. This process yielded 2813 proteins, which we refer to as the “selected off-target proteome” (SOTP). An iterative algorithm was then used to identify four human cell lines out of 932. The 4 cell lines collectively expressed ~ 80% of the SOTP based on transcriptome data. Second, we used mass spectrometry to quantify the intracellular and extracellular proteins from the selected cell lines. Among over 10,000 quantifiable proteins identified, 1828 were part of the predefined SOTP. The SOTP was designed to be easily modified or expanded, owing to the rational selection process developed and the label free LC–MS/MS approach chosen. This versatility inherent to our platform is essential to design fit-for-purpose studies that can address the dynamic questions faced in investigative toxicology.
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Affiliation(s)
- Xin Liu
- Amgen Inc., Translational Safety and Bioanalytical Sciences, 360 Binney St., Cambridge, MA, 02142, USA.,Novartis Institutes for Biomedical Research, 500 Technology Square, Cambridge, MA, 02139, USA
| | - Ye Zhang
- Amgen Inc., Translational Safety and Bioanalytical Sciences, 360 Binney St., Cambridge, MA, 02142, USA.,Novartis Institutes for Biomedical Research, 500 Technology Square, Cambridge, MA, 02139, USA
| | - Lucas D Ward
- Amgen Inc., Translational Safety and Bioanalytical Sciences, 360 Binney St., Cambridge, MA, 02142, USA.,Alnylam Pharmaceuticals, 300 Third St., Cambridge, MA, 02142, USA
| | - Qinghong Yan
- Amgen Inc., Translational Safety and Bioanalytical Sciences, 360 Binney St., Cambridge, MA, 02142, USA.,Fosun Pharma, 104 Carnegie Center Drive, Suite 204, Princeton, NJ, 08540, USA
| | - Tanggis Bohnuud
- Amgen Inc., Translational Safety and Bioanalytical Sciences, 360 Binney St., Cambridge, MA, 02142, USA.,Beam Pharmaceuticals, 26 Landsdowne St., Cambridge, MA, 02139, USA
| | - Rocio Hernandez
- Amgen Inc., Translational Safety and Bioanalytical Sciences, 360 Binney St., Cambridge, MA, 02142, USA.,Amgen Inc., Translational Safety and Bioanalytical Sciences, 1 Amgen Center Dr., Thousand Oaks, CA, 91320, USA
| | - Socheata Lao
- Amgen Inc., Translational Safety and Bioanalytical Sciences, 360 Binney St., Cambridge, MA, 02142, USA.,Amgen Inc., Translational Safety and Bioanalytical Sciences, 1120 Veteran Blvd, South San Francisco, CA, 94080, USA
| | - Jing Yuan
- Amgen Inc., Translational Safety and Bioanalytical Sciences, 360 Binney St., Cambridge, MA, 02142, USA.,Drug Safety Research and Development, Pfizer Inc., 1 Portland St., Cambridge, MA, 02139, USA
| | - Fan Fan
- Amgen Inc., Translational Safety and Bioanalytical Sciences, 360 Binney St., Cambridge, MA, 02142, USA. .,Amgen Inc., Translational Safety and Bioanalytical Sciences, 1120 Veteran Blvd, South San Francisco, CA, 94080, USA.
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2
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Olas B, Urbańska K, Bryś M. Saponins as Modulators of the Blood Coagulation System and Perspectives Regarding Their Use in the Prevention of Venous Thromboembolic Incidents. Molecules 2020; 25:molecules25215171. [PMID: 33172028 PMCID: PMC7664220 DOI: 10.3390/molecules25215171] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Revised: 11/03/2020] [Accepted: 11/04/2020] [Indexed: 01/08/2023] Open
Abstract
Saponins comprise a heterogenous group of chemical compounds containing a triterpene or steroid aglycone group and at least one sugar chain. They exist as secondary metabolites, occurring frequently in dicotyledonous plants and lower marine animals. Plant saponin extracts or single saponins have indicated antiplatelet and anticoagulant activity. Venous thromboembolism (VTE), including deep venous thrombosis and pulmonary embolism, is a multifactorial disease influenced by various patient characteristics such as age, immobility, previous thromboembolism and inherited thrombophilia. This mini-review (1) evaluates the current literature on saponins as modulators of the coagulation system, (2) discusses the impact of chemical structure on the modulation of the coagulation system, which may further provide a basis for drug or supplement design, (3) examines perspectives of their use in the prevention of VTE. It also describes the molecular mechanisms of action of the saponins involved in the prevention of VTE.
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Affiliation(s)
- Beata Olas
- Department of General Biochemistry, Faculty of Biology and Environmental Protection, University of Lodz, Pomorska 141/3, 90-236 Lodz, Poland
- Correspondence: ; Tel./Fax: +48-42-6354485
| | - Karina Urbańska
- Faculty of Medicine, Medical University of Lodz, 90-419 Lodz, Poland;
| | - Magdalena Bryś
- Department of Cytobiochemistry, Faculty of Biology and Environmental Protection, University of Lodz, Pomorska 141/3, 90-236 Lodz, Poland;
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3
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Romano JD, Tatonetti NP. Informatics and Computational Methods in Natural Product Drug Discovery: A Review and Perspectives. Front Genet 2019; 10:368. [PMID: 31114606 PMCID: PMC6503039 DOI: 10.3389/fgene.2019.00368] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Accepted: 04/05/2019] [Indexed: 12/17/2022] Open
Abstract
The discovery of new pharmaceutical drugs is one of the preeminent tasks-scientifically, economically, and socially-in biomedical research. Advances in informatics and computational biology have increased productivity at many stages of the drug discovery pipeline. Nevertheless, drug discovery has slowed, largely due to the reliance on small molecules as the primary source of novel hypotheses. Natural products (such as plant metabolites, animal toxins, and immunological components) comprise a vast and diverse source of bioactive compounds, some of which are supported by thousands of years of traditional medicine, and are largely disjoint from the set of small molecules used commonly for discovery. However, natural products possess unique characteristics that distinguish them from traditional small molecule drug candidates, requiring new methods and approaches for assessing their therapeutic potential. In this review, we investigate a number of state-of-the-art techniques in bioinformatics, cheminformatics, and knowledge engineering for data-driven drug discovery from natural products. We focus on methods that aim to bridge the gap between traditional small-molecule drug candidates and different classes of natural products. We also explore the current informatics knowledge gaps and other barriers that need to be overcome to fully leverage these compounds for drug discovery. Finally, we conclude with a "road map" of research priorities that seeks to realize this goal.
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Affiliation(s)
- Joseph D. Romano
- Department of Biomedical Informatics, Columbia University, New York, NY, United States
- Department of Systems Biology, Columbia University, New York, NY, United States
- Department of Medicine, Columbia University, New York, NY, United States
- Data Science Institute, Columbia University, New York, NY, United States
| | - Nicholas P. Tatonetti
- Department of Biomedical Informatics, Columbia University, New York, NY, United States
- Department of Systems Biology, Columbia University, New York, NY, United States
- Department of Medicine, Columbia University, New York, NY, United States
- Data Science Institute, Columbia University, New York, NY, United States
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4
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Wang D, Eraslan B, Wieland T, Hallström B, Hopf T, Zolg DP, Zecha J, Asplund A, Li LH, Meng C, Frejno M, Schmidt T, Schnatbaum K, Wilhelm M, Ponten F, Uhlen M, Gagneur J, Hahne H, Kuster B. A deep proteome and transcriptome abundance atlas of 29 healthy human tissues. Mol Syst Biol 2019; 15:e8503. [PMID: 30777892 PMCID: PMC6379049 DOI: 10.15252/msb.20188503] [Citation(s) in RCA: 418] [Impact Index Per Article: 83.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 01/01/2019] [Accepted: 01/08/2019] [Indexed: 11/28/2022] Open
Abstract
Genome-, transcriptome- and proteome-wide measurements provide insights into how biological systems are regulated. However, fundamental aspects relating to which human proteins exist, where they are expressed and in which quantities are not fully understood. Therefore, we generated a quantitative proteome and transcriptome abundance atlas of 29 paired healthy human tissues from the Human Protein Atlas project representing human genes by 18,072 transcripts and 13,640 proteins including 37 without prior protein-level evidence. The analysis revealed that hundreds of proteins, particularly in testis, could not be detected even for highly expressed mRNAs, that few proteins show tissue-specific expression, that strong differences between mRNA and protein quantities within and across tissues exist and that protein expression is often more stable across tissues than that of transcripts. Only 238 of 9,848 amino acid variants found by exome sequencing could be confidently detected at the protein level showing that proteogenomics remains challenging, needs better computational methods and requires rigorous validation. Many uses of this resource can be envisaged including the study of gene/protein expression regulation and biomarker specificity evaluation.
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Affiliation(s)
- Dongxue Wang
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
| | - Basak Eraslan
- Computational Biology, Department of Informatics, Technical University of Munich, Garching bei München, Germany
- Department of Biochemistry, Quantitative Biosciences Munich, Gene Center, Ludwig Maximilian Universität, München, Germany
| | | | - Björn Hallström
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | | | - Daniel Paul Zolg
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
| | - Jana Zecha
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
| | - Anna Asplund
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Li-Hua Li
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
| | - Chen Meng
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
| | - Martin Frejno
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
| | - Tobias Schmidt
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
| | | | - Mathias Wilhelm
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
| | - Frederik Ponten
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Julien Gagneur
- Computational Biology, Department of Informatics, Technical University of Munich, Garching bei München, Germany
| | | | - Bernhard Kuster
- Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany
- Center for Integrated Protein Science Munich (CIPSM), Munich, Germany
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5
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Hao Y, Quinnies K, Realubit R, Karan C, Tatonetti NP. Tissue-Specific Analysis of Pharmacological Pathways. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2018; 7:453-463. [PMID: 29920991 PMCID: PMC6063738 DOI: 10.1002/psp4.12305] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Revised: 03/19/2018] [Accepted: 04/11/2018] [Indexed: 01/06/2023]
Abstract
Understanding the downstream consequences of pharmacologically targeted proteins is essential to drug design. Current approaches investigate molecular effects under tissue‐naïve assumptions. Many target proteins, however, have tissue‐specific expression. A systematic study connecting drugs to target pathways in in vivo human tissues is needed. We introduced a data‐driven method that integrates drug‐target relationships with gene expression, protein‐protein interaction, and pathway annotation data. We applied our method to four independent genomewide expression datasets and built 467,396 connections between 1,034 drugs and 954 pathways in 259 human tissues or cell lines. We validated our results using data from L1000 and Pharmacogenomics Knowledgebase (PharmGKB), and observed high precision and recall. We predicted and tested anticoagulant effects of 22 compounds experimentally that were previously unknown, and used clinical data to validate these effects retrospectively. Our systematic study provides a better understanding of the cellular response to drugs and can be applied to many research topics in systems pharmacology.
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Affiliation(s)
- Yun Hao
- Departments of Biomedical Informatics, Systems Biology, and Medicine, Columbia University, New York, New York, USA
| | - Kayla Quinnies
- Departments of Biomedical Informatics, Systems Biology, and Medicine, Columbia University, New York, New York, USA
| | - Ronald Realubit
- Columbia Genome Center, Columbia University, New York, New York, USA
| | - Charles Karan
- Columbia Genome Center, Columbia University, New York, New York, USA
| | - Nicholas P Tatonetti
- Departments of Biomedical Informatics, Systems Biology, and Medicine, Columbia University, New York, New York, USA.,Institute for Genomic Medicine, Columbia University, New York, New York, USA.,Data Science Institute, Columbia University, New York, NY, USA
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6
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Devost D, Sleno R, Pétrin D, Zhang A, Shinjo Y, Okde R, Aoki J, Inoue A, Hébert TE. Conformational Profiling of the AT1 Angiotensin II Receptor Reflects Biased Agonism, G Protein Coupling, and Cellular Context. J Biol Chem 2017; 292:5443-5456. [PMID: 28213525 DOI: 10.1074/jbc.m116.763854] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Revised: 02/03/2017] [Indexed: 12/16/2022] Open
Abstract
Here, we report the design and use of G protein-coupled receptor-based biosensors to monitor ligand-mediated conformational changes in receptors in intact cells. These biosensors use bioluminescence resonance energy transfer with Renilla luciferase (RlucII) as an energy donor, placed at the distal end of the receptor C-tail, and the small fluorescent molecule FlAsH as an energy acceptor, its binding site inserted at different positions throughout the intracellular loops and C-terminal tail of the angiotensin II type I receptor. We verified that the modifications did not compromise receptor localization or function before proceeding further. Our biosensors were able to capture effects of both canonical and biased ligands, even to the extent of discriminating between different biased ligands. Using a combination of G protein inhibitors and HEK 293 cell lines that were CRISPR/Cas9-engineered to delete Gαq, Gα11, Gα12, and Gα13 or β-arrestins, we showed that Gαq and Gα11 are required for functional responses in conformational sensors in ICL3 but not ICL2. Loss of β-arrestin did not alter biased ligand effects on ICL2P2. We also demonstrate that such biosensors are portable between different cell types and yield context-dependent readouts of G protein-coupled receptor conformation. Our study provides mechanistic insights into signaling events that depend on either G proteins or β-arrestin.
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Affiliation(s)
- Dominic Devost
- From the Department of Pharmacology and Therapeutics, McGill University, Montréal, Québec H3G 1Y6, Canada
| | - Rory Sleno
- From the Department of Pharmacology and Therapeutics, McGill University, Montréal, Québec H3G 1Y6, Canada
| | - Darlaine Pétrin
- From the Department of Pharmacology and Therapeutics, McGill University, Montréal, Québec H3G 1Y6, Canada
| | - Alice Zhang
- From the Department of Pharmacology and Therapeutics, McGill University, Montréal, Québec H3G 1Y6, Canada
| | - Yuji Shinjo
- the Graduate School of Pharmaceutical Sciences, Tohoku University, Sendai, Miyagi 980-8578, Japan
| | - Rakan Okde
- From the Department of Pharmacology and Therapeutics, McGill University, Montréal, Québec H3G 1Y6, Canada
| | - Junken Aoki
- the Graduate School of Pharmaceutical Sciences, Tohoku University, Sendai, Miyagi 980-8578, Japan.,the Japan Agency for Medical Research and Development-Core Research for Evolutional Science and Technology (AMED-CREST), Chiyoda-ku, Tokyo 100-0004, Japan, and
| | - Asuka Inoue
- the Graduate School of Pharmaceutical Sciences, Tohoku University, Sendai, Miyagi 980-8578, Japan.,the Precursory Research for Embryonic Science and Technology (PRESTO), Japan Science and Technology Agency (JST), Kawaguchi, Saitama 332-0012, Japan
| | - Terence E Hébert
- From the Department of Pharmacology and Therapeutics, McGill University, Montréal, Québec H3G 1Y6, Canada,
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