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Matić J, Akladios F, Battisti UM, Håversen L, Nain-Perez A, Füchtbauer AF, Kim W, Monjas L, Rivero AR, Borén J, Mardinoglu A, Uhlen M, Grøtli M. Sulfone-based human liver pyruvate kinase inhibitors - Design, synthesis and in vitro bioactivity. Eur J Med Chem 2024; 269:116306. [PMID: 38471358 DOI: 10.1016/j.ejmech.2024.116306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 02/02/2024] [Accepted: 03/04/2024] [Indexed: 03/14/2024]
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a prevalent pathological condition characterised by the accumulation of fat in the liver. Almost one-third of the global population is affected by NAFLD, making it a significant health concern. However, despite its prevalence, there is currently no approved drug specifically designed for the treatment of NAFLD. To address this critical gap, researchers have been investigating potential targets for NAFLD drug development. One promising candidate is the liver isoform of pyruvate kinase (PKL). In recent studies, Urolithin C, an allosteric inhibitor of PKL, has emerged as a potential lead compound for therapeutic intervention. Building upon this knowledge, our team has conducted a comprehensive structure-activity relationship of Urolithin C. In this work, we have employed a scaffold-hopping approach, modifying the urolithin structure by replacing the urolithin carbonyl with a sulfone moiety. Our structure-activity relationship analysis has identified the sulfone group as particularly favourable for potent PKL inhibition. Additionally, we have found that the presence of catechol moieties on the two aromatic rings further improves the inhibitory activity. The most promising inhibitor from this new series displayed nanomolar inhibition, boasting an IC50 value of 0.07 μM. This level of potency rivals that of urolithin D and significantly surpasses the effectiveness of urolithin C by an order of magnitude. To better understand the molecular interactions underlying this inhibition, we obtained the crystal structure of one of the inhibitors complexed with PKL. This structural insight served as a valuable reference point, aiding us in the design of inhibitors.
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Affiliation(s)
- Josipa Matić
- Department of Chemistry and Molecular Biology, University of Gothenburg, SE-412 96, Gothenburg, Sweden; Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Fady Akladios
- Department of Chemistry and Molecular Biology, University of Gothenburg, SE-412 96, Gothenburg, Sweden; Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Umberto Maria Battisti
- Department of Chemistry and Molecular Biology, University of Gothenburg, SE-412 96, Gothenburg, Sweden; Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Liliana Håversen
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, SE-413 45, Gothenburg, Sweden
| | - Amalyn Nain-Perez
- Department of Chemistry and Molecular Biology, University of Gothenburg, SE-412 96, Gothenburg, Sweden; Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Anders Foller Füchtbauer
- Department of Chemistry and Molecular Biology, University of Gothenburg, SE-412 96, Gothenburg, Sweden; Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Woonghee Kim
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Leticia Monjas
- Department of Chemistry and Molecular Biology, University of Gothenburg, SE-412 96, Gothenburg, Sweden; Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Alexandra Rodriguez Rivero
- Department of Chemistry and Molecular Biology, University of Gothenburg, SE-412 96, Gothenburg, Sweden; Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Jan Borén
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, SE-413 45, Gothenburg, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden; Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Morten Grøtli
- Department of Chemistry and Molecular Biology, University of Gothenburg, SE-412 96, Gothenburg, Sweden.
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2
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Uhlen M, Quake SR. Sequential sequencing by synthesis and the next-generation sequencing revolution. Trends Biotechnol 2023; 41:1565-1572. [PMID: 37482467 DOI: 10.1016/j.tibtech.2023.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/11/2023] [Accepted: 06/15/2023] [Indexed: 07/25/2023]
Abstract
The impact of next-generation sequencing (NGS) cannot be overestimated. The technology has transformed the field of life science, contributing to a dramatic expansion in our understanding of human health and disease and our understanding of biology and ecology. The vast majority of the major NGS systems today are based on the concept of 'sequencing by synthesis' (SBS) with sequential detection of nucleotide incorporation using an engineered DNA polymerase. Based on this strategy, various alternative platforms have been developed, including the use of either native nucleotides or reversible terminators and different strategies for the attachment of DNA to a solid support. In this review, some of the key concepts leading to this remarkable development are discussed.
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Affiliation(s)
- Mathias Uhlen
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden; Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden.
| | - Stephen R Quake
- Departments of Bioengineering and Applied Physics, Stanford University, Stanford, CA, USA; Chan Zuckerberg Initiative, Redwood City, California, USA, Stanford, CA, USA
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3
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Chen A, Sun Y, Lei Y, Li C, Liao S, Meng J, Bai Y, Liu Z, Liang Z, Zhu Z, Yuan N, Yang H, Wu Z, Lin F, Wang K, Li M, Zhang S, Yang M, Fei T, Zhuang Z, Huang Y, Zhang Y, Xu Y, Cui L, Zhang R, Han L, Sun X, Chen B, Li W, Huangfu B, Ma K, Ma J, Li Z, Lin Y, Wang H, Zhong Y, Zhang H, Yu Q, Wang Y, Liu X, Peng J, Liu C, Chen W, Pan W, An Y, Xia S, Lu Y, Wang M, Song X, Liu S, Wang Z, Gong C, Huang X, Yuan Y, Zhao Y, Chai Q, Tan X, Liu J, Zheng M, Li S, Huang Y, Hong Y, Huang Z, Li M, Jin M, Li Y, Zhang H, Sun S, Gao L, Bai Y, Cheng M, Hu G, Liu S, Wang B, Xiang B, Li S, Li H, Chen M, Wang S, Li M, Liu W, Liu X, Zhao Q, Lisby M, Wang J, Fang J, Lin Y, Xie Q, Liu Z, He J, Xu H, Huang W, Mulder J, Yang H, Sun Y, Uhlen M, Poo M, Wang J, Yao J, Wei W, Li Y, Shen Z, Liu L, Liu Z, Xu X, Li C. Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex. Cell 2023; 186:3726-3743.e24. [PMID: 37442136 DOI: 10.1016/j.cell.2023.06.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 02/24/2023] [Accepted: 06/14/2023] [Indexed: 07/15/2023]
Abstract
Elucidating the cellular organization of the cerebral cortex is critical for understanding brain structure and function. Using large-scale single-nucleus RNA sequencing and spatial transcriptomic analysis of 143 macaque cortical regions, we obtained a comprehensive atlas of 264 transcriptome-defined cortical cell types and mapped their spatial distribution across the entire cortex. We characterized the cortical layer and region preferences of glutamatergic, GABAergic, and non-neuronal cell types, as well as regional differences in cell-type composition and neighborhood complexity. Notably, we discovered a relationship between the regional distribution of various cell types and the region's hierarchical level in the visual and somatosensory systems. Cross-species comparison of transcriptomic data from human, macaque, and mouse cortices further revealed primate-specific cell types that are enriched in layer 4, with their marker genes expressed in a region-dependent manner. Our data provide a cellular and molecular basis for understanding the evolution, development, aging, and pathogenesis of the primate brain.
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Affiliation(s)
- Ao Chen
- BGI-Shenzhen, Shenzhen 518103, China; Department of Biology, University of Copenhagen, Copenhagen 2200, Denmark; BGI Research-Southwest, BGI, Chongqing 401329, China; JFL-BGI STOmics Center, Jinfeng Laboratory, Chongqing 401329, China
| | - Yidi Sun
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
| | - Ying Lei
- BGI-Shenzhen, Shenzhen 518103, China; BGI-Hangzhou, Hangzhou 310012, China
| | - Chao Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Sha Liao
- BGI-Shenzhen, Shenzhen 518103, China; BGI Research-Southwest, BGI, Chongqing 401329, China; JFL-BGI STOmics Center, Jinfeng Laboratory, Chongqing 401329, China
| | - Juan Meng
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yiqin Bai
- Lingang Laboratory, Shanghai 200031, China
| | - Zhen Liu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhifeng Liang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Nini Yuan
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Hao Yang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zihan Wu
- Tencent AI Lab, Shenzhen 518057, China
| | - Feng Lin
- BGI-Shenzhen, Shenzhen 518103, China
| | - Kexin Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mei Li
- BGI-Shenzhen, Shenzhen 518103, China
| | - Shuzhen Zhang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Tianyi Fei
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhenkun Zhuang
- BGI-Shenzhen, Shenzhen 518103, China; School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Yiming Huang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yong Zhang
- BGI-Shenzhen, Shenzhen 518103, China; School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Yuanfang Xu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Luman Cui
- BGI-Shenzhen, Shenzhen 518103, China
| | - Ruiyi Zhang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Lei Han
- BGI-Shenzhen, Shenzhen 518103, China
| | - Xing Sun
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | | | - Baoqian Huangfu
- BGI-Shenzhen, Shenzhen 518103, China; School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | | | - Jianyun Ma
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhao Li
- BGI-Shenzhen, Shenzhen 518103, China
| | - Yikun Lin
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - He Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yanqing Zhong
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Huifang Zhang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Qian Yu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yaqian Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xing Liu
- BGI-Shenzhen, Shenzhen 518103, China
| | - Jian Peng
- BGI-Shenzhen, Shenzhen 518103, China
| | | | - Wei Chen
- BGI-Shenzhen, Shenzhen 518103, China
| | | | - Yingjie An
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shihui Xia
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yanbing Lu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mingli Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xinxiang Song
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shuai Liu
- BGI-Shenzhen, Shenzhen 518103, China
| | | | - Chun Gong
- BGI-Shenzhen, Shenzhen 518103, China; China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Xin Huang
- BGI-Shenzhen, Shenzhen 518103, China
| | - Yue Yuan
- BGI-Shenzhen, Shenzhen 518103, China
| | - Yun Zhao
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Qinwen Chai
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xing Tan
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jianfeng Liu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mingyuan Zheng
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shengkang Li
- BGI-Shenzhen, Shenzhen 518103, China; Guangdong Bigdata Engineering Technology Research Center for Life Sciences, Shenzhen 518083, China
| | | | - Yan Hong
- BGI-Shenzhen, Shenzhen 518103, China
| | | | - Min Li
- BGI-Shenzhen, Shenzhen 518103, China
| | - Mengmeng Jin
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yan Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Hui Zhang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Suhong Sun
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Li Gao
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yinqi Bai
- BGI-Shenzhen, Shenzhen 518103, China
| | | | - Guohai Hu
- China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Shiping Liu
- BGI-Shenzhen, Shenzhen 518103, China; BGI-Hangzhou, Hangzhou 310012, China
| | - Bo Wang
- China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Bin Xiang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shuting Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Huanhuan Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mengni Chen
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shiwen Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Minglong Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Xin Liu
- BGI-Shenzhen, Shenzhen 518103, China
| | - Qian Zhao
- BGI-Shenzhen, Shenzhen 518103, China
| | - Michael Lisby
- Department of Biology, University of Copenhagen, Copenhagen 2200, Denmark
| | - Jing Wang
- BGI-Shenzhen, Shenzhen 518103, China
| | - Jiao Fang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yun Lin
- BGI-Shenzhen, Shenzhen 518103, China
| | - Qing Xie
- BGI-Shenzhen, Shenzhen 518103, China
| | - Zhen Liu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China
| | - Jie He
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Huatai Xu
- Lingang Laboratory, Shanghai 200031, China
| | - Wei Huang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jan Mulder
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm 17121, Sweden; Department of Neuroscience, Karolinska Institute, Stockholm 17177, Sweden
| | | | - Yangang Sun
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mathias Uhlen
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm 17121, Sweden; Department of Neuroscience, Karolinska Institute, Stockholm 17177, Sweden
| | - Muming Poo
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China
| | - Jian Wang
- BGI-Shenzhen, Shenzhen 518103, China; China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | | | - Wu Wei
- Lingang Laboratory, Shanghai 200031, China; CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
| | - Yuxiang Li
- BGI-Shenzhen, Shenzhen 518103, China; BGI Research-Wuhan, BGI, Wuhan 430074, China.
| | - Zhiming Shen
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China.
| | - Longqi Liu
- BGI-Shenzhen, Shenzhen 518103, China; BGI-Hangzhou, Hangzhou 310012, China.
| | - Zhiyong Liu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China.
| | - Xun Xu
- BGI-Shenzhen, Shenzhen 518103, China; Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen 518120, China.
| | - Chengyu Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China; School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China.
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Yang H, Zhang C, Turkez H, Uhlen M, Boren J, Mardinoglu A. Revisiting the role of serine metabolism in hepatic lipogenesis. Nat Metab 2023; 5:760-761. [PMID: 37169876 DOI: 10.1038/s42255-023-00792-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 03/28/2023] [Indexed: 05/13/2023]
Affiliation(s)
- Hong Yang
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum, Turkey
| | - Mathias Uhlen
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden.
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK.
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Battisti UM, Monjas L, Akladios F, Matic J, Andresen E, Nagel CH, Hagkvist M, Håversen L, Kim W, Uhlen M, Borén J, Mardinoğlu A, Grøtli M. Exploration of Novel Urolithin C Derivatives as Non-Competitive Inhibitors of Liver Pyruvate Kinase. Pharmaceuticals (Basel) 2023; 16:ph16050668. [PMID: 37242451 DOI: 10.3390/ph16050668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 04/25/2023] [Accepted: 04/26/2023] [Indexed: 05/28/2023] Open
Abstract
The inhibition of liver pyruvate kinase could be beneficial to halt or reverse non-alcoholic fatty liver disease (NAFLD), a progressive accumulation of fat in the liver that can lead eventually to cirrhosis. Recently, urolithin C has been reported as a new scaffold for the development of allosteric inhibitors of liver pyruvate kinase (PKL). In this work, a comprehensive structure-activity analysis of urolithin C was carried out. More than 50 analogues were synthesized and tested regarding the chemical features responsible for the desired activity. These data could pave the way to the development of more potent and selective PKL allosteric inhibitors.
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Affiliation(s)
- Umberto Maria Battisti
- Department of Chemistry and Molecular Biology, University of Gothenburg, SE-412 96 Gothenburg, Sweden
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 65 Stockholm, Sweden
| | - Leticia Monjas
- Department of Chemistry and Molecular Biology, University of Gothenburg, SE-412 96 Gothenburg, Sweden
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 65 Stockholm, Sweden
| | - Fady Akladios
- Department of Chemistry and Molecular Biology, University of Gothenburg, SE-412 96 Gothenburg, Sweden
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 65 Stockholm, Sweden
| | - Josipa Matic
- Department of Chemistry and Molecular Biology, University of Gothenburg, SE-412 96 Gothenburg, Sweden
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 65 Stockholm, Sweden
| | - Eric Andresen
- Department of Chemistry and Molecular Biology, University of Gothenburg, SE-412 96 Gothenburg, Sweden
| | - Carolin H Nagel
- Department of Chemistry and Molecular Biology, University of Gothenburg, SE-412 96 Gothenburg, Sweden
| | - Malin Hagkvist
- Department of Chemistry and Molecular Biology, University of Gothenburg, SE-412 96 Gothenburg, Sweden
| | - Liliana Håversen
- Department of Molecular and Clinical Medicine, University of Gothenburg, SE-413 45 Gothenburg, Sweden
- Sahlgrenska University Hospital, SE-413 45 Gothenburg, Sweden
| | - Woonghee Kim
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 65 Stockholm, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 65 Stockholm, Sweden
| | - Jan Borén
- Department of Molecular and Clinical Medicine, University of Gothenburg, SE-413 45 Gothenburg, Sweden
- Sahlgrenska University Hospital, SE-413 45 Gothenburg, Sweden
| | - Adil Mardinoğlu
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 65 Stockholm, Sweden
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London SE1 9RT, UK
| | - Morten Grøtli
- Department of Chemistry and Molecular Biology, University of Gothenburg, SE-412 96 Gothenburg, Sweden
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6
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Allesøe RL, Lundgaard AT, Hernández Medina R, Aguayo-Orozco A, Johansen J, Nissen JN, Brorsson C, Mazzoni G, Niu L, Biel JH, Brasas V, Webel H, Benros ME, Pedersen AG, Chmura PJ, Jacobsen UP, Mari A, Koivula R, Mahajan A, Vinuela A, Tajes JF, Sharma S, Haid M, Hong MG, Musholt PB, De Masi F, Vogt J, Pedersen HK, Gudmundsdottir V, Jones A, Kennedy G, Bell J, Thomas EL, Frost G, Thomsen H, Hansen E, Hansen TH, Vestergaard H, Muilwijk M, Blom MT, 't Hart LM, Pattou F, Raverdy V, Brage S, Kokkola T, Heggie A, McEvoy D, Mourby M, Kaye J, Hattersley A, McDonald T, Ridderstråle M, Walker M, Forgie I, Giordano GN, Pavo I, Ruetten H, Pedersen O, Hansen T, Dermitzakis E, Franks PW, Schwenk JM, Adamski J, McCarthy MI, Pearson E, Banasik K, Rasmussen S, Brunak S, Thomas CE, Haussler R, Beulens J, Rutters F, Nijpels G, van Oort S, Groeneveld L, Elders P, Giorgino T, Rodriquez M, Nice R, Perry M, Bianzano S, Graefe-Mody U, Hennige A, Grempler R, Baum P, Stærfeldt HH, Shah N, Teare H, Ehrhardt B, Tillner J, Dings C, Lehr T, Scherer N, Sihinevich I, Cabrelli L, Loftus H, Bizzotto R, Tura A, Dekkers K, van Leeuwen N, Groop L, Slieker R, Ramisch A, Jennison C, McVittie I, Frau F, Steckel-Hamann B, Adragni K, Thomas M, Pasdar NA, Fitipaldi H, Kurbasic A, Mutie P, Pomares-Millan H, Bonnefond A, Canouil M, Caiazzo R, Verkindt H, Holl R, Kuulasmaa T, Deshmukh H, Cederberg H, Laakso M, Vangipurapu J, Dale M, Thorand B, Nicolay C, Fritsche A, Hill A, Hudson M, Thorne C, Allin K, Arumugam M, Jonsson A, Engelbrechtsen L, Forman A, Dutta A, Sondertoft N, Fan Y, Gough S, Robertson N, McRobert N, Wesolowska-Andersen A, Brown A, Davtian D, Dawed A, Donnelly L, Palmer C, White M, Ferrer J, Whitcher B, Artati A, Prehn C, Adam J, Grallert H, Gupta R, Sackett PW, Nilsson B, Tsirigos K, Eriksen R, Jablonka B, Uhlen M, Gassenhuber J, Baltauss T, de Preville N, Klintenberg M, Abdalla M. Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models. Nat Biotechnol 2023; 41:399-408. [PMID: 36593394 PMCID: PMC10017515 DOI: 10.1038/s41587-022-01520-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 09/20/2022] [Indexed: 01/03/2023]
Abstract
The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.
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Affiliation(s)
- Rosa Lundbye Allesøe
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.,Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Agnete Troen Lundgaard
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Ricardo Hernández Medina
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Alejandro Aguayo-Orozco
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Joachim Johansen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Jakob Nybo Nissen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Caroline Brorsson
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Gianluca Mazzoni
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Lili Niu
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jorge Hernansanz Biel
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Valentas Brasas
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Henry Webel
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Michael Eriksen Benros
- Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark.,Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Anders Gorm Pedersen
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Piotr Jaroslaw Chmura
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Ulrik Plesner Jacobsen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Andrea Mari
- C.N.R. Institute of Neuroscience, Padova, Italy
| | - Robert Koivula
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Ana Vinuela
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.,Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, UK
| | | | - Sapna Sharma
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, Germany.,Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, Germany.,Chair of Food Chemistry and Molecular and Sensory Science, Technical University of Munich, Freising, Germany
| | - Mark Haid
- Metabolomics and Proteomics Core, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Neuherberg, Germany
| | - Mun-Gwan Hong
- Affinity Proteomics, Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden
| | - Petra B Musholt
- Research and Development Global Development, Translational Medicine and Clinical Pharmacology, Sanofi-Aventis Deutschland, Frankfurt, Germany
| | - Federico De Masi
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Josef Vogt
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Helle Krogh Pedersen
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.,Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Valborg Gudmundsdottir
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Angus Jones
- University of Exeter Medical School, Exeter, UK
| | - Gwen Kennedy
- The Immunoassay Biomarker Core Laboratory, School of Medicine, University of Dundee, Dundee, UK
| | - Jimmy Bell
- Research Centre for Optimal Health, Department of Life Sciences, University of Westminster, London, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, Department of Life Sciences, University of Westminster, London, UK
| | - Gary Frost
- Section for Nutrition Research, Faculty of Medicine, Imperial College London, London, UK
| | - Henrik Thomsen
- Department of Radiology, Copenhagen University Hospital Herlev-Gentofte, Herlev, Denmark
| | - Elizaveta Hansen
- Department of Radiology, Copenhagen University Hospital Herlev-Gentofte, Herlev, Denmark
| | - Tue Haldor Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Henrik Vestergaard
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mirthe Muilwijk
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Marieke T Blom
- Department of General Practice, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Leen M 't Hart
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.,Department of Biomedical Data Science, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.,Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Francois Pattou
- Inserm, Univ Lille, CHU Lille, Lille Pasteur Institute, EGID, Lille, France
| | - Violeta Raverdy
- Inserm, Univ Lille, CHU Lille, Lille Pasteur Institute, EGID, Lille, France
| | - Soren Brage
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Tarja Kokkola
- Department of Medicine, University of Eastern Finland, Kuopio, Finland
| | - Alison Heggie
- Institute of Cellular Medicine, Newcastle University, Newcastle, UK
| | - Donna McEvoy
- Diabetes Research Network, Royal Victoria Infirmary, Newcastle, UK
| | - Miranda Mourby
- Centre for Health, Law and Emerging Technologies (HeLEX), Faculty of Law, University of Oxford, Oxford, UK
| | - Jane Kaye
- Centre for Health, Law and Emerging Technologies (HeLEX), Faculty of Law, University of Oxford, Oxford, UK
| | | | | | - Martin Ridderstråle
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Mark Walker
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, UK
| | - Ian Forgie
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Giuseppe N Giordano
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, CRC, Lund University, SUS, Malmö, Sweden
| | - Imre Pavo
- Eli Lilly Regional Operations, Vienna, Austria
| | - Hartmut Ruetten
- Research and Development Global Development, Translational Medicine and Clinical Pharmacology, Sanofi-Aventis Deutschland, Frankfurt, Germany
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Emmanouil Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Paul W Franks
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden.,Harvard T.H. Chan School of Public Health, Boston, MA, USA.,OCDEM, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Jochen M Schwenk
- Affinity Proteomics, Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.,Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.,Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK.,Genentech, South San Francisco, CA, USA
| | - Ewan Pearson
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Karina Banasik
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Simon Rasmussen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. .,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.
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7
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Ezzamouri B, Rosario D, Bidkhori G, Lee S, Uhlen M, Shoaie S. Metabolic modelling of the human gut microbiome in type 2 diabetes patients in response to metformin treatment. NPJ Syst Biol Appl 2023; 9:2. [PMID: 36681701 PMCID: PMC9867701 DOI: 10.1038/s41540-022-00261-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 11/08/2022] [Indexed: 01/22/2023] Open
Abstract
The human gut microbiome has been associated with several metabolic disorders including type 2 diabetes mellitus. Understanding metabolic changes in the gut microbiome is important to elucidate the role of gut bacteria in regulating host metabolism. Here, we used available metagenomics data from a metformin study, together with genome-scale metabolic modelling of the key bacteria in individual and community-level to investigate the mechanistic role of the gut microbiome in response to metformin. Individual modelling predicted that species that are increased after metformin treatment have higher growth rates in comparison to species that are decreased after metformin treatment. Gut microbial enrichment analysis showed prior to metformin treatment pathways related to the hypoglycemic effect were enriched. Our observations highlight how the key bacterial species after metformin treatment have commensal and competing behavior, and how their cellular metabolism changes due to different nutritional environment. Integrating different diets showed there were specific microbial alterations between different diets. These results show the importance of the nutritional environment and how dietary guidelines may improve drug efficiency through the gut microbiota.
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Affiliation(s)
- Bouchra Ezzamouri
- grid.13097.3c0000 0001 2322 6764Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, SE1 9RT London, UK ,grid.420545.20000 0004 0489 3985Unit for Population-Based Dermatology, St John’s Institute of Dermatology, King’s College London and Guy’s and St Thomas’ NHS Foundation Trust, London, UK
| | - Dorines Rosario
- grid.13097.3c0000 0001 2322 6764Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, SE1 9RT London, UK
| | - Gholamreza Bidkhori
- grid.13097.3c0000 0001 2322 6764Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, SE1 9RT London, UK ,Present Address: AIVIVO Ltd. Unit 25, Bio-innovation centre, Cambridge Science Park, Cambridge, UK
| | - Sunjae Lee
- grid.13097.3c0000 0001 2322 6764Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, SE1 9RT London, UK
| | - Mathias Uhlen
- grid.5037.10000000121581746Science for Life Laboratory, KTH - Royal Institute of Technology, 171 21 Stockholm, Sweden
| | - Saeed Shoaie
- grid.13097.3c0000 0001 2322 6764Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, SE1 9RT London, UK ,grid.5037.10000000121581746Science for Life Laboratory, KTH - Royal Institute of Technology, 171 21 Stockholm, Sweden
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8
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Battisti UM, Gao C, Nilsson O, Akladios F, Lulla A, Bogucka A, Nain-Perez A, Håversen L, Kim W, Boren J, Hyvönen M, Uhlen M, Mardinoglu A, Grøtli M. Serendipitous Identification of a Covalent Activator of Liver Pyruvate Kinase. Chembiochem 2023; 24:e202200339. [PMID: 36250581 PMCID: PMC10099687 DOI: 10.1002/cbic.202200339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 10/14/2022] [Indexed: 01/05/2023]
Abstract
Enzymes are effective biological catalysts that accelerate almost all metabolic reactions in living organisms. Synthetic modulators of enzymes are useful tools for the study of enzymatic reactions and can provide starting points for the design of new drugs. Here, we report on the discovery of a class of biologically active compounds that covalently modifies lysine residues in human liver pyruvate kinase (PKL), leading to allosteric activation of the enzyme (EC50 =0.29 μM). Surprisingly, the allosteric activation control point resides on the lysine residue K282 present in the catalytic site of PKL. These findings were confirmed by structural data, MS/MS experiments, and molecular modelling studies. Altogether, our study provides a molecular basis for the activation mechanism and establishes a framework for further development of human liver pyruvate kinase covalent activators.
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Affiliation(s)
- Umberto Maria Battisti
- Department of Chemistry and Molecular Biology, University of Gothenburg, 412 96, Gothenburg, Sweden
| | - Chunxia Gao
- Department of Chemistry and Molecular Biology, University of Gothenburg, 412 96, Gothenburg, Sweden
| | - Oscar Nilsson
- Department of Chemistry and Molecular Biology, University of Gothenburg, 412 96, Gothenburg, Sweden
| | - Fady Akladios
- Department of Chemistry and Molecular Biology, University of Gothenburg, 412 96, Gothenburg, Sweden
| | - Aleksei Lulla
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, UK
| | - Agnieszka Bogucka
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, UK
| | - Amalyn Nain-Perez
- Department of Chemistry and Molecular Biology, University of Gothenburg, 412 96, Gothenburg, Sweden
| | - Liliana Håversen
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden
| | - Woonghee Kim
- Science for Life Laboratory, KTH-Royal Institute of Technology, 171 21, Stockholm, Sweden
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden
| | - Marko Hyvönen
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, UK
| | - Mathias Uhlen
- Science for Life Laboratory, KTH-Royal Institute of Technology, 171 21, Stockholm, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH-Royal Institute of Technology, 171 21, Stockholm, Sweden
| | - Morten Grøtli
- Department of Chemistry and Molecular Biology, University of Gothenburg, 412 96, Gothenburg, Sweden
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9
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Battisti UM, Gao C, Nilsson O, Akladios F, Lulla A, Bogucka A, Nain‐Perez A, Håversen L, Kim W, Boren J, Hyvönen M, Uhlen M, Mardinoglu A, Grøtli M. Serendipitous Identification of a Covalent Activator of Liver Pyruvate Kinase. Chembiochem 2022. [DOI: 10.1002/cbic.202200699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Umberto Maria Battisti
- Department of Chemistry and Molecular Biology University of Gothenburg Gothenburg 412 96 Sweden
| | - Chunxia Gao
- Department of Chemistry and Molecular Biology University of Gothenburg Gothenburg 412 96 Sweden
| | - Oscar Nilsson
- Department of Chemistry and Molecular Biology University of Gothenburg Gothenburg 412 96 Sweden
| | - Fady Akladios
- Department of Chemistry and Molecular Biology University of Gothenburg Gothenburg 412 96 Sweden
| | - Aleksei Lulla
- Department of Biochemistry University of Cambridge Cambridge CB2 1GA UK
| | - Agnieszka Bogucka
- Department of Biochemistry University of Cambridge Cambridge CB2 1GA UK
| | - Amalyn Nain‐Perez
- Department of Chemistry and Molecular Biology University of Gothenburg Gothenburg 412 96 Sweden
| | - Liliana Håversen
- Department of Molecular and Clinical Medicine University of Gothenburg and Sahlgrenska University Hospital 413 45 Gothenburg Sweden
| | - Woonghee Kim
- Science for Life Laboratory KTH-Royal Institute of Technology 171 21 Stockholm Sweden
| | - Jan Boren
- Department of Molecular and Clinical Medicine University of Gothenburg and Sahlgrenska University Hospital 413 45 Gothenburg Sweden
| | - Marko Hyvönen
- Department of Biochemistry University of Cambridge Cambridge CB2 1GA UK
| | - Mathias Uhlen
- Science for Life Laboratory KTH-Royal Institute of Technology 171 21 Stockholm Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory KTH-Royal Institute of Technology 171 21 Stockholm Sweden
| | - Morten Grøtli
- Department of Chemistry and Molecular Biology University of Gothenburg Gothenburg 412 96 Sweden
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10
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Wang F, Ding P, Liang X, Ding X, Brandt CB, Sjöstedt E, Zhu J, Bolund S, Zhang L, de Rooij LPMH, Luo L, Wei Y, Zhao W, Lv Z, Haskó J, Li R, Qin Q, Jia Y, Wu W, Yuan Y, Pu M, Wang H, Wu A, Xie L, Liu P, Chen F, Herold J, Kalucka J, Karlsson M, Zhang X, Helmig RB, Fagerberg L, Lindskog C, Pontén F, Uhlen M, Bolund L, Jessen N, Jiang H, Xu X, Yang H, Carmeliet P, Mulder J, Chen D, Lin L, Luo Y. Author Correction: Endothelial cell heterogeneity and microglia regulons revealed by a pig cell landscape at single-cell level. Nat Commun 2022; 13:6748. [DOI: 10.1038/s41467-022-34498-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
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11
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Begum N, Lee S, Portlock TJ, Pellon A, Nasab SDS, Nielsen J, Uhlen M, Moyes DL, Shoaie S. Integrative functional analysis uncovers metabolic differences between Candida species. Commun Biol 2022; 5:1013. [PMID: 36163459 PMCID: PMC9512779 DOI: 10.1038/s42003-022-03955-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 09/07/2022] [Indexed: 12/02/2022] Open
Abstract
Candida species are a dominant constituent of the human mycobiome and associated with the development of several diseases. Understanding the Candida species metabolism could provide key insights into their ability to cause pathogenesis. Here, we have developed the BioFung database, providing an efficient annotation of protein-encoding genes. Along, with BioFung, using carbohydrate-active enzyme (CAZymes) analysis, we have uncovered core and accessory features across Candida species demonstrating plasticity, adaption to the environment and acquired features. We show a greater importance of amino acid metabolism, as functional analysis revealed that all Candida species can employ amino acid metabolism. However, metabolomics revealed that only a specific cluster of species (AGAu species—C. albicans, C. glabrata and C. auris) utilised amino acid metabolism including arginine, cysteine, and methionine metabolism potentially improving their competitive fitness in pathogenesis. We further identified critical metabolic pathways in the AGAu cluster with biomarkers and anti-fungal target potential in the CAZyme profile, polyamine, choline and fatty acid biosynthesis pathways. This study, combining genomic analysis, and validation with gene expression and metabolomics, highlights the metabolic diversity with AGAu species that underlies their remarkable ability to dominate they mycobiome and cause disease. Metabolic differences between Candida species are uncovered using the BioFung database alongside genomic and metabolic analysis.
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Affiliation(s)
- Neelu Begum
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, London, UK
| | - Sunjae Lee
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, London, UK
| | - Theo John Portlock
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Aize Pellon
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, London, UK
| | - Shervin Dokht Sadeghi Nasab
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, London, UK
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Kemivägen 10, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden.,BioInnovation Institute, Ole Maaløes Vej 3, DK2200, Copenhagen N, Denmark
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - David L Moyes
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, London, UK.
| | - Saeed Shoaie
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, London, UK. .,Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden.
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12
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Proffitt C, Bidkhori G, Lee S, Tebani A, Mardinoglu A, Uhlen M, Moyes DL, Shoaie S. Genome-scale metabolic modelling of the human gut microbiome reveals changes of the glyoxylate and dicarboxylate metabolism in metabolic disorders. iScience 2022; 25:104513. [PMID: 35754734 PMCID: PMC9213702 DOI: 10.1016/j.isci.2022.104513] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 03/14/2022] [Accepted: 05/27/2022] [Indexed: 11/20/2022] Open
Abstract
The human gut microbiome has been associated with metabolic disorders including obesity, type 2 diabetes, and atherosclerosis. Understanding the contribution of microbiome metabolic changes is important for elucidating the role of gut bacteria in regulating metabolism. We used available metagenomics data from these metabolic disorders, together with genome-scale metabolic modeling of key bacteria in the individual and community-level to investigate the mechanistic role of the gut microbiome in metabolic diseases. Modeling predicted increased levels of glutamate consumption along with the production of ammonia, arginine, and proline in gut bacteria common across the disorders. Abundance profiles and network-dependent analysis identified the enrichment of tartrate dehydrogenase in the disorders. Moreover, independent plasma metabolite levels showed associations between metabolites including proline and tyrosine and an increased tartrate metabolism in healthy obese individuals. We, therefore, propose that an increased tartrate metabolism could be a significant mediator of the microbiome metabolic changes in metabolic disorders. Metagenomic analysis highlights key common bacterial species across metabolic diseases Metabolic models showed higher levels of acetate produced by disease enriched bacteria Reaction analysis revealed increases in the glyoxylate and dicarboxylate pathway Metabolomics and modeling analysis showed the potential role of tartrate metabolism
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Affiliation(s)
- Ceri Proffitt
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
| | - Gholamreza Bidkhori
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
| | - Sunjae Lee
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
| | - Abdellah Tebani
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Adil Mardinoglu
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - David L. Moyes
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
| | - Saeed Shoaie
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
- Corresponding author
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13
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Han L, Wei X, Liu C, Volpe G, Zhuang Z, Zou X, Wang Z, Pan T, Yuan Y, Zhang X, Fan P, Guo P, Lai Y, Lei Y, Liu X, Yu F, Shangguan S, Lai G, Deng Q, Liu Y, Wu L, Shi Q, Yu H, Huang Y, Cheng M, Xu J, Liu Y, Wang M, Wang C, Zhang Y, Xie D, Yang Y, Yu Y, Zheng H, Wei Y, Huang F, Lei J, Huang W, Zhu Z, Lu H, Wang B, Wei X, Chen F, Yang T, Du W, Chen J, Xu S, An J, Ward C, Wang Z, Pei Z, Wong CW, Liu X, Zhang H, Liu M, Qin B, Schambach A, Isern J, Feng L, Liu Y, Guo X, Liu Z, Sun Q, Maxwell PH, Barker N, Muñoz-Cánoves P, Gu Y, Mulder J, Uhlen M, Tan T, Liu S, Yang H, Wang J, Hou Y, Xu X, Esteban MA, Liu L. Cell transcriptomic atlas of the non-human primate Macaca fascicularis. Nature 2022; 604:723-731. [PMID: 35418686 DOI: 10.1038/s41586-022-04587-3] [Citation(s) in RCA: 58] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 02/23/2022] [Indexed: 12/22/2022]
Abstract
Studying tissue composition and function in non-human primates (NHPs) is crucial to understand the nature of our own species. Here we present a large-scale cell transcriptomic atlas that encompasses over 1 million cells from 45 tissues of the adult NHP Macaca fascicularis. This dataset provides a vast annotated resource to study a species phylogenetically close to humans. To demonstrate the utility of the atlas, we have reconstructed the cell-cell interaction networks that drive Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases, and intersected our data with human genetic disease orthologues to establish potential clinical associations. Our M. fascicularis cell atlas constitutes an essential reference for future studies in humans and NHPs.
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Affiliation(s)
- Lei Han
- BGI-Shenzhen, Shenzhen, China.,BGI-Beijing, Beijing, China.,Shenzhen Bay Laboratory, Shenzhen, China
| | - Xiaoyu Wei
- BGI-Shenzhen, Shenzhen, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Chuanyu Liu
- BGI-Shenzhen, Shenzhen, China.,BGI-Beijing, Beijing, China.,Shenzhen Bay Laboratory, Shenzhen, China
| | - Giacomo Volpe
- Hematology and Cell Therapy Unit, IRCCS-Istituto Tumori 'Giovanni Paolo II', Bari, Italy
| | - Zhenkun Zhuang
- BGI-Shenzhen, Shenzhen, China.,School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Xuanxuan Zou
- BGI-Shenzhen, Shenzhen, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Zhifeng Wang
- BGI-Shenzhen, Shenzhen, China.,BGI-Beijing, Beijing, China
| | - Taotao Pan
- BGI-Shenzhen, Shenzhen, China.,BGI-Beijing, Beijing, China
| | - Yue Yuan
- BGI-Shenzhen, Shenzhen, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Xiao Zhang
- State Key Laboratory for Zoonotic Diseases, Key Laboratory for Zoonosis Research of Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, China
| | - Peng Fan
- State Key Laboratory for Zoonotic Diseases, Key Laboratory for Zoonosis Research of Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, China
| | - Pengcheng Guo
- State Key Laboratory for Zoonotic Diseases, Key Laboratory for Zoonosis Research of Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, China
| | - Yiwei Lai
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Ying Lei
- BGI-Shenzhen, Shenzhen, China.,BGI-Beijing, Beijing, China.,Shenzhen Bay Laboratory, Shenzhen, China
| | - Xingyuan Liu
- State Key Laboratory for Zoonotic Diseases, Key Laboratory for Zoonosis Research of Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, China
| | - Feng Yu
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Shuncheng Shangguan
- Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health and Guangzhou Medical University, Guangzhou, China
| | - Guangyao Lai
- Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health and Guangzhou Medical University, Guangzhou, China
| | - Qiuting Deng
- BGI-Shenzhen, Shenzhen, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Ya Liu
- BGI-Shenzhen, Shenzhen, China.,BGI-Beijing, Beijing, China
| | - Liang Wu
- BGI-Shenzhen, Shenzhen, China.,BGI-Beijing, Beijing, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Quan Shi
- BGI-Shenzhen, Shenzhen, China.,Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Hao Yu
- BGI-Shenzhen, Shenzhen, China
| | - Yunting Huang
- BGI-Shenzhen, Shenzhen, China.,China National GeneBank, BGI-Shenzhen, Shenzhen, China
| | - Mengnan Cheng
- BGI-Shenzhen, Shenzhen, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Jiangshan Xu
- BGI-Shenzhen, Shenzhen, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Yang Liu
- BGI-Shenzhen, Shenzhen, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | | | - Chunqing Wang
- BGI-Shenzhen, Shenzhen, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Yuanhang Zhang
- BGI-Shenzhen, Shenzhen, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Duo Xie
- BGI-Shenzhen, Shenzhen, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Yunzhi Yang
- BGI College and Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
| | - Yeya Yu
- BGI College and Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
| | - Huiwen Zheng
- BGI College and Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
| | - Yanrong Wei
- BGI College and Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
| | - Fubaoqian Huang
- BGI-Shenzhen, Shenzhen, China.,School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Junjie Lei
- BGI-Shenzhen, Shenzhen, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Waidong Huang
- BGI-Shenzhen, Shenzhen, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Zhiyong Zhu
- BGI-Shenzhen, Shenzhen, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Haorong Lu
- BGI-Shenzhen, Shenzhen, China.,China National GeneBank, BGI-Shenzhen, Shenzhen, China
| | - Bo Wang
- BGI-Shenzhen, Shenzhen, China.,China National GeneBank, BGI-Shenzhen, Shenzhen, China
| | - Xiaofeng Wei
- BGI-Shenzhen, Shenzhen, China.,China National GeneBank, BGI-Shenzhen, Shenzhen, China
| | - Fengzhen Chen
- BGI-Shenzhen, Shenzhen, China.,China National GeneBank, BGI-Shenzhen, Shenzhen, China
| | - Tao Yang
- BGI-Shenzhen, Shenzhen, China.,China National GeneBank, BGI-Shenzhen, Shenzhen, China
| | - Wensi Du
- BGI-Shenzhen, Shenzhen, China.,China National GeneBank, BGI-Shenzhen, Shenzhen, China
| | - Jing Chen
- BGI-Shenzhen, Shenzhen, China.,China National GeneBank, BGI-Shenzhen, Shenzhen, China
| | - Shibo Xu
- Institute for Stem Cells and Neural Regeneration, School of Pharmacy, State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
| | - Juan An
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,University of Science and Technology of China, Hefei, China
| | - Carl Ward
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Zongren Wang
- Department of Urology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhong Pei
- Department of Neurology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | | | - Xiaolei Liu
- State Key Laboratory for Zoonotic Diseases, Key Laboratory for Zoonosis Research of Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, China
| | - Huafeng Zhang
- Department of Orthopedics, Tianjin Medical University General Hospital, Tianjin, China
| | - Mingyuan Liu
- State Key Laboratory for Zoonotic Diseases, Key Laboratory for Zoonosis Research of Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, China
| | - Baoming Qin
- Laboratory of Metabolism and Cell Fate, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Axel Schambach
- Institute of Experimental Hematology, Hannover Medical School, Hannover, Germany.,Division of Hematology/Oncology, Harvard Medical School, MA, Boston, USA
| | - Joan Isern
- Spanish National Center for Cardiovascular Research (CNIC), Madrid, Spain
| | - Liqiang Feng
- State Key Laboratory of Respiratory Diseases, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Yan Liu
- Institute for Stem Cells and Neural Regeneration, School of Pharmacy, State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
| | - Xiangyu Guo
- Jinan University, Guangzhou, China.,Hubei Topgene Biotechnology Co., Ltd, Wuhan, China
| | - Zhen Liu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Qiang Sun
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Patrick H Maxwell
- Cambridge Institute for Medical Research, Department of Medicine, University of Cambridge, Cambridge, UK
| | - Nick Barker
- A*STAR Institute of Molecular and Cell Biology, Singapore, Singapore
| | - Pura Muñoz-Cánoves
- Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), ICREA and CIBERNED, Barcelona, Spain
| | - Ying Gu
- BGI-Shenzhen, Shenzhen, China
| | - Jan Mulder
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden.,Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Mathias Uhlen
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden.,Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Tao Tan
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China
| | - Shiping Liu
- BGI-Shenzhen, Shenzhen, China.,BGI-Beijing, Beijing, China.,Shenzhen Bay Laboratory, Shenzhen, China
| | - Huanming Yang
- BGI-Shenzhen, Shenzhen, China.,James D. Watson Institute of Genome Sciences, Hangzhou, China
| | - Jian Wang
- BGI-Shenzhen, Shenzhen, China.,James D. Watson Institute of Genome Sciences, Hangzhou, China
| | - Yong Hou
- BGI-Shenzhen, Shenzhen, China. .,BGI-Beijing, Beijing, China. .,Shenzhen Bay Laboratory, Shenzhen, China. .,BGI College and Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.
| | - Xun Xu
- BGI-Shenzhen, Shenzhen, China. .,BGI-Beijing, Beijing, China. .,BGI College and Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China. .,Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen, China.
| | - Miguel A Esteban
- State Key Laboratory for Zoonotic Diseases, Key Laboratory for Zoonosis Research of Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, China. .,Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China. .,Institute of Stem Cells and Regeneration, Chinese Academy of Sciences, Beijing, China.
| | - Longqi Liu
- BGI-Shenzhen, Shenzhen, China. .,BGI-Beijing, Beijing, China. .,Shenzhen Bay Laboratory, Shenzhen, China. .,BGI College and Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.
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14
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Li X, Shong K, Kim W, Yuan M, Yang H, Sato Y, Kume H, Ogawa S, Turkez H, Shoaie S, Boren J, Nielsen J, Uhlen M, Zhang C, Mardinoglu A. Prediction of drug candidates for clear cell renal cell carcinoma using a systems biology-based drug repositioning approach. EBioMedicine 2022; 78:103963. [PMID: 35339898 PMCID: PMC8960981 DOI: 10.1016/j.ebiom.2022.103963] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 03/09/2022] [Accepted: 03/09/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND The response rates of the clinical chemotherapies are still low in clear cell renal cell carcinoma (ccRCC). Computational drug repositioning is a promising strategy to discover new uses for existing drugs to treat patients who cannot get benefits from clinical drugs. METHODS We proposed a systematic approach which included the target prediction based on the co-expression network analysis of transcriptomics profiles of ccRCC patients and drug repositioning for cancer treatment based on the analysis of shRNA- and drug-perturbed signature profiles of human kidney cell line. FINDINGS First, based on the gene co-expression network analysis, we identified two types of gene modules in ccRCC, which significantly enriched with unfavorable and favorable signatures indicating poor and good survival outcomes of patients, respectively. Then, we selected four genes, BUB1B, RRM2, ASF1B and CCNB2, as the potential drug targets based on the topology analysis of modules. Further, we repurposed three most effective drugs for each target by applying the proposed drug repositioning approach. Finally, we evaluated the effects of repurposed drugs using an in vitro model and observed that these drugs inhibited the protein levels of their corresponding target genes and cell viability. INTERPRETATION These findings proved the usefulness and efficiency of our approach to improve the drug repositioning researches for cancer treatment and precision medicine. FUNDING This study was funded by Knut and Alice Wallenberg Foundation and Bash Biotech Inc., San Diego, CA, USA.
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Affiliation(s)
- Xiangyu Li
- Bash Biotech Inc, 600 est Broadway, Suite 700, San Diego, CA 92101, USA; Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden
| | - Koeun Shong
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden
| | - Woonghee Kim
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden
| | - Meng Yuan
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden
| | - Hong Yang
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden
| | - Yusuke Sato
- Department of Pathology and Tumor Biology, Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto 606-8501, Japan; Department of Urology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8654, Japan
| | - Haruki Kume
- Department of Urology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8654, Japan
| | - Seishi Ogawa
- Department of Pathology and Tumor Biology, Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto 606-8501, Japan; Centre for Hematology and Regenerative Medicine, Department of Medicine, Karolinska Institute, Stockholm SE-17177, Sweden
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum 25240, Turkey
| | - Saeed Shoaie
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden; Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London SE1 9RT, UK
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg SE-41345, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg SE-41296, Sweden; BioInnovation Institute, Copenhagen N DK-2200, Denmark
| | - Mathias Uhlen
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden; Key Laboratory of Advanced Drug Preparation Technologies, School of Pharmaceutical Sciences, Ministry of Education, Zhengzhou University, Zhengzhou 450001, China.
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden; Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London SE1 9RT, UK.
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15
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Yuan M, Shong K, Li X, Ashraf S, Shi M, Kim W, Nielsen J, Turkez H, Shoaie S, Uhlen M, Zhang C, Mardinoglu A. A Gene Co-Expression Network-Based Drug Repositioning Approach Identifies Candidates for Treatment of Hepatocellular Carcinoma. Cancers (Basel) 2022; 14:cancers14061573. [PMID: 35326724 PMCID: PMC8946504 DOI: 10.3390/cancers14061573] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 03/15/2022] [Accepted: 03/16/2022] [Indexed: 12/21/2022] Open
Abstract
Simple Summary Hepatocellular carcinoma (HCC) is the most common malignancy of liver cancer. However, treatment of HCC is still severely limited due to limitation of drug therapy. We aimed to screen more possible target genes and candidate drugs for HCC, exploring the possibility of drug treatments from systems biological perspective. We identified ten candidate target genes, which are hub genes in HCC co-expression networks, which also possess significant prognostic value in two independent HCC cohorts. The rationality of these target genes was well demonstrated through variety analyses of patient expression profiles. We then screened candidate drugs for target genes and finally identified withaferin-a and mitoxantrone as the candidate drug for HCC treatment. The drug effectiveness was validated in in vitro model and computational analysis, providing more evidence for our drug repositioning method and results. Abstract Hepatocellular carcinoma (HCC) is a malignant liver cancer that continues to increase deaths worldwide owing to limited therapies and treatments. Computational drug repurposing is a promising strategy to discover potential indications of existing drugs. In this study, we present a systematic drug repositioning method based on comprehensive integration of molecular signatures in liver cancer tissue and cell lines. First, we identify robust prognostic genes and two gene co-expression modules enriched in unfavorable prognostic genes based on two independent HCC cohorts, which showed great consistency in functional and network topology. Then, we screen 10 genes as potential target genes for HCC on the bias of network topology analysis in these two modules. Further, we perform a drug repositioning method by integrating the shRNA and drug perturbation of liver cancer cell lines and identifying potential drugs for every target gene. Finally, we evaluate the effects of the candidate drugs through an in vitro model and observe that two identified drugs inhibited the protein levels of their corresponding target genes and cell migration, also showing great binding affinity in protein docking analysis. Our study demonstrates the usefulness and efficiency of network-based drug repositioning approach to discover potential drugs for cancer treatment and precision medicine approach.
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Affiliation(s)
- Meng Yuan
- Science for Life Laboratory, KTH—Royal Institute of Technology, SE-17165 Stockholm, Sweden; (M.Y.); (K.S.); (X.L.); (M.S.); (W.K.); (S.S.); (M.U.)
| | - Koeun Shong
- Science for Life Laboratory, KTH—Royal Institute of Technology, SE-17165 Stockholm, Sweden; (M.Y.); (K.S.); (X.L.); (M.S.); (W.K.); (S.S.); (M.U.)
| | - Xiangyu Li
- Science for Life Laboratory, KTH—Royal Institute of Technology, SE-17165 Stockholm, Sweden; (M.Y.); (K.S.); (X.L.); (M.S.); (W.K.); (S.S.); (M.U.)
- Bash Biotech Inc., 600 West Broadway, Suite 700, San Diego, CA 92101, USA
| | - Sajda Ashraf
- Heka Lab, Camlik Mah. Hearty, Sk. No:4 Heka Human Plaza Umraniye, Istanbul 34774, Turkey;
| | - Mengnan Shi
- Science for Life Laboratory, KTH—Royal Institute of Technology, SE-17165 Stockholm, Sweden; (M.Y.); (K.S.); (X.L.); (M.S.); (W.K.); (S.S.); (M.U.)
| | - Woonghee Kim
- Science for Life Laboratory, KTH—Royal Institute of Technology, SE-17165 Stockholm, Sweden; (M.Y.); (K.S.); (X.L.); (M.S.); (W.K.); (S.S.); (M.U.)
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-41296 Gothenburg, Sweden;
- BioInnovation Institute, DK-2200 Copenhagen, Denmark
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum 25240, Turkey;
| | - Saeed Shoaie
- Science for Life Laboratory, KTH—Royal Institute of Technology, SE-17165 Stockholm, Sweden; (M.Y.); (K.S.); (X.L.); (M.S.); (W.K.); (S.S.); (M.U.)
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London SE1 9RT, UK
| | - Mathias Uhlen
- Science for Life Laboratory, KTH—Royal Institute of Technology, SE-17165 Stockholm, Sweden; (M.Y.); (K.S.); (X.L.); (M.S.); (W.K.); (S.S.); (M.U.)
| | - Cheng Zhang
- Science for Life Laboratory, KTH—Royal Institute of Technology, SE-17165 Stockholm, Sweden; (M.Y.); (K.S.); (X.L.); (M.S.); (W.K.); (S.S.); (M.U.)
- Key Laboratory of Advanced Drug Preparation Technologies, School of Pharmaceutical Sciences, Ministry of Education, Zhengzhou University, Zhengzhou 450001, China
- Correspondence: (C.Z.); (A.M.)
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH—Royal Institute of Technology, SE-17165 Stockholm, Sweden; (M.Y.); (K.S.); (X.L.); (M.S.); (W.K.); (S.S.); (M.U.)
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London SE1 9RT, UK
- Correspondence: (C.Z.); (A.M.)
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16
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Karlsson M, Sjöstedt E, Oksvold P, Sivertsson Å, Huang J, Álvez MB, Arif M, Li X, Lin L, Yu J, Ma T, Xu F, Han P, Jiang H, Mardinoglu A, Zhang C, von Feilitzen K, Xu X, Wang J, Yang H, Bolund L, Zhong W, Fagerberg L, Lindskog C, Pontén F, Mulder J, Luo Y, Uhlen M. Genome-wide annotation of protein-coding genes in pig. BMC Biol 2022; 20:25. [PMID: 35073880 PMCID: PMC8788080 DOI: 10.1186/s12915-022-01229-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 01/07/2022] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND There is a need for functional genome-wide annotation of the protein-coding genes to get a deeper understanding of mammalian biology. Here, a new annotation strategy is introduced based on dimensionality reduction and density-based clustering of whole-body co-expression patterns. This strategy has been used to explore the gene expression landscape in pig, and we present a whole-body map of all protein-coding genes in all major pig tissues and organs. RESULTS An open-access pig expression map ( www.rnaatlas.org ) is presented based on the expression of 350 samples across 98 well-defined pig tissues divided into 44 tissue groups. A new UMAP-based classification scheme is introduced, in which all protein-coding genes are stratified into tissue expression clusters based on body-wide expression profiles. The distribution and tissue specificity of all 22,342 protein-coding pig genes are presented. CONCLUSIONS Here, we present a new genome-wide annotation strategy based on dimensionality reduction and density-based clustering. A genome-wide resource of the transcriptome map across all major tissues and organs in pig is presented, and the data is available as an open-access resource ( www.rnaatlas.org ), including a comparison to the expression of human orthologs.
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Affiliation(s)
- Max Karlsson
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Evelina Sjöstedt
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Per Oksvold
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Åsa Sivertsson
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Jinrong Huang
- BGI-Shenzhen, Shenzhen, China
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, Qingdao, China
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - María Bueno Álvez
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Muhammad Arif
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Xiangyu Li
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Lin Lin
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| | - Jiaying Yu
- BGI-Shenzhen, Shenzhen, China
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, Qingdao, China
| | - Tao Ma
- MGI, BGI-Shenzhen, Shenzhen, China
| | - Fengping Xu
- BGI-Shenzhen, Shenzhen, China
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, Qingdao, China
| | - Peng Han
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, Qingdao, China
| | | | - Adil Mardinoglu
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Cheng Zhang
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Kalle von Feilitzen
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Xun Xu
- BGI-Shenzhen, Shenzhen, China
| | | | | | - Lars Bolund
- BGI-Shenzhen, Shenzhen, China
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, Qingdao, China
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Wen Zhong
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Linn Fagerberg
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Cecilia Lindskog
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Fredrik Pontén
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Jan Mulder
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Yonglun Luo
- BGI-Shenzhen, Shenzhen, China
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, Qingdao, China
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| | - Mathias Uhlen
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden.
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden.
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17
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Abstract
Fungal communities (mycobiome) have an important role in sustaining the resilience of complex microbial communities and maintenance of homeostasis. The mycobiome remains relatively unexplored compared to the bacteriome despite increasing evidence highlighting their contribution to host-microbiome interactions in health and disease. Despite being a small proportion of the total species, fungi constitute a large proportion of the biomass within the human microbiome and thus serve as a potential target for metabolic reprogramming in pathogenesis and disease mechanism. Metabolites produced by fungi shape host niches, induce immune tolerance and changes in their levels prelude changes associated with metabolic diseases and cancer. Given the complexity of microbial interactions, studying the metabolic interplay of the mycobiome with both host and microbiome is a demanding but crucial task. However, genome-scale modelling and synthetic biology can provide an integrative platform that allows elucidation of the multifaceted interactions between mycobiome, microbiome and host. The inferences gained from understanding mycobiome interplay with other organisms can delineate the key role of the mycobiome in pathophysiology and reveal its role in human disease.
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Affiliation(s)
- Neelu Begum
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, UK
| | - Azadeh Harzandi
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, UK
| | - Sunjae Lee
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, UK
| | - Mathias Uhlen
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - David L. Moyes
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, UK
| | - Saeed Shoaie
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, UK
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
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18
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Gilvesy A, Husen E, Magloczky Z, Mihaly O, Hortobágyi T, Kanatani S, Heinsen H, Renier N, Hökfelt T, Mulder J, Uhlen M, Kovacs GG, Adori C. Spatiotemporal characterization of cellular tau pathology in the human locus coeruleus-pericoerulear complex by three-dimensional imaging. Acta Neuropathol 2022; 144:651-676. [PMID: 36040521 PMCID: PMC9468059 DOI: 10.1007/s00401-022-02477-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/28/2022] [Accepted: 07/29/2022] [Indexed: 01/28/2023]
Abstract
Tau pathology of the noradrenergic locus coeruleus (LC) is a hallmark of several age-related neurodegenerative disorders, including Alzheimer's disease. However, a comprehensive neuropathological examination of the LC is difficult due to its small size and rod-like shape. To investigate the LC cytoarchitecture and tau cytoskeletal pathology in relation to possible propagation patterns of disease-associated tau in an unprecedented large-scale three-dimensional view, we utilized volume immunostaining and optical clearing technology combined with light sheet fluorescence microscopy. We examined AT8+ pathological tau in the LC/pericoerulear region of 20 brains from Braak neurofibrillary tangle (NFT) stage 0-6. We demonstrate an intriguing morphological complexity and heterogeneity of AT8+ cellular structures in the LC, representing various intracellular stages of NFT maturation and their diverse transition forms. We describe novel morphologies of neuronal tau pathology such as AT8+ cells with fine filamentous somatic protrusions or with disintegrating soma. We show that gradual dendritic atrophy is the first morphological sign of the degeneration of tangle-bearing neurons, even preceding axonal lesions. Interestingly, irrespective of the Braak NFT stage, tau pathology is more advanced in the dorsal LC that preferentially projects to vulnerable forebrain regions in Alzheimer's disease, like the hippocampus or neocortical areas, compared to the ventral LC projecting to the cerebellum and medulla. Moreover, already in the precortical Braak 0 stage, 3D analysis reveals clustering tendency and dendro-dendritic close appositions of AT8+ LC neurons, AT8+ long axons of NFT-bearing cells that join the ascending dorsal noradrenergic bundle after leaving the LC, as well as AT8+ processes of NFT-bearing LC neurons that target the 4th ventricle wall. Our study suggests that the unique cytoarchitecture, comprised of a densely packed and dendritically extensively interconnected neuronal network with long projections, makes the human LC to be an ideal anatomical template for early accumulation and trans-neuronal spreading of hyperphosphorylated tau.
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Affiliation(s)
- Abris Gilvesy
- Department of Neuroscience, Karolinska Institutet, Solnavägen 9, 17177, Stockholm, Sweden
- McGill University, Montreal, QC, H3A 0G4, Canada
| | - Evelina Husen
- Department of Neuroscience, Karolinska Institutet, Solnavägen 9, 17177, Stockholm, Sweden
| | - Zsofia Magloczky
- Human Brain Research Laboratory, Institute of Experimental Medicine, ELKH, Budapest, Hungary
| | - Orsolya Mihaly
- Department of Pathology, St. Borbála Hospital, Tatabánya, Hungary
| | - Tibor Hortobágyi
- Department of Neurology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
- Department of Old Age Psychiatry, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK
- Centre for Age-Related Medicine, SESAM, Stavanger University Hospital, Stavanger, Norway
- Institute of Neuropathology, University of Zurich, Zurich, Switzerland
| | - Shigeaki Kanatani
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, 17177, Stockholm, Sweden
| | - Helmut Heinsen
- Clinic of Psychiatry and Institute of Forensic Pathology, University of Würzburg, 97080, Würzburg, Germany
- LIM-44, University of Sao Paulo Medical School, Sao Paulo, Brazil
| | - Nicolas Renier
- Sorbonne Université, Paris Brain Institute-ICM, INSERM, CNRS, AP-HP, Hôpital de la Pitié Salpêtrière, 75013, Paris, France
| | - Tomas Hökfelt
- Department of Neuroscience, Karolinska Institutet, Solnavägen 9, 17177, Stockholm, Sweden
| | - Jan Mulder
- Department of Neuroscience, Karolinska Institutet, Solnavägen 9, 17177, Stockholm, Sweden
| | - Mathias Uhlen
- Department of Neuroscience, Karolinska Institutet, Solnavägen 9, 17177, Stockholm, Sweden
- Science for Life Laboratory, Royal Institute of Technology, 10691, Stockholm, Sweden
| | - Gabor G Kovacs
- Tanz Centre for Research in Neurodegenerative Disease and Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
- Laboratory Medicine Program and Krembil Brain Institute, University Health Network, Toronto, ON, Canada
| | - Csaba Adori
- Department of Neuroscience, Karolinska Institutet, Solnavägen 9, 17177, Stockholm, Sweden.
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19
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Mikus MS, Kolmert J, Andersson LI, Östling J, Knowles RG, Gómez C, Ericsson M, Thörngren JO, Khoonsari PE, Dahlén B, Kupczyk M, De Meulder B, Auffray C, Bakke PS, Beghe B, Bel EH, Caruso M, Chanez P, Chawes B, Fowler SJ, Gaga M, Geiser T, Gjomarkaj M, Horváth I, Howarth PH, Johnston SL, Joos G, Krug N, Montuschi P, Musial J, Niżankowska-Mogilnicka E, Olsson HK, Papi A, Rabe KF, Sandström T, Shaw DE, Siafakas NM, Uhlen M, Riley JH, Bates S, Middelveld RJM, Wheelock CE, Chung KF, Adcock IM, Sterk PJ, Djukanovic R, Nilsson P, Dahlén SE, James A. Plasma proteins elevated in severe asthma despite oral steroid use and unrelated to Type-2 inflammation. Eur Respir J 2021; 59:13993003.00142-2021. [PMID: 34737220 PMCID: PMC8850689 DOI: 10.1183/13993003.00142-2021] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 06/24/2021] [Indexed: 12/02/2022]
Abstract
Rationale Asthma phenotyping requires novel biomarker discovery. Objectives To identify plasma biomarkers associated with asthma phenotypes by application of a new proteomic panel to samples from two well-characterised cohorts of severe (SA) and mild-to-moderate (MMA) asthmatics, COPD subjects and healthy controls (HCs). Methods An antibody-based array targeting 177 proteins predominantly involved in pathways relevant to inflammation, lipid metabolism, signal transduction and extracellular matrix was applied to plasma from 525 asthmatics and HCs in the U-BIOPRED cohort, and 142 subjects with asthma and COPD from the validation cohort BIOAIR. Effects of oral corticosteroids (OCS) were determined by a 2-week, placebo-controlled OCS trial in BIOAIR, and confirmed by relation to objective OCS measures in U-BIOPRED. Results In U-BIOPRED, 110 proteins were significantly different, mostly elevated, in SA compared to MMA and HCs. 10 proteins were elevated in SA versus MMA in both U-BIOPRED and BIOAIR (alpha-1-antichymotrypsin, apolipoprotein-E, complement component 9, complement factor I, macrophage inflammatory protein-3, interleukin-6, sphingomyelin phosphodiesterase 3, TNF receptor superfamily member 11a, transforming growth factor-β and glutathione S-transferase). OCS treatment decreased most proteins, yet differences between SA and MMA remained following correction for OCS use. Consensus clustering of U-BIOPRED protein data yielded six clusters associated with asthma control, quality of life, blood neutrophils, high-sensitivity C-reactive protein and body mass index, but not Type-2 inflammatory biomarkers. The mast cell specific enzyme carboxypeptidase A3 was one major contributor to cluster differentiation. Conclusions The plasma proteomic panel revealed previously unexplored yet potentially useful Type-2-independent biomarkers and validated several proteins with established involvement in the pathophysiology of SA. Application of new proteomic panel in two established European asthma cohorts identifies plasma proteins associated with disease severity independently of Type-2 inflammation, suggesting potentially useful novel biomarkers and therapeutic targets.https://bit.ly/3jtTq5m
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Affiliation(s)
- Maria Sparreman Mikus
- Department of Protein Science, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden .,Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Johan Kolmert
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.,Centre for Allergy Research, Karolinska Institutet, Stockholm, Sweden
| | - Lars I Andersson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.,Centre for Allergy Research, Karolinska Institutet, Stockholm, Sweden
| | | | | | - Cristina Gómez
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.,Centre for Allergy Research, Karolinska Institutet, Stockholm, Sweden
| | - Magnus Ericsson
- Department of Laboratory Medicine, Karolinska University Hospital Huddinge, Stockholm, Sweden
| | - John-Olof Thörngren
- Department of Laboratory Medicine, Karolinska University Hospital Huddinge, Stockholm, Sweden
| | - Payam Emami Khoonsari
- Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Solna, Sweden
| | - Barbro Dahlén
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.,Centre for Allergy Research, Karolinska Institutet, Stockholm, Sweden.,Department of Medicine, Karolinska University Hospital Huddinge, Stockholm, Sweden
| | - Maciej Kupczyk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.,Centre for Allergy Research, Karolinska Institutet, Stockholm, Sweden.,Department of Internal Medicine, Asthma and Allergy, Medical University of Lodz, University of Lodz, Lodz, Poland
| | | | - Charles Auffray
- European Institute for Systems Biology and Medicine, Lyon, France
| | - Per S Bakke
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Bianca Beghe
- Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Elisabeth H Bel
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Massimo Caruso
- Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy
| | - Pascal Chanez
- Assistance Publique des Hôpitaux de Marseille, Clinique des Bronches, Allergies et Sommeil, Aix Marseille Université, Marseille, France
| | - Bo Chawes
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Stephen J Fowler
- Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, The University of Manchester; Manchester Academic Health Science Centre and NIHR Manchester Biomedical Research Centre, Manchester University Hospitals NHS Foundation Trust, Manchester, UK
| | - Mina Gaga
- Respiratory Medicine Dept and Asthma Centre, Athens Chest Hospital "Sotiria", University of Athens, Athens, Greece
| | - Thomas Geiser
- Department for Pulmonary Medicine, University Hospital and University of Bern, Bern, Switzerland
| | - Mark Gjomarkaj
- Institute for Research and Biomedical Innovation, Italian National Research Council, Palermo, Italy
| | - Ildikó Horváth
- Department of Pulmonology, Semmelweis University, Budapest, Hungary
| | - Peter H Howarth
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton NHS Foundation Trust, and Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | | | - Guy Joos
- Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium.,Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium
| | - Norbert Krug
- Fraunhofer Institute for Toxicology and Experimental Medicine, Hannover, Germany
| | - Paolo Montuschi
- Department of Pharmacology, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Jacek Musial
- Department of Internal Medicine, Jagiellonian University Medical College, Krakow, Poland
| | | | - Henric K Olsson
- Translational Science and Experimental Medicine, Research and Early Development, Respiratory & Immunology, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Alberto Papi
- Division of lnternal and Cardiorespiratory Medicine, University of Ferrara, Ferrara, Italy
| | - Klaus F Rabe
- Department of Internal Medicine, Christian Albrechts University Kiel, Kiel, Germany
| | - Thomas Sandström
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Dominick E Shaw
- Respiratory Research Unit, University of Nottingham, Nottingham, UK
| | - Nikolaos M Siafakas
- Department of Thoracic Medicine, Medical School, University of Crete, Heraklion, Crete, Greece
| | - Mathias Uhlen
- Department of Protein Science, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden.,Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - John H Riley
- Respiratory Therapeutic Unit, GlaxoSmithKline, London, UK
| | - Stewart Bates
- Respiratory Therapeutic Unit, GlaxoSmithKline, London, UK
| | - Roelinde J M Middelveld
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.,Centre for Allergy Research, Karolinska Institutet, Stockholm, Sweden
| | - Craig E Wheelock
- Centre for Allergy Research, Karolinska Institutet, Stockholm, Sweden.,Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Kian Fan Chung
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Ian M Adcock
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Peter J Sterk
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Ratko Djukanovic
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton NHS Foundation Trust, and Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Peter Nilsson
- Department of Protein Science, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden
| | - Sven-Erik Dahlén
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.,Centre for Allergy Research, Karolinska Institutet, Stockholm, Sweden
| | - Anna James
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.,Centre for Allergy Research, Karolinska Institutet, Stockholm, Sweden
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20
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Doran S, Arif M, Lam S, Bayraktar A, Turkez H, Uhlen M, Boren J, Mardinoglu A. Multi-omics approaches for revealing the complexity of cardiovascular disease. Brief Bioinform 2021; 22:bbab061. [PMID: 33725119 PMCID: PMC8425417 DOI: 10.1093/bib/bbab061] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 01/20/2021] [Accepted: 02/05/2021] [Indexed: 02/06/2023] Open
Abstract
The development and progression of cardiovascular disease (CVD) can mainly be attributed to the narrowing of blood vessels caused by atherosclerosis and thrombosis, which induces organ damage that will result in end-organ dysfunction characterized by events such as myocardial infarction or stroke. It is also essential to consider other contributory factors to CVD, including cardiac remodelling caused by cardiomyopathies and co-morbidities with other diseases such as chronic kidney disease. Besides, there is a growing amount of evidence linking the gut microbiota to CVD through several metabolic pathways. Hence, it is of utmost importance to decipher the underlying molecular mechanisms associated with these disease states to elucidate the development and progression of CVD. A wide array of systems biology approaches incorporating multi-omics data have emerged as an invaluable tool in establishing alterations in specific cell types and identifying modifications in signalling events that promote disease development. Here, we review recent studies that apply multi-omics approaches to further understand the underlying causes of CVD and provide possible treatment strategies by identifying novel drug targets and biomarkers. We also discuss very recent advances in gut microbiota research with an emphasis on how diet and microbial composition can impact the development of CVD. Finally, we present various biological network analyses and other independent studies that have been employed for providing mechanistic explanation and developing treatment strategies for end-stage CVD, namely myocardial infarction and stroke.
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Affiliation(s)
- Stephen Doran
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, United Kingdom
| | - Muhammad Arif
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Simon Lam
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, United Kingdom
| | - Abdulahad Bayraktar
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, United Kingdom
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum, Turkey
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Jan Boren
- Institute of Medicine, Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital Gothenburg, Sweden
| | - Adil Mardinoglu
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, United Kingdom
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
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21
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Arif M, Zhang C, Li X, Güngör C, Çakmak B, Arslantürk M, Tebani A, Özcan B, Subaş O, Zhou W, Piening B, Turkez H, Fagerberg L, Price N, Hood L, Snyder M, Nielsen J, Uhlen M, Mardinoglu A. iNetModels 2.0: an interactive visualization and database of multi-omics data. Nucleic Acids Res 2021; 49:W271-W276. [PMID: 33849075 PMCID: PMC8262747 DOI: 10.1093/nar/gkab254] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 03/10/2021] [Accepted: 03/29/2021] [Indexed: 12/11/2022] Open
Abstract
It is essential to reveal the associations between various omics data for a comprehensive understanding of the altered biological process in human wellness and disease. To date, very few studies have focused on collecting and exhibiting multi-omics associations in a single database. Here, we present iNetModels, an interactive database and visualization platform of Multi-Omics Biological Networks (MOBNs). This platform describes the associations between the clinical chemistry, anthropometric parameters, plasma proteomics, plasma metabolomics, as well as metagenomics for oral and gut microbiome obtained from the same individuals. Moreover, iNetModels includes tissue- and cancer-specific Gene Co-expression Networks (GCNs) for exploring the connections between the specific genes. This platform allows the user to interactively explore a single feature's association with other omics data and customize its particular context (e.g. male/female specific). The users can also register their data for sharing and visualization of the MOBNs and GCNs. Moreover, iNetModels allows users who do not have a bioinformatics background to facilitate human wellness and disease research. iNetModels can be accessed freely at https://inetmodels.com without any limitation.
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Affiliation(s)
- Muhammad Arif
- Science for Life Laboratory, KTH – Royal Institute of Technology, Stockholm SE-171 21, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, KTH – Royal Institute of Technology, Stockholm SE-171 21, Sweden
- School of Pharmaceutical Sciences & Key Laboratory of Advanced Drug Preparation Technologies, Ministry of Education, Zhengzhou University, Zhengzhou, Henan Province, PR 450001, China
| | - Xiangyu Li
- Science for Life Laboratory, KTH – Royal Institute of Technology, Stockholm SE-171 21, Sweden
| | - Cem Güngör
- Bash Biotech Inc, 600 West Broadway, Suite 700, San Diego, CA, USA
| | - Buğra Çakmak
- Bash Biotech Inc, 600 West Broadway, Suite 700, San Diego, CA, USA
| | - Metin Arslantürk
- Bash Biotech Inc, 600 West Broadway, Suite 700, San Diego, CA, USA
| | - Abdellah Tebani
- Department of Metabolic Biochemistry, Rouen University Hospital, 76000 Rouen, France
- Normandie Univ, UNIROUEN, CHU Rouen, INSERM U1245, 76000 Rouen, France
| | - Berkay Özcan
- Bash Biotech Inc, 600 West Broadway, Suite 700, San Diego, CA, USA
| | - Oğuzhan Subaş
- Bash Biotech Inc, 600 West Broadway, Suite 700, San Diego, CA, USA
| | - Wenyu Zhou
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Brian Piening
- Providence Cancer Center, Oregon Area, Portland, OR, USA
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum, Turkey
| | - Linn Fagerberg
- Science for Life Laboratory, KTH – Royal Institute of Technology, Stockholm SE-171 21, Sweden
| | | | - Leroy Hood
- Institute of Systems Biology, Seattle, USA
| | - Michael Snyder
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH – Royal Institute of Technology, Stockholm SE-171 21, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH – Royal Institute of Technology, Stockholm SE-171 21, Sweden
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London SE1 9RT, UK
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22
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Arif M, Zhang C, Li X, Güngör C, Çakmak B, Arslantürk M, Tebani A, Özcan B, Subaş O, Zhou W, Piening B, Turkez H, Fagerberg L, Price N, Hood L, Snyder M, Nielsen J, Uhlen M, Mardinoglu A. iNetModels 2.0: an interactive visualization and database of multi-omics data. Nucleic Acids Res 2021; 49:W271-W276. [PMID: 33849075 DOI: 10.1101/2021.11.10.468051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 03/10/2021] [Accepted: 03/29/2021] [Indexed: 05/20/2023] Open
Abstract
It is essential to reveal the associations between various omics data for a comprehensive understanding of the altered biological process in human wellness and disease. To date, very few studies have focused on collecting and exhibiting multi-omics associations in a single database. Here, we present iNetModels, an interactive database and visualization platform of Multi-Omics Biological Networks (MOBNs). This platform describes the associations between the clinical chemistry, anthropometric parameters, plasma proteomics, plasma metabolomics, as well as metagenomics for oral and gut microbiome obtained from the same individuals. Moreover, iNetModels includes tissue- and cancer-specific Gene Co-expression Networks (GCNs) for exploring the connections between the specific genes. This platform allows the user to interactively explore a single feature's association with other omics data and customize its particular context (e.g. male/female specific). The users can also register their data for sharing and visualization of the MOBNs and GCNs. Moreover, iNetModels allows users who do not have a bioinformatics background to facilitate human wellness and disease research. iNetModels can be accessed freely at https://inetmodels.com without any limitation.
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Affiliation(s)
- Muhammad Arif
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-171 21, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-171 21, Sweden
- School of Pharmaceutical Sciences & Key Laboratory of Advanced Drug Preparation Technologies, Ministry of Education, Zhengzhou University, Zhengzhou, Henan Province, PR 450001, China
| | - Xiangyu Li
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-171 21, Sweden
| | - Cem Güngör
- Bash Biotech Inc, 600 West Broadway, Suite 700, San Diego, CA, USA
| | - Buğra Çakmak
- Bash Biotech Inc, 600 West Broadway, Suite 700, San Diego, CA, USA
| | - Metin Arslantürk
- Bash Biotech Inc, 600 West Broadway, Suite 700, San Diego, CA, USA
| | - Abdellah Tebani
- Department of Metabolic Biochemistry, Rouen University Hospital, 76000 Rouen, France
- Normandie Univ, UNIROUEN, CHU Rouen, INSERM U1245, 76000 Rouen, France
| | - Berkay Özcan
- Bash Biotech Inc, 600 West Broadway, Suite 700, San Diego, CA, USA
| | - Oğuzhan Subaş
- Bash Biotech Inc, 600 West Broadway, Suite 700, San Diego, CA, USA
| | - Wenyu Zhou
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Brian Piening
- Providence Cancer Center, Oregon Area, Portland, OR, USA
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum, Turkey
| | - Linn Fagerberg
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-171 21, Sweden
| | | | - Leroy Hood
- Institute of Systems Biology, Seattle, USA
| | - Michael Snyder
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-171 21, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-171 21, Sweden
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London SE1 9RT, UK
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23
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Li X, Kim W, Juszczak K, Arif M, Sato Y, Kume H, Ogawa S, Turkez H, Boren J, Nielsen J, Uhlen M, Zhang C, Mardinoglu A. Stratification of patients with clear cell renal cell carcinoma to facilitate drug repositioning. iScience 2021; 24:102722. [PMID: 34258555 PMCID: PMC8253978 DOI: 10.1016/j.isci.2021.102722] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 05/14/2021] [Accepted: 06/10/2021] [Indexed: 12/24/2022] Open
Abstract
Clear cell renal cell carcinoma (ccRCC) is the most common histological type of kidney cancer and has high heterogeneity. Stratification of ccRCC is important since distinct subtypes differ in prognosis and treatment. Here, we applied a systems biology approach to stratify ccRCC into three molecular subtypes with different mRNA expression patterns and prognosis of patients. Further, we developed a set of biomarkers that could robustly classify the patients into each of the three subtypes and predict the prognosis of patients. Then, we reconstructed subtype-specific metabolic models and performed essential gene analysis to identify the potential drug targets. We identified four drug targets, including SOAT1, CRLS1, and ACACB, essential in all the three subtypes and GPD2, exclusively essential to subtype 1. Finally, we repositioned mitotane, an FDA-approved SOAT1 inhibitor, to treat ccRCC and showed that it decreased tumor cell viability and inhibited tumor cell growth based on in vitro experiments. Three consistent molecular ccRCC subtypes were found to guide patients' prognoses REOs-based biomarker was developed to robustly classify patients at individual level SOAT1 is identified as a common drug target for all ccRCC subtypes Mitotane was repositioned treatment of ccRCC via inhibiting SOAT1
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Affiliation(s)
- Xiangyu Li
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm 17165, Sweden.,Bash Biotech Inc, 600 West Broadway, Suite 700, San Diego, CA 92101, USA
| | - Woonghee Kim
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm 17165, Sweden
| | - Kajetan Juszczak
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm 17165, Sweden
| | - Muhammad Arif
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm 17165, Sweden
| | - Yusuke Sato
- Department of Pathology and Tumor Biology, Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto 606-8501, Japan.,Department of Urology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8654, Japan
| | - Haruki Kume
- Department of Urology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8654, Japan
| | - Seishi Ogawa
- Department of Pathology and Tumor Biology, Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto 606-8501, Japan.,Centre for Hematology and Regenerative Medicine, Department of Medicine, Karolinska Institute, Stockholm 17177, Sweden
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum 25240, Turkey
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg 41345, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg 41296, Sweden.,BioInnovation Institute, Copenhagen N 2200, Denmark
| | - Mathias Uhlen
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm 17165, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm 17165, Sweden.,Key Laboratory of Advanced Drug Preparation Technologies, School of Pharmaceutical Sciences, Ministry of Education, Zhengzhou University, Zhengzhou 450001, China
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm 17165, Sweden.,Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London SE1 9RT, UK
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24
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Bosley JR, Björnson E, Zhang C, Turkez H, Nielsen J, Uhlen M, Borén J, Mardinoglu A. Informing Pharmacokinetic Models With Physiological Data: Oral Population Modeling of L-Serine in Humans. Front Pharmacol 2021; 12:643179. [PMID: 34054524 PMCID: PMC8156419 DOI: 10.3389/fphar.2021.643179] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Accepted: 04/30/2021] [Indexed: 11/13/2022] Open
Abstract
To determine how to set optimal oral L-serine (serine) dose levels for a clinical trial, existing literature was surveyed. Data sufficient to set the dose was inadequate, and so an (n = 10) phase I-A calibration trial was performed, administering serine with and without other oral agents. We analyzed the trial and the literature data using pharmacokinetic (PK) modeling and statistical analysis. The therapeutic goal is to modulate specific serine-related metabolic pathways in the liver using the lowest possible dose which gives the desired effect since the upper bound was expected to be limited by toxicity. A standard PK approach, in which a common model structure was selected using a fit to data, yielded a model with a single central compartment corresponding to plasma, clearance from that compartment, and an endogenous source of serine. To improve conditioning, a parametric structure was changed to estimate ratios (bioavailability over volume, for example). Model fit quality was improved and the uncertainty in estimated parameters was reduced. Because of the particular interest in the fate of serine, the model was used to estimate whether serine is consumed in the gut, absorbed by the liver, or entered the blood in either a free state, or in a protein- or tissue-bound state that is not measured by our assay. The PK model structure was set up to represent relevant physiology, and this quantitative systems biology approach allowed a broader set of physiological data to be used to narrow parameter and prediction confidence intervals, and to better understand the biological meaning of the data. The model results allowed us to determine the optimal human dose for future trials, including a trial design component including IV and tracer studies. A key contribution is that we were able to use human physiological data from the literature to inform the PK model and to set reasonable bounds on parameters, and to improve model conditioning. Leveraging literature data produced a more predictive, useful model.
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Affiliation(s)
- J R Bosley
- Clermont Bosley LLC, Philadelphia, PA, United States
| | - Elias Björnson
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden.,Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum, Turkey
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Jan Borén
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden.,Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, United Kingdom
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25
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Lakshmikanth T, Muhammad SA, Olin A, Chen Y, Mikes J, Fagerberg L, Gummesson A, Bergström G, Uhlen M, Brodin P. Human Immune System Variation during 1 Year. Cell Rep 2021; 32:107923. [PMID: 32697987 DOI: 10.1016/j.celrep.2020.107923] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Revised: 03/19/2020] [Accepted: 06/26/2020] [Indexed: 12/22/2022] Open
Abstract
The human immune system varies extensively between individuals, but variation within individuals over time has not been well characterized. Systems-level analyses allow for simultaneous quantification of many interacting immune system components and the inference of global regulatory principles. Here, we present a longitudinal, systems-level analysis in 99 healthy adults 50 to 65 years of age and sampled every third month for 1 year. We describe the structure of interindividual variation and characterize extreme phenotypes along a principal curve. From coordinated measurement fluctuations, we infer relationships between 115 immune cell populations and 750 plasma proteins constituting the blood immune system. While most individuals have stable immune systems, the degree of longitudinal variability is an individual feature. The most variable individuals, in the absence of overt infections, exhibited differences in markers of metabolic health suggestive of a possible link between metabolic and immunologic homeostatic regulation.
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Affiliation(s)
- Tadepally Lakshmikanth
- Science for Life Laboratory, Department of Women's and Children's Health, Karolinska Institutet, Karolinska, Sweden
| | - Sayyed Auwn Muhammad
- Science for Life Laboratory, Department of Women's and Children's Health, Karolinska Institutet, Karolinska, Sweden
| | - Axel Olin
- Science for Life Laboratory, Department of Women's and Children's Health, Karolinska Institutet, Karolinska, Sweden
| | - Yang Chen
- Science for Life Laboratory, Department of Women's and Children's Health, Karolinska Institutet, Karolinska, Sweden
| | - Jaromir Mikes
- Science for Life Laboratory, Department of Women's and Children's Health, Karolinska Institutet, Karolinska, Sweden
| | - Linn Fagerberg
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Anders Gummesson
- Department of Molecular and Clinical Medicine, Wallenberg Laboratory, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden; Department of Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Göran Bergström
- Department of Molecular and Clinical Medicine, Wallenberg Laboratory, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden; Department of Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Petter Brodin
- Science for Life Laboratory, Department of Women's and Children's Health, Karolinska Institutet, Karolinska, Sweden; Department of Pediatric Rheumatology, Karolinska University Hospital, Karolinska, Sweden.
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26
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Rosario D, Bidkhori G, Lee S, Bedarf J, Hildebrand F, Le Chatelier E, Uhlen M, Ehrlich SD, Proctor G, Wüllner U, Mardinoglu A, Shoaie S. Systematic analysis of gut microbiome reveals the role of bacterial folate and homocysteine metabolism in Parkinson's disease. Cell Rep 2021; 34:108807. [PMID: 33657381 DOI: 10.1016/j.celrep.2021.108807] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 12/22/2020] [Accepted: 02/09/2021] [Indexed: 01/06/2023] Open
Abstract
Parkinson's disease (PD) is the most common progressive neurological disorder compromising motor functions. However, nonmotor symptoms, such as gastrointestinal (GI) dysfunction, precede those affecting movement. Evidence of an early involvement of the GI tract and enteric nervous system highlights the need for better understanding of the role of gut microbiota in GI complications in PD. Here, we investigate the gut microbiome of patients with PD using metagenomics and serum metabolomics. We integrate these data using metabolic modeling and construct an integrative correlation network giving insight into key microbial species linked with disease severity, GI dysfunction, and age of patients with PD. Functional analysis reveals an increased microbial capability to degrade mucin and host glycans in PD. Personalized community-level metabolic modeling reveals the microbial contribution to folate deficiency and hyperhomocysteinemia observed in patients with PD. The metabolic modeling approach could be applied to uncover gut microbial metabolic contributions to PD pathophysiology.
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Affiliation(s)
- Dorines Rosario
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT London, UK
| | - Gholamreza Bidkhori
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT London, UK
| | - Sunjae Lee
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT London, UK
| | - Janis Bedarf
- Department of Neurology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany; Gut Microbes & Health, Quadram Institute Bioscience, Norwich Research Park, Norwich, Norfolk NR4 7UA, UK
| | - Falk Hildebrand
- Gut Microbes & Health, Quadram Institute Bioscience, Norwich Research Park, Norwich, Norfolk NR4 7UA, UK; European Molecular Biology Laboratory, Structural and Computational Biology Unit, 69117 Heidelberg, Germany; Digital Biology, Earlham Institute, Norwich, Norwich Research Park, Norwich NR4 7UZ, Norfolk, UK
| | | | - Mathias Uhlen
- Science for Life Laboratory (SciLifeLab), KTH - Royal Institute of Technology, Tomtebodavägen 23, 171 65 Solna, Stockholm, Sweden
| | | | - Gordon Proctor
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT London, UK
| | - Ullrich Wüllner
- Department of Neurology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany; German Centre for Neurodegenerative Disease Research (DZNE), 53127 Bonn, Germany
| | - Adil Mardinoglu
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT London, UK; Science for Life Laboratory (SciLifeLab), KTH - Royal Institute of Technology, Tomtebodavägen 23, 171 65 Solna, Stockholm, Sweden.
| | - Saeed Shoaie
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT London, UK; Science for Life Laboratory (SciLifeLab), KTH - Royal Institute of Technology, Tomtebodavägen 23, 171 65 Solna, Stockholm, Sweden.
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27
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Mohammadi E, Benfeitas R, Turkez H, Boren J, Nielsen J, Uhlen M, Mardinoglu A. Applications of Genome-Wide Screening and Systems Biology Approaches in Drug Repositioning. Cancers (Basel) 2020; 12:E2694. [PMID: 32967266 PMCID: PMC7563533 DOI: 10.3390/cancers12092694] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 09/14/2020] [Accepted: 09/16/2020] [Indexed: 12/24/2022] Open
Abstract
Modern drug discovery through de novo drug discovery entails high financial costs, low success rates, and lengthy trial periods. Drug repositioning presents a suitable approach for overcoming these issues by re-evaluating biological targets and modes of action of approved drugs. Coupling high-throughput technologies with genome-wide essentiality screens, network analysis, genome-scale metabolic modeling, and machine learning techniques enables the proposal of new drug-target signatures and uncovers unanticipated modes of action for available drugs. Here, we discuss the current issues associated with drug repositioning in light of curated high-throughput multi-omic databases, genome-wide screening technologies, and their application in systems biology/medicine approaches.
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Affiliation(s)
- Elyas Mohammadi
- Science for Life Laboratory, KTH–Royal Institute of Technology, SE-17121 Stockholm, Sweden; (E.M.); (M.U.)
- Department of Animal Science, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
| | - Rui Benfeitas
- National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, SE-10691 Stockholm, Sweden;
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, 25240 Erzurum, Turkey;
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg, The Wallenberg Laboratory, Sahlgrenska University Hospital, SE-41345 Gothenburg, Sweden;
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-41296 Gothenburg, Sweden;
- BioInnovation Institute, DK-2200 Copenhagen N, Denmark
| | - Mathias Uhlen
- Science for Life Laboratory, KTH–Royal Institute of Technology, SE-17121 Stockholm, Sweden; (E.M.); (M.U.)
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH–Royal Institute of Technology, SE-17121 Stockholm, Sweden; (E.M.); (M.U.)
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London SE1 9RT, UK
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28
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Tebani A, Gummesson A, Zhong W, Koistinen IS, Lakshmikanth T, Olsson LM, Boulund F, Neiman M, Stenlund H, Hellström C, Karlsson MJ, Arif M, Dodig-Crnković T, Mardinoglu A, Lee S, Zhang C, Chen Y, Olin A, Mikes J, Danielsson H, von Feilitzen K, Jansson PA, Angerås O, Huss M, Kjellqvist S, Odeberg J, Edfors F, Tremaroli V, Forsström B, Schwenk JM, Nilsson P, Moritz T, Bäckhed F, Engstrand L, Brodin P, Bergström G, Uhlen M, Fagerberg L. Integration of molecular profiles in a longitudinal wellness profiling cohort. Nat Commun 2020; 11:4487. [PMID: 32900998 PMCID: PMC7479148 DOI: 10.1038/s41467-020-18148-7] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 08/03/2020] [Indexed: 12/19/2022] Open
Abstract
An important aspect of precision medicine is to probe the stability in molecular profiles among healthy individuals over time. Here, we sample a longitudinal wellness cohort with 100 healthy individuals and analyze blood molecular profiles including proteomics, transcriptomics, lipidomics, metabolomics, autoantibodies and immune cell profiling, complemented with gut microbiota composition and routine clinical chemistry. Overall, our results show high variation between individuals across different molecular readouts, while the intra-individual baseline variation is low. The analyses show that each individual has a unique and stable plasma protein profile throughout the study period and that many individuals also show distinct profiles with regards to the other omics datasets, with strong underlying connections between the blood proteome and the clinical chemistry parameters. In conclusion, the results support an individual-based definition of health and show that comprehensive omics profiling in a longitudinal manner is a path forward for precision medicine. An important aspect of precision medicine is to probe the stability in molecular profiles among healthy individuals over time. Here, the authors sample a longitudinal wellness cohort and analyse blood molecular profiles as well as gut microbiota composition.
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Affiliation(s)
- Abdellah Tebani
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Anders Gummesson
- Wallenberg Laboratory and Sahlgrenska Center for Cardiovascular and Metabolic Research, Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.,Region Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Genetics and Genomics, Gothenburg, Sweden
| | - Wen Zhong
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Ina Schuppe Koistinen
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.,Center for Translational Microbiome Research, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
| | - Tadepally Lakshmikanth
- Science for Life Laboratory, Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | - Lisa M Olsson
- Wallenberg Laboratory and Sahlgrenska Center for Cardiovascular and Metabolic Research, Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Fredrik Boulund
- Center for Translational Microbiome Research, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
| | - Maja Neiman
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Hans Stenlund
- Swedish Metabolomics Centre, Department of Molecular Biology, Umeå University, 901 87, Umeå, Sweden
| | - Cecilia Hellström
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Max J Karlsson
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Muhammad Arif
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Tea Dodig-Crnković
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.,Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK
| | - Sunjae Lee
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Yang Chen
- Science for Life Laboratory, Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | - Axel Olin
- Science for Life Laboratory, Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | - Jaromir Mikes
- Science for Life Laboratory, Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | - Hanna Danielsson
- Center for Translational Microbiome Research, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
| | - Kalle von Feilitzen
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Per-Anders Jansson
- Wallenberg Laboratory and Sahlgrenska Center for Cardiovascular and Metabolic Research, Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.,Region Västra Götaland, Sahlgrenska University Hospital, Department of Internal Medicine, Gothenburg, Sweden
| | - Oskar Angerås
- Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.,Region Västra Götaland, Sahlgrenska University Hospital, Department of Cardiology, Gothenburg, Sweden
| | - Mikael Huss
- Codon Consulting, 118 26, Stockholm, Sweden.,Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden
| | - Sanela Kjellqvist
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Jacob Odeberg
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Fredrik Edfors
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Valentina Tremaroli
- Wallenberg Laboratory and Sahlgrenska Center for Cardiovascular and Metabolic Research, Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Björn Forsström
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Jochen M Schwenk
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Peter Nilsson
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Thomas Moritz
- Swedish Metabolomics Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, 907 36, Umeå, Sweden
| | - Fredrik Bäckhed
- Wallenberg Laboratory and Sahlgrenska Center for Cardiovascular and Metabolic Research, Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.,Region Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Physiology, Gothenburg, Sweden.,Novo Nordisk Foundation Center for Basic Metabolic Research, Section for Metabolic Receptology and Enteroendocrinology, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lars Engstrand
- Center for Translational Microbiome Research, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
| | - Petter Brodin
- Science for Life Laboratory, Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | - Göran Bergström
- Wallenberg Laboratory and Sahlgrenska Center for Cardiovascular and Metabolic Research, Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.,Region Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Physiology, Gothenburg, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.,Center for Biosustainability, Danish Technical University, Copenhagen, Denmark
| | - Linn Fagerberg
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.
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Altay O, Mohammadi E, Lam S, Turkez H, Boren J, Nielsen J, Uhlen M, Mardinoglu A. Current Status of COVID-19 Therapies and Drug Repositioning Applications. iScience 2020; 23:101303. [PMID: 32622261 PMCID: PMC7305759 DOI: 10.1016/j.isci.2020.101303] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 06/15/2020] [Accepted: 06/17/2020] [Indexed: 02/09/2023] Open
Abstract
The rapid and global spread of a new human coronavirus (SARS-CoV-2) has produced an immediate urgency to discover promising targets for the treatment of COVID-19. Drug repositioning is an attractive approach that can facilitate the drug discovery process by repurposing existing pharmaceuticals to treat illnesses other than their primary indications. Here, we review current information concerning the global health issue of COVID-19 including promising approved drugs and ongoing clinical trials for prospective treatment options. In addition, we describe computational approaches to be used in drug repurposing and highlight examples of in silico studies of drug development efforts against SARS-CoV-2.
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Affiliation(s)
- Ozlem Altay
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm 17121, Sweden
| | - Elyas Mohammadi
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm 17121, Sweden; Department of Animal Science, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
| | - Simon Lam
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London SE1 9RT, UK
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum 25240, Turkey
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg, The Wallenberg Laboratory, Sahlgrenska University Hospital, Gothenburg 41345, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-Gothenburg, 41296, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm 17121, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm 17121, Sweden; Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London SE1 9RT, UK.
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Rosario D, Boren J, Uhlen M, Proctor G, Aarsland D, Mardinoglu A, Shoaie S. Systems Biology Approaches to Understand the Host-Microbiome Interactions in Neurodegenerative Diseases. Front Neurosci 2020; 14:716. [PMID: 32733199 PMCID: PMC7360858 DOI: 10.3389/fnins.2020.00716] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 06/12/2020] [Indexed: 12/12/2022] Open
Abstract
Neurodegenerative diseases (NDDs) comprise a broad range of progressive neurological disorders with multifactorial etiology contributing to disease pathophysiology. Evidence of the microbiome involvement in the gut-brain axis urges the interest in understanding metabolic interactions between the microbiota and host physiology in NDDs. Systems Biology offers a holistic integrative approach to study the interplay between the different biologic systems as part of a whole, and may elucidate the host–microbiome interactions in NDDs. We reviewed direct and indirect pathways through which the microbiota can modulate the bidirectional communication of the gut-brain axis, and explored the evidence of microbial dysbiosis in Alzheimer’s and Parkinson’s diseases. As the gut microbiota being strongly affected by diet, the potential approaches to targeting the human microbiota through diet for the stimulation of neuroprotective microbial-metabolites secretion were described. We explored the potential of Genome-scale metabolic models (GEMs) to infer microbe-microbe and host-microbe interactions and to identify the microbiome contribution to disease development or prevention. Finally, a systemic approach based on GEMs and ‘omics integration, that would allow the design of sustainable personalized anti-inflammatory diets in NDDs prevention, through the modulation of gut microbiota was described.
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Affiliation(s)
- Dorines Rosario
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, United Kingdom
| | - Jan Boren
- Department of Molecular and Clinical Medicine, Sahlgrenska University Hospital, University of Gothenburg, Gothenburg, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Gordon Proctor
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, United Kingdom
| | - Dag Aarsland
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Adil Mardinoglu
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, United Kingdom.,Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Saeed Shoaie
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, United Kingdom.,Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
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Lam S, Bayraktar A, Zhang C, Turkez H, Nielsen J, Boren J, Shoaie S, Uhlen M, Mardinoglu A. A systems biology approach for studying neurodegenerative diseases. Drug Discov Today 2020; 25:1146-1159. [PMID: 32442631 DOI: 10.1016/j.drudis.2020.05.010] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 04/13/2020] [Accepted: 05/13/2020] [Indexed: 01/06/2023]
Abstract
Neurodegenerative diseases (NDDs), such as Alzheimer's (AD) and Parkinson's (PD), are among the leading causes of lost years of healthy life and exert a great strain on public healthcare systems. Despite being first described more than a century ago, no effective cure exists for AD or PD. Although extensively characterised at the molecular level, traditional neurodegeneration research remains marred by narrow-sense approaches surrounding amyloid β (Aβ), tau, and α-synuclein (α-syn). A systems biology approach enables the integration of multi-omics data and informs discovery of biomarkers, drug targets, and treatment strategies. Here, we present a comprehensive timeline of high-throughput data collection, and associated biotechnological advancements and computational analysis related to AD and PD. We hereby propose that a philosophical change in the definitions of AD and PD is now needed.
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Affiliation(s)
- Simon Lam
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK
| | - Abdulahad Bayraktar
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK
| | - Cheng Zhang
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-17121, Sweden
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum, 25240, Turkey
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-41296, Gothenburg, Sweden
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg, The Wallenberg Laboratory, Sahlgrenska University Hospital, Gothenburg, SE-413 45, Sweden
| | - Saeed Shoaie
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK; Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-17121, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-17121, Sweden
| | - Adil Mardinoglu
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK; Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-17121, Sweden.
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Ozcan M, Altay O, Lam S, Turkez H, Aksoy Y, Nielsen J, Uhlen M, Boren J, Mardinoglu A. Improvement in the Current Therapies for Hepatocellular Carcinoma Using a Systems Medicine Approach. ACTA ACUST UNITED AC 2020; 4:e2000030. [PMID: 32529800 DOI: 10.1002/adbi.202000030] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 03/02/2020] [Accepted: 03/09/2020] [Indexed: 12/24/2022]
Abstract
Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death primarily due to the lack of effective targeted therapies. Despite the distinct morphological and phenotypic patterns of HCC, treatment strategies are restricted to relatively homogeneous therapies, including multitargeted tyrosine kinase inhibitors and immune checkpoint inhibitors. Therefore, more effective therapy options are needed to target dysregulated metabolic and molecular pathways in HCC. Integrative genomic profiling of HCC patients provides insight into the most frequently mutated genes and molecular targets, including telomerase reverse transcriptase, the TP53 gene, and the Wnt/β-catenin signaling pathway oncogene (CTNNB1). Moreover, emerging techniques, such as genome-scale metabolic models may elucidate the underlying cancer-specific metabolism, which allows for the discovery of potential drug targets and identification of biomarkers. De novo lipogenesis has been revealed as consistently upregulated since it is required for cell proliferation in all HCC patients. The metabolic network-driven stratification of HCC patients in terms of redox responses, utilization of metabolites, and subtype-specific pathways may have clinical implications to drive the development of personalized medicine. In this review, the current and emerging therapeutic targets in light of molecular approaches and metabolic network-based strategies are summarized, prompting effective treatment of HCC patients.
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Affiliation(s)
- Mehmet Ozcan
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE 17121, Sweden.,Department of Medical Biochemistry, Faculty of Medicine, Hacettepe University, Ankara, 06100, Turkey
| | - Ozlem Altay
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE 17121, Sweden
| | - Simon Lam
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum, 25240, Turkey
| | - Yasemin Aksoy
- Department of Medical Biochemistry, Faculty of Medicine, Hacettepe University, Ankara, 06100, Turkey
| | - Jens Nielsen
- Prof. J. Nielsen, Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE-41296, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE 17121, Sweden
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg, The Wallenberg Laboratory, Sahlgrenska University Hospital, Gothenburg, SE-413 45, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE 17121, Sweden.,Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK
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Lam S, Doran S, Yuksel HH, Altay O, Turkez H, Nielsen J, Boren J, Uhlen M, Mardinoglu A. Addressing the heterogeneity in liver diseases using biological networks. Brief Bioinform 2020; 22:1751-1766. [PMID: 32201876 PMCID: PMC7986590 DOI: 10.1093/bib/bbaa002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 11/28/2019] [Accepted: 01/03/2020] [Indexed: 12/19/2022] Open
Abstract
The abnormalities in human metabolism have been implicated in the progression of several complex human diseases, including certain cancers. Hence, deciphering the underlying molecular mechanisms associated with metabolic reprogramming in a disease state can greatly assist in elucidating the disease aetiology. An invaluable tool for establishing connections between global metabolic reprogramming and disease development is the genome-scale metabolic model (GEM). Here, we review recent work on the reconstruction of cell/tissue-type and cancer-specific GEMs and their use in identifying metabolic changes occurring in response to liver disease development, stratification of the heterogeneous disease population and discovery of novel drug targets and biomarkers. We also discuss how GEMs can be integrated with other biological networks for generating more comprehensive cell/tissue models. In addition, we review the various biological network analyses that have been employed for the development of efficient treatment strategies. Finally, we present three case studies in which independent studies converged on conclusions underlying liver disease.
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Affiliation(s)
- Simon Lam
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, United Kingdom; Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-17121, Sweden
| | - Stephen Doran
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, United Kingdom; Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-17121, Sweden
| | - Hatice Hilal Yuksel
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, United Kingdom; Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-17121, Sweden
| | - Ozlem Altay
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, United Kingdom; Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-17121, Sweden
| | - Hasan Turkez
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, United Kingdom; Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-17121, Sweden
| | - Jens Nielsen
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, United Kingdom; Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-17121, Sweden
| | - Jan Boren
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, United Kingdom; Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-17121, Sweden
| | - Mathias Uhlen
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, United Kingdom; Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-17121, Sweden
| | - Adil Mardinoglu
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, United Kingdom; Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-17121, Sweden
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Li X, Turanli B, Juszczak K, Kim W, Arif M, Sato Y, Ogawa S, Turkez H, Nielsen J, Boren J, Uhlen M, Zhang C, Mardinoglu A. Classification of clear cell renal cell carcinoma based on PKM alternative splicing. Heliyon 2020; 6:e03440. [PMID: 32095654 PMCID: PMC7033363 DOI: 10.1016/j.heliyon.2020.e03440] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 02/07/2020] [Accepted: 02/14/2020] [Indexed: 01/17/2023] Open
Abstract
Clear cell renal cell carcinoma (ccRCC) accounts for 70-80% of kidney cancer diagnoses and displays high molecular and histologic heterogeneity. Hence, it is necessary to reveal the underlying molecular mechanisms involved in progression of ccRCC to better stratify the patients and design effective treatment strategies. Here, we analyzed the survival outcome of ccRCC patients as a consequence of the differential expression of four transcript isoforms of the pyruvate kinase muscle type (PKM). We first extracted a classification biomarker consisting of eight gene pairs whose within-sample relative expression orderings (REOs) could be used to robustly classify the patients into two groups with distinct molecular characteristics and survival outcomes. Next, we validated our findings in a validation cohort and an independent Japanese ccRCC cohort. We finally performed drug repositioning analysis based on transcriptomic expression profiles of drug-perturbed cancer cell lines and proposed that paracetamol, nizatidine, dimethadione and conessine can be repurposed to treat the patients in one of the subtype of ccRCC whereas chenodeoxycholic acid, fenoterol and hexylcaine can be repurposed to treat the patients in the other subtype.
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Affiliation(s)
- Xiangyu Li
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Beste Turanli
- Department of Bioengineering, Istanbul Medeniyet University, Istanbul, Turkey
| | - Kajetan Juszczak
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Woonghee Kim
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Muhammad Arif
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Yusuke Sato
- Department of Pathology and Tumor Biology, Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
- Department of Urology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Seishi Ogawa
- Department of Pathology and Tumor Biology, Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
- Department of Medicine, Centre for Hematology and Regenerative Medicine, Karolinska Institute, Stockholm, Sweden
| | - Hasan Turkez
- Department of Molecular Biology and Genetics, Erzurum Technical University, Erzurum, 25240, Turkey
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
- School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, PR China
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
- Centre for Host–Microbiome Interactions, Dental Institute, King's College London, London, SE1 9RT, United Kingdom
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Uhlen M, Karlsson MJ, Zhong W, Tebani A, Pou C, Mikes J, Lakshmikanth T, Forsström B, Edfors F, Odeberg J, Mardinoglu A, Zhang C, von Feilitzen K, Mulder J, Sjöstedt E, Hober A, Oksvold P, Zwahlen M, Ponten F, Lindskog C, Sivertsson Å, Fagerberg L, Brodin P. A genome-wide transcriptomic analysis of protein-coding genes in human blood cells. Science 2019; 366:366/6472/eaax9198. [DOI: 10.1126/science.aax9198] [Citation(s) in RCA: 183] [Impact Index Per Article: 36.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 11/06/2019] [Indexed: 12/14/2022]
Abstract
Blood is the predominant source for molecular analyses in humans, both in clinical and research settings. It is the target for many therapeutic strategies, emphasizing the need for comprehensive molecular maps of the cells constituting human blood. In this study, we performed a genome-wide transcriptomic analysis of protein-coding genes in sorted blood immune cell populations to characterize the expression levels of each individual gene across the blood cell types. All data are presented in an interactive, open-access Blood Atlas as part of the Human Protein Atlas and are integrated with expression profiles across all major tissues to provide spatial classification of all protein-coding genes. This allows for a genome-wide exploration of the expression profiles across human immune cell populations and all major human tissues and organs.
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Affiliation(s)
- Mathias Uhlen
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Max J. Karlsson
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Wen Zhong
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Abdellah Tebani
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Christian Pou
- Science for Life Laboratory, Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden
| | - Jaromir Mikes
- Science for Life Laboratory, Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden
| | - Tadepally Lakshmikanth
- Science for Life Laboratory, Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden
| | - Björn Forsström
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Fredrik Edfors
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Jacob Odeberg
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
- Coagulation Unit, Department of Hematology, Karolinska University Hospital, Stockholm, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London, UK
| | - Cheng Zhang
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Kalle von Feilitzen
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Jan Mulder
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Evelina Sjöstedt
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Andreas Hober
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Per Oksvold
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Martin Zwahlen
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Fredrik Ponten
- Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - Cecilia Lindskog
- Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - Åsa Sivertsson
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Linn Fagerberg
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Petter Brodin
- Science for Life Laboratory, Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden
- Unit of Pediatric Rheumatology, Karolinska University Hospital, Stockholm, Sweden
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Altay O, Nielsen J, Uhlen M, Boren J, Mardinoglu A. Systems biology perspective for studying the gut microbiota in human physiology and liver diseases. EBioMedicine 2019; 49:364-373. [PMID: 31636011 PMCID: PMC6945237 DOI: 10.1016/j.ebiom.2019.09.057] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 09/21/2019] [Accepted: 09/23/2019] [Indexed: 02/06/2023] Open
Abstract
The advancement in high-throughput sequencing technologies and systems biology approaches have revolutionized our understanding of biological systems and opened a new path to investigate unacknowledged biological phenomena. In parallel, the field of human microbiome research has greatly evolved and the relative contribution of the gut microbiome to health and disease have been systematically explored. This review provides an overview of the network-based and translational systems biology-based studies focusing on the function and composition of gut microbiota. We also discussed the association between the gut microbiome and the overall human physiology, as well as hepatic diseases and other metabolic disorders.
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Affiliation(s)
- Ozlem Altay
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden.
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden; Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, United Kingdom.
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Turanli B, Altay O, Borén J, Turkez H, Nielsen J, Uhlen M, Arga KY, Mardinoglu A. Systems biology based drug repositioning for development of cancer therapy. Semin Cancer Biol 2019; 68:47-58. [PMID: 31568815 DOI: 10.1016/j.semcancer.2019.09.020] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 09/23/2019] [Accepted: 09/24/2019] [Indexed: 01/20/2023]
Abstract
Drug repositioning is a powerful method that can assists the conventional drug discovery process by using existing drugs for treatment of a disease rather than its original indication. The first examples of repurposed drugs were discovered serendipitously, however data accumulated by high-throughput screenings and advancements in computational biology methods have paved the way for rational drug repositioning methods. As chemotherapeutic agents have notorious side effects that significantly reduce quality of life, drug repositioning promises repurposed noncancer drugs with little or tolerable adverse effects for cancer patients. Here, we review current drug-related data types and databases including some examples of web-based drug repositioning tools. Next, we describe systems biology approaches to be used in drug repositioning for effective cancer therapy. Finally, we highlight examples of mostly repurposed drugs for cancer treatment and provide an overview of future expectations in the field for development of effective treatment strategies.
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Affiliation(s)
- Beste Turanli
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-17121, Sweden; Department of Bioengineering, Marmara University, Istanbul, Turkey; Department of Bioengineering, Istanbul Medeniyet University, Istanbul, Turkey
| | - Ozlem Altay
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-17121, Sweden
| | - Jan Borén
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital Gothenburg, Sweden
| | - Hasan Turkez
- Department of Molecular Biology and Genetics, Erzurum Technical University, Erzurum 25240, Turkey
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-17121, Sweden
| | | | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-17121, Sweden; Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London SE1 9RT, United Kingdom.
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Lee S, Zhang C, Arif M, Liu Z, Benfeitas R, Bidkhori G, Deshmukh S, Al Shobky M, Lovric A, Boren J, Nielsen J, Uhlen M, Mardinoglu A. TCSBN: a database of tissue and cancer specific biological networks. Nucleic Acids Res 2019; 46:D595-D600. [PMID: 29069445 PMCID: PMC5753183 DOI: 10.1093/nar/gkx994] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 10/12/2017] [Indexed: 12/15/2022] Open
Abstract
Biological networks provide new opportunities for understanding the cellular biology in both health and disease states. We generated tissue specific integrated networks (INs) for liver, muscle and adipose tissues by integrating metabolic, regulatory and protein-protein interaction networks. We also generated human co-expression networks (CNs) for 46 normal tissues and 17 cancers to explore the functional relationships between genes as well as their relationships with biological functions, and investigate the overlap between functional and physical interactions provided by CNs and INs, respectively. These networks can be employed in the analysis of omics data, provide detailed insight into disease mechanisms by identifying the key biological components and eventually can be used in the development of efficient treatment strategies. Moreover, comparative analysis of the networks may allow for the identification of tissue-specific targets that can be used in the development of drugs with the minimum toxic effect to other human tissues. These context-specific INs and CNs are presented in an interactive website http://inetmodels.com without any limitation.
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Affiliation(s)
- Sunjae Lee
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Muhammad Arif
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Zhengtao Liu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Rui Benfeitas
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Gholamreza Bidkhori
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Sumit Deshmukh
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Mohamed Al Shobky
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Alen Lovric
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, SE-413 45, Sweden
| | - Jens Nielsen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden.,Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE-412 96, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden.,Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE-412 96, Sweden
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Abstract
Multi-omics multi-tissue data are used to interpret genome-wide association study results from mice to identify key driver genes of non-alcoholic fatty liver disease. Non-alcoholic fatty liver disease (NAFLD) is the accumulation of fat (steatosis) in the liver due to causes other than excessive alcohol consumption. The disease may progress to more severe forms of liver diseases, including non-alcoholic steatohepatitis, cirrhosis, and hepatocellular carcinoma. In this issue of Cell Systems, Krishnan et al. (2018) reveal mechanisms underlying NAFLD by generating multi-omics data using liver and adipose tissues obtained from the Hybrid Mouse Diversity Panel, consisting of 113 mouse strains with various degrees of NAFLD. The study identified key driver genes of NAFLD that can be used in the development of efficient treatment strategies and illustrates the potential utility of systematic analysis of multi-layer biological networks.
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Affiliation(s)
- Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden; Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Jan Borén
- Department of Molecular and Clinical Medicine/Wallenberg Laboratory, University of Gothenburg, and Sahlgrenska University Hospital, Gothenburg, Sweden
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40
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Edfors F, Forsström B, Vunk H, Kotol D, Fredolini C, Maddalo G, Svensson AS, Boström T, Tegel H, Nilsson P, Schwenk JM, Uhlen M. Screening a Resource of Recombinant Protein Fragments for Targeted Proteomics. J Proteome Res 2019; 18:2706-2718. [PMID: 31094526 DOI: 10.1021/acs.jproteome.8b00924] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The availability of proteomics resources hosting protein and peptide standards, as well as the data describing their analytical performances, will continue to enhance our current capabilities to develop targeted proteomics methods for quantitative biology. This study describes the analysis of a resource of 26,840 individually purified recombinant protein fragments corresponding to more than 16,000 human protein-coding genes. The resource was screened to identify proteotypic peptides suitable for targeted proteomics efforts, and we report LC-MS/MS assay coordinates for more than 25,000 proteotypic peptides, corresponding to more than 10,000 unique proteins. Additionally, peptide formation and digestion kinetics were, for a subset of the standards, monitored using a time-course protocol involving parallel digestion of isotope-labeled recombinant protein standards and endogenous human plasma proteins. We show that the strategy by adding isotope-labeled recombinant proteins before trypsin digestion enables short digestion protocols (≤60 min) with robust quantitative precision. In a proof-of-concept study, we quantified 23 proteins in human plasma using assay parameters defined in our study and used the standards to describe distinct clusters of individuals linked to different levels of LPA, APOE, SERPINA5, and TFRC. In summary, we describe the use and utility of a resource of recombinant proteins to identify proteotypic peptides useful for targeted proteomics assay development.
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Affiliation(s)
- Fredrik Edfors
- Science for Life Laboratory, Division of Systems Biology, Department of Protein Science , KTH-Royal Institute of Technology , SE - 171 21 Stockholm , Sweden
| | - Björn Forsström
- Science for Life Laboratory, Division of Systems Biology, Department of Protein Science , KTH-Royal Institute of Technology , SE - 171 21 Stockholm , Sweden
| | - Helian Vunk
- Science for Life Laboratory, Division of Systems Biology, Department of Protein Science , KTH-Royal Institute of Technology , SE - 171 21 Stockholm , Sweden
| | - David Kotol
- Science for Life Laboratory, Division of Systems Biology, Department of Protein Science , KTH-Royal Institute of Technology , SE - 171 21 Stockholm , Sweden
| | - Claudia Fredolini
- Science for Life Laboratory, Division of Affinity Proteomics, Department of Protein Science , KTH-Royal Institute of Technology , SE - 171 21 Stockholm , Sweden
| | - Gianluca Maddalo
- Science for Life Laboratory, Division of Systems Biology, Department of Protein Science , KTH-Royal Institute of Technology , SE - 171 21 Stockholm , Sweden
| | - Anne-Sophie Svensson
- Albanova University Center , KTH-Royal Institute of Technology , SE - 171 21 Stockholm , Sweden
| | - Tove Boström
- Albanova University Center , KTH-Royal Institute of Technology , SE - 171 21 Stockholm , Sweden.,Atlas Antibodies AB , SE - 114 21 Stockholm , Sweden
| | - Hanna Tegel
- Albanova University Center , KTH-Royal Institute of Technology , SE - 171 21 Stockholm , Sweden
| | - Peter Nilsson
- Science for Life Laboratory, Division of Affinity Proteomics, Department of Protein Science , KTH-Royal Institute of Technology , SE - 171 21 Stockholm , Sweden
| | - Jochen M Schwenk
- Science for Life Laboratory, Division of Affinity Proteomics, Department of Protein Science , KTH-Royal Institute of Technology , SE - 171 21 Stockholm , Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, Division of Systems Biology, Department of Protein Science , KTH-Royal Institute of Technology , SE - 171 21 Stockholm , Sweden.,Albanova University Center , KTH-Royal Institute of Technology , SE - 171 21 Stockholm , Sweden.,Department of Neuroscience - Karolinska Institute , SE - 171 65 Solna , Sweden.,Novo Nordisk Foundation Center for Biosustainability , Technical University of Denmark , DK - 2970 Hørsholm , Denmark
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41
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Turanli B, Karagoz K, Bidkhori G, Sinha R, Gatza ML, Uhlen M, Mardinoglu A, Arga KY. Multi-Omic Data Interpretation to Repurpose Subtype Specific Drug Candidates for Breast Cancer. Front Genet 2019; 10:420. [PMID: 31134131 PMCID: PMC6514249 DOI: 10.3389/fgene.2019.00420] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Accepted: 04/17/2019] [Indexed: 12/20/2022] Open
Abstract
Triple-negative breast cancer (TNBC), which is largely synonymous with the basal-like molecular subtype, is the 5th leading cause of cancer deaths for women in the United States. The overall prognosis for TNBC patients remains poor given that few treatment options exist; including targeted therapies (not FDA approved), and multi-agent chemotherapy as standard-of-care treatment. TNBC like other complex diseases is governed by the perturbations of the complex interaction networks thereby elucidating the underlying molecular mechanisms of this disease in the context of network principles, which have the potential to identify targets for drug development. Here, we present an integrated "omics" approach based on the use of transcriptome and interactome data to identify dynamic/active protein-protein interaction networks (PPINs) in TNBC patients. We have identified three highly connected modules, EED, DHX9, and AURKA, which are extremely activated in TNBC tumors compared to both normal tissues and other breast cancer subtypes. Based on the functional analyses, we propose that these modules are potential drivers of proliferation and, as such, should be considered candidate molecular targets for drug development or drug repositioning in TNBC. Consistent with this argument, we repurposed steroids, anti-inflammatory agents, anti-infective agents, cardiovascular agents for patients with basal-like breast cancer. Finally, we have performed essential metabolite analysis on personalized genome-scale metabolic models and found that metabolites such as sphingosine-1-phosphate and cholesterol-sulfate have utmost importance in TNBC tumor growth.
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Affiliation(s)
- Beste Turanli
- Department of Bioengineering, Marmara University, Istanbul, Turkey.,Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.,Department of Bioengineering, Istanbul Medeniyet University, Istanbul, Turkey
| | - Kubra Karagoz
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States
| | - Gholamreza Bidkhori
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Raghu Sinha
- Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, PA, United States
| | - Michael L Gatza
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.,Faculty of Dentistry, Oral and Craniofacial Sciences, Centre for Host-Microbiome Interactions, King's College London, London, United Kingdom.,Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
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42
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Turanli B, Zhang C, Kim W, Benfeitas R, Uhlen M, Arga KY, Mardinoglu A. Discovery of therapeutic agents for prostate cancer using genome-scale metabolic modeling and drug repositioning. EBioMedicine 2019; 42:386-396. [PMID: 30905848 PMCID: PMC6491384 DOI: 10.1016/j.ebiom.2019.03.009] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Revised: 02/28/2019] [Accepted: 03/04/2019] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Genome-scale metabolic models (GEMs) offer insights into cancer metabolism and have been used to identify potential biomarkers and drug targets. Drug repositioning is a time- and cost-effective method of drug discovery that can be applied together with GEMs for effective cancer treatment. METHODS In this study, we reconstruct a prostate cancer (PRAD)-specific GEM for exploring prostate cancer metabolism and also repurposing new therapeutic agents that can be used in development of effective cancer treatment. We integrate global gene expression profiling of cell lines with >1000 different drugs through the use of prostate cancer GEM and predict possible drug-gene interactions. FINDINGS We identify the key reactions with altered fluxes based on the gene expression changes and predict the potential drug effect in prostate cancer treatment. We find that sulfamethoxypyridazine, azlocillin, hydroflumethiazide, and ifenprodil can be repurposed for the treatment of prostate cancer based on an in silico cell viability assay. Finally, we validate the effect of ifenprodil using an in vitro cell assay and show its inhibitory effect on a prostate cancer cell line. INTERPRETATION Our approach demonstate how GEMs can be used to predict therapeutic agents for cancer treatment based on drug repositioning. Besides, it paved a way and shed a light on the applicability of computational models to real-world biomedical or pharmaceutical problems.
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Affiliation(s)
- Beste Turanli
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-17121, Sweden; Department of Bioengineering, Marmara University, Istanbul, Turkey; Department of Bioengineering, Istanbul Medeniyet University, Istanbul, Turkey
| | - Cheng Zhang
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-17121, Sweden
| | - Woonghee Kim
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-17121, Sweden
| | - Rui Benfeitas
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-17121, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-17121, Sweden
| | | | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-17121, Sweden; Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg SE-41296, Sweden; Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, United Kingdom.
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43
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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: 394] [Impact Index Per Article: 78.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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44
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Lundgren S, Fagerström-Vahman H, Zhang C, Ben-Dror L, Mardinoglu A, Uhlen M, Nodin B, Jirström K. Discovery of KIRREL as a biomarker for prognostic stratification of patients with thin melanoma. Biomark Res 2019; 7:1. [PMID: 30675360 PMCID: PMC6332842 DOI: 10.1186/s40364-018-0153-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Accepted: 12/28/2018] [Indexed: 12/20/2022] Open
Abstract
There is a great unmet clinical need to identify patients with thin primary cutaneous melanomas (T1, Breslow thickness ≤ 1 mm) who have a high risk for tumour recurrence and death from melanoma. Kin of IRRE-like protein 1 (KIRREL/NEPH1) is expressed in podocytes and involved in glomerular filtration. Screening in the Human Protein Atlas portal revealed a particularly high expression of KIRREL in melanoma, both at the mRNA and protein levels. In this study, we followed up on these findings and examined the prognostic value of KIRREL in a population-based cohort. Immunohistochemical expression of KIRREL was examined in tissue microarrays with a subset of primary tumours and paired lymph node metastases from an original cohort of 268 incident cases of melanoma in the Malmö Diet and Cancer study. KIRREL mRNA expression was examined in 103 melanoma cases in The Cancer Genome Atlas (TCGA). Membranous/cytoplasmic expression of KIRREL was detected in 158/185 (85.4%) primary tumours and 18/19 (94.7%) metastases. High expression of KIRREL was significantly associated with several unfavourable clinicopathological factors. High KIRREL protein expression was an independent factor of reduced recurrence free and melanoma specific survival, particularly in thin melanomas, even outperforming absolute thickness and ulceration (HR = 30.85; 95% CI 1.54-616.36 and HR = 6.32 95% CI 1.19-33.65). High mRNA levels of KIRREL were not significantly associated with survival in TCGA. In conclusion, KIRREL is not only a novel potential diagnostic marker for melanoma, but may also be a useful prognostic biomarker for improved stratification of patients with thin melanoma. These findings may be of high clinical relevance and therefore merit further validation.
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Affiliation(s)
- Sebastian Lundgren
- 1Department of Clinical Sciences Lund, Oncology and Pathology, Lund University, Lund, Sweden
| | | | - Cheng Zhang
- 2Science for Life Laboratory, Department of Proteomics, School of Biotechnology, Royal Institute of Technology, Stockholm, Sweden
| | - Liv Ben-Dror
- 1Department of Clinical Sciences Lund, Oncology and Pathology, Lund University, Lund, Sweden
| | - Adil Mardinoglu
- 2Science for Life Laboratory, Department of Proteomics, School of Biotechnology, Royal Institute of Technology, Stockholm, Sweden
| | - Mathias Uhlen
- 2Science for Life Laboratory, Department of Proteomics, School of Biotechnology, Royal Institute of Technology, Stockholm, Sweden
| | - Björn Nodin
- 1Department of Clinical Sciences Lund, Oncology and Pathology, Lund University, Lund, Sweden
| | - Karin Jirström
- 1Department of Clinical Sciences Lund, Oncology and Pathology, Lund University, Lund, Sweden
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45
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Liu Z, Zhang C, Lee S, Kim W, Klevstig M, Harzandi AM, Sikanic N, Arif M, Ståhlman M, Nielsen J, Uhlen M, Boren J, Mardinoglu A. Pyruvate kinase L/R is a regulator of lipid metabolism and mitochondrial function. Metab Eng 2019; 52:263-272. [PMID: 30615941 DOI: 10.1016/j.ymben.2019.01.001] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 12/26/2018] [Accepted: 01/03/2019] [Indexed: 02/07/2023]
Abstract
The pathogenesis of non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC) has been associated with altered expression of liver-specific genes including pyruvate kinase liver and red blood cell (PKLR), patatin-like phospholipase domain containing 3 (PNPLA3) and proprotein convertase subtilisin/kexin type 9 (PCSK9). Here, we inhibited and overexpressed the expression of these three genes in HepG2 cells, generated RNA-seq data before and after perturbation and revealed the altered global biological functions with the modulation of these genes using integrated network (IN) analysis. We found that modulation of these genes effects the total triglycerides levels within the cells and viability of the cells. Next, we generated IN for HepG2 cells, identified reporter transcription factors based on IN and found that the modulation of these genes affects key metabolic pathways associated with lipid metabolism (steroid biosynthesis, PPAR signalling pathway, fatty acid synthesis and oxidation) and cancer development (DNA replication, cell cycle and p53 signalling) involved in the progression of NAFLD and HCC. Finally, we observed that inhibition of PKLR lead to decreased glucose uptake and decreased mitochondrial activity in HepG2 cells. Hence, our systems level analysis indicated that PKLR can be targeted for development efficient treatment strategy for NAFLD and HCC.
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Affiliation(s)
- Zhengtao Liu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Sunjae Lee
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Woonghee Kim
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Martina Klevstig
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Azadeh M Harzandi
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Natasa Sikanic
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Muhammad Arif
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Marcus Ståhlman
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden; Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden; Centre for Host-Microbiome Interactions, Dental Institute, King's College London, London SE1 9RT, United Kingdom.
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46
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Benfeitas R, Bidkhori G, Mukhopadhyay B, Klevstig M, Arif M, Zhang C, Lee S, Cinar R, Nielsen J, Uhlen M, Boren J, Kunos G, Mardinoglu A. Characterization of heterogeneous redox responses in hepatocellular carcinoma patients using network analysis. EBioMedicine 2018; 40:471-487. [PMID: 30606699 PMCID: PMC6412169 DOI: 10.1016/j.ebiom.2018.12.057] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2018] [Revised: 12/20/2018] [Accepted: 12/26/2018] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Redox metabolism is often considered a potential target for cancer treatment, but a systematic examination of redox responses in hepatocellular carcinoma (HCC) is missing. METHODS Here, we employed systems biology and biological network analyses to reveal key roles of genes associated with redox metabolism in HCC by integrating multi-omics data. FINDINGS We found that several redox genes, including 25 novel potential prognostic genes, are significantly co-expressed with liver-specific genes and genes associated with immunity and inflammation. Based on an integrative analysis, we found that HCC tumors display antagonistic behaviors in redox responses. The two HCC groups are associated with altered fatty acid, amino acid, drug and hormone metabolism, differentiation, proliferation, and NADPH-independent vs -dependent antioxidant defenses. Redox behavior varies with known tumor subtypes and progression, affecting patient survival. These antagonistic responses are also displayed at the protein and metabolite level and were validated in several independent cohorts. We finally showed the differential redox behavior using mice transcriptomics in HCC and noncancerous tissues and associated with hypoxic features of the two redox gene groups. INTERPRETATION Our integrative approaches highlighted mechanistic differences among tumors and allowed the identification of a survival signature and several potential therapeutic targets for the treatment of HCC.
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Affiliation(s)
- Rui Benfeitas
- Science for Life Laboratory, KTH - Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
| | - Gholamreza Bidkhori
- Science for Life Laboratory, KTH - Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
| | - Bani Mukhopadhyay
- Laboratory of Physiologic Studies, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA.
| | - Martina Klevstig
- Department of Molecular and Clinical Medicine, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden.
| | - Muhammad Arif
- Science for Life Laboratory, KTH - Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
| | - Cheng Zhang
- Science for Life Laboratory, KTH - Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
| | - Sunjae Lee
- Science for Life Laboratory, KTH - Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
| | - Resat Cinar
- Laboratory of Physiologic Studies, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA.
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden.
| | - George Kunos
- Laboratory of Physiologic Studies, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA.
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, SE-171 21 Stockholm, Sweden; Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden; Centre for Host-Microbiome Interactions, Dental Institute, King's College London, London, UK.
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47
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Hu FJ, Volk AL, Persson H, Säll A, Borrebaeck C, Uhlen M, Rockberg J. Combination of phage and Gram-positive bacterial display of human antibody repertoires enables isolation of functional high affinity binders. N Biotechnol 2018; 45:80-88. [DOI: 10.1016/j.nbt.2017.07.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Revised: 06/28/2017] [Accepted: 07/31/2017] [Indexed: 10/19/2022]
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Bidkhori G, Benfeitas R, Elmas E, Kararoudi MN, Arif M, Uhlen M, Nielsen J, Mardinoglu A. Metabolic Network-Based Identification and Prioritization of Anticancer Targets Based on Expression Data in Hepatocellular Carcinoma. Front Physiol 2018; 9:916. [PMID: 30065658 PMCID: PMC6056771 DOI: 10.3389/fphys.2018.00916] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Accepted: 06/22/2018] [Indexed: 12/23/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is a deadly form of liver cancer with high mortality worldwide. Unfortunately, the large heterogeneity of this disease makes it difficult to develop effective treatment strategies. Cellular network analyses have been employed to study heterogeneity in cancer, and to identify potential therapeutic targets. However, the existing approaches do not consider metabolic growth requirements, i.e., biological network functionality, to rank candidate targets while preventing toxicity to non-cancerous tissues. Here, we developed an algorithm to overcome these issues based on integration of gene expression data, genome-scale metabolic models, network controllability, and dispensability, as well as toxicity analysis. This method thus predicts and ranks potential anticancer non-toxic controlling metabolite and gene targets. Our algorithm encompasses both objective-driven and-independent tasks, and uses network topology to finally rank the predicted therapeutic targets. We employed this algorithm to the analysis of transcriptomic data for 50 HCC patients with both cancerous and non-cancerous samples. We identified several potential targets that would prevent cell growth, including 74 anticancer metabolites, and 3 gene targets (PRKACA, PGS1, and CRLS1). The predicted anticancer metabolites showed good agreement with existing FDA-approved cancer drugs, and the 3 genes were experimentally validated by performing experiments in HepG2 and Hep3B liver cancer cell lines. Our observations indicate that our novel approach successfully identifies therapeutic targets for effective treatment of cancer. This approach may also be applied to any cancer type that has tumor and non-tumor gene or protein expression data.
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Affiliation(s)
- Gholamreza Bidkhori
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Rui Benfeitas
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Ezgi Elmas
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | | | - Muhammad Arif
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden.,Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
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Rosario D, Benfeitas R, Bidkhori G, Zhang C, Uhlen M, Shoaie S, Mardinoglu A. Understanding the Representative Gut Microbiota Dysbiosis in Metformin-Treated Type 2 Diabetes Patients Using Genome-Scale Metabolic Modeling. Front Physiol 2018; 9:775. [PMID: 29988585 PMCID: PMC6026676 DOI: 10.3389/fphys.2018.00775] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 06/04/2018] [Indexed: 01/21/2023] Open
Abstract
Dysbiosis in the gut microbiome composition may be promoted by therapeutic drugs such as metformin, the world’s most prescribed antidiabetic drug. Under metformin treatment, disturbances of the intestinal microbes lead to increased abundance of Escherichia spp., Akkermansia muciniphila, Subdoligranulum variabile and decreased abundance of Intestinibacter bartlettii. This alteration may potentially lead to adverse effects on the host metabolism, with the depletion of butyrate producer genus. However, an increased production of butyrate and propionate was verified in metformin-treated Type 2 diabetes (T2D) patients. The mechanisms underlying these nutritional alterations and their relation with gut microbiota dysbiosis remain unclear. Here, we used Genome-scale Metabolic Models of the representative gut bacteria Escherichia spp., I. bartlettii, A. muciniphila, and S. variabile to elucidate their bacterial metabolism and its effect on intestinal nutrient pool, including macronutrients (e.g., amino acids and short chain fatty acids), minerals and chemical elements (e.g., iron and oxygen). We applied flux balance analysis (FBA) coupled with synthetic lethality analysis interactions to identify combinations of reactions and extracellular nutrients whose absence prevents growth. Our analyses suggest that Escherichia sp. is the bacteria least vulnerable to nutrient availability. We have also examined bacterial contribution to extracellular nutrients including short chain fatty acids, amino acids, and gasses. For instance, Escherichia sp. and S. variabile may contribute to the production of important short chain fatty acids (e.g., acetate and butyrate, respectively) involved in the host physiology under aerobic and anaerobic conditions. We have also identified pathway susceptibility to nutrient availability and reaction changes among the four bacteria using both FBA and flux variability analysis. For instance, lipopolysaccharide synthesis, nucleotide sugar metabolism, and amino acid metabolism are pathways susceptible to changes in Escherichia sp. and A. muciniphila. Our observations highlight important commensal and competing behavior, and their association with cellular metabolism for prevalent gut microbes. The results of our analysis have potential important implications for development of new therapeutic approaches in T2D patients through the development of prebiotics, probiotics, or postbiotics.
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Affiliation(s)
- Dorines Rosario
- Science for Life Laboratory, Royal Institute of Technology, Stockholm, Sweden
| | - Rui Benfeitas
- Science for Life Laboratory, Royal Institute of Technology, Stockholm, Sweden
| | - Gholamreza Bidkhori
- Science for Life Laboratory, Royal Institute of Technology, Stockholm, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, Royal Institute of Technology, Stockholm, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, Royal Institute of Technology, Stockholm, Sweden
| | - Saeed Shoaie
- Centre for Host-Microbiome Interactions, Dental Institute, King's College London, London, United Kingdom.,Centre for Translational Microbiome Research, Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, Stockholm, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, Royal Institute of Technology, Stockholm, Sweden.,Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
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Abstract
Detailed insights into the biological functions of the liver and an understanding of its crosstalk with other human tissues and the gut microbiota can be used to develop novel strategies for the prevention and treatment of liver-associated diseases, including fatty liver disease, cirrhosis, hepatocellular carcinoma and type 2 diabetes mellitus. Biological network models, including metabolic, transcriptional regulatory, protein-protein interaction, signalling and co-expression networks, can provide a scaffold for studying the biological pathways operating in the liver in connection with disease development in a systematic manner. Here, we review studies in which biological network models were used to integrate multiomics data to advance our understanding of the pathophysiological responses of complex liver diseases. We also discuss how this mechanistic approach can contribute to the discovery of potential biomarkers and novel drug targets, which might lead to the design of targeted and improved treatment strategies. Finally, we present a roadmap for the successful integration of models of the liver and other human tissues with the gut microbiota to simulate whole-body metabolic functions in health and disease.
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Affiliation(s)
- Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden. .,Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Ulf Smith
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Jens Nielsen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.,Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
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