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Bilbao A, Munoz N, Kim J, Orton DJ, Gao Y, Poorey K, Pomraning KR, Weitz K, Burnet M, Nicora CD, Wilton R, Deng S, Dai Z, Oksen E, Gee A, Fasani RA, Tsalenko A, Tanjore D, Gardner J, Smith RD, Michener JK, Gladden JM, Baker ES, Petzold CJ, Kim YM, Apffel A, Magnuson JK, Burnum-Johnson KE. PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements. Nat Commun 2023; 14:2461. [PMID: 37117207 PMCID: PMC10147702 DOI: 10.1038/s41467-023-37031-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 02/24/2023] [Indexed: 04/30/2023] Open
Abstract
Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.
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Affiliation(s)
- Aivett Bilbao
- Pacific Northwest National Laboratory, Richland, WA, USA.
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA.
| | - Nathalie Munoz
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Joonhoon Kim
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Daniel J Orton
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Yuqian Gao
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | | | - Kyle R Pomraning
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Karl Weitz
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Meagan Burnet
- Pacific Northwest National Laboratory, Richland, WA, USA
| | | | - Rosemarie Wilton
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Argonne National Laboratory, Lemont, IL, USA
| | - Shuang Deng
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Ziyu Dai
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Ethan Oksen
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Aaron Gee
- Agilent Research Laboratories, Agilent Technologies, Santa Clara, CA, USA
| | - Rick A Fasani
- Agilent Research Laboratories, Agilent Technologies, Santa Clara, CA, USA
| | - Anya Tsalenko
- Agilent Research Laboratories, Agilent Technologies, Santa Clara, CA, USA
| | - Deepti Tanjore
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - James Gardner
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | | | - Joshua K Michener
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - John M Gladden
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Sandia National Laboratory, Livermore, CA, USA
| | - Erin S Baker
- Department of Chemistry, University of North Carolina, Chapel Hill, NC, USA
| | - Christopher J Petzold
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Young-Mo Kim
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Alex Apffel
- Agilent Research Laboratories, Agilent Technologies, Santa Clara, CA, USA
| | - Jon K Magnuson
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Kristin E Burnum-Johnson
- Pacific Northwest National Laboratory, Richland, WA, USA.
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA.
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2
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Gurdo N, Taylor Parkins SK, Fricano M, Wulff T, Nielsen LK, Nikel PI. Protocol for absolute quantification of proteins in Gram-negative bacteria based on QconCAT-based labeled peptides. STAR Protoc 2023; 4:102060. [PMID: 36853682 PMCID: PMC9881405 DOI: 10.1016/j.xpro.2023.102060] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/02/2022] [Accepted: 01/05/2023] [Indexed: 01/26/2023] Open
Abstract
Mass-spectrometry-based absolute protein quantification uses labeled quantification concatamer (QconCAT) as internal standards (ISs). To calculate the amount of protein(s), the ion intensity ratio between the analyte and its cognate IS is compared in each biological sample. The present protocol describes a systematic workflow to design, produce, and purify QconCATs and to quantify soluble proteins in Pseudomonas putida KT2440. Our methodology enables the quantification of detectable peptide and serves as a versatile platform to produce ISs for different biological systems.
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Affiliation(s)
- Nicolás Gurdo
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | | | - Martina Fricano
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | - Tune Wulff
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | - Lars Keld Nielsen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens Lyngby, Denmark; Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Pablo Iván Nikel
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens Lyngby, Denmark.
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3
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Batianis C, van Rosmalen RP, Major M, van Ee C, Kasiotakis A, Weusthuis RA, Martins Dos Santos VAP. A tunable metabolic valve for precise growth control and increased product formation in Pseudomonas putida. Metab Eng 2023; 75:47-57. [PMID: 36244546 DOI: 10.1016/j.ymben.2022.10.002] [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: 08/05/2022] [Revised: 10/06/2022] [Accepted: 10/08/2022] [Indexed: 11/06/2022]
Abstract
Metabolic engineering of microorganisms aims to design strains capable of producing valuable compounds under relevant industrial conditions and in an economically competitive manner. From this perspective, and beyond the need for a catalyst, biomass is essentially a cost-intensive, abundant by-product of a microbial conversion. Yet, few broadly applicable strategies focus on the optimal balance between product and biomass formation. Here, we present a genetic control module that can be used to precisely modulate growth of the industrial bacterial chassis Pseudomonas putida KT2440. The strategy is based on the controllable expression of the key metabolic enzyme complex pyruvate dehydrogenase (PDH) which functions as a metabolic valve. By tuning the PDH activity, we accurately controlled biomass formation, resulting in six distinct growth rates with parallel overproduction of excess pyruvate. We deployed this strategy to identify optimal growth patterns that improved the production yield of 2-ketoisovalerate and lycopene by 2.5- and 1.38-fold, respectively. This ability to dynamically steer fluxes to balance growth and production substantially enhances the potential of this remarkable microbial chassis for a wide range of industrial applications.
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Affiliation(s)
- Christos Batianis
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
| | - Rik P van Rosmalen
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
| | - Monika Major
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
| | - Cheyenne van Ee
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
| | - Alexandros Kasiotakis
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
| | - Ruud A Weusthuis
- Bioprocess Engineering, Wageningen University and Research, Wageningen, the Netherlands
| | - Vitor A P Martins Dos Santos
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands; Bioprocess Engineering, Wageningen University and Research, Wageningen, the Netherlands; LifeGlimmer GmbH, Berlin, Germany.
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4
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Song X. Statistical and Computational Methods for Proteogenomic Data Analysis. Methods Mol Biol 2023; 2629:271-303. [PMID: 36929082 DOI: 10.1007/978-1-0716-2986-4_13] [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] [Indexed: 03/18/2023]
Abstract
Proteins are the functional molecules for almost all cellular and biological processes. They are also the targets of most drugs. Proteins employ complex, multilevel regulations, so their abundance levels do not well correlated with their mRNA expression levels. The structure, activity, and functional roles of proteins are affected by posttranslational modifications (PTM), which are even less correlated with mRNA expression levels than protein abundances. Comprehensive characterization of the proteomics data is critical for understanding the molecular and cellular mechanisms of biological systems and developing news therapeutics. Current large-scale proteomic profiling technologies, such as mass spectrometry, provide relative identification of peptides and proteins, with data vulnerable to outliers, batch effects, and nonrandom missingness. In order to perform high-quality proteomic data analysis, we will first introduce a data preprocessing and quality control pipeline that includes normalization, outlier detection and removal, batch effect identification and handling, and missing data imputation. Then, we will describe several statistical methods that leverage well-processed proteomic data to generate scientific discoveries, especially with an integration with genomics and transcriptomics. These methods cover topics like association analysis, network construction, clustering, and cell-type deconvolution. To demonstrate these methods, we will use the proteogenomic data from the lung squamous cell carcinoma study of the Clinical Proteomic Tumor Analysis Consortium and provide sample codes for data access and analyses.
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Affiliation(s)
- Xiaoyu Song
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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5
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Patil N, Howe O, Cahill P, Byrne HJ. Monitoring and modelling the dynamics of the cellular glycolysis pathway: A review and future perspectives. Mol Metab 2022; 66:101635. [PMID: 36379354 PMCID: PMC9703637 DOI: 10.1016/j.molmet.2022.101635] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 10/28/2022] [Accepted: 11/06/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND The dynamics of the cellular glycolysis pathway underpin cellular function and dysfunction, and therefore ultimately health, disease, diagnostic and therapeutic strategies. Evolving our understanding of this fundamental process and its dynamics remains critical. SCOPE OF REVIEW This paper reviews the medical relevance of glycolytic pathway in depth and explores the current state of the art for monitoring and modelling the dynamics of the process. The future perspectives of label free, vibrational microspectroscopic techniques to overcome the limitations of the current approaches are considered. MAJOR CONCLUSIONS Vibrational microspectroscopic techniques can potentially operate in the niche area of limitations of other omics technologies for non-destructive, real-time, in vivo label-free monitoring of glycolysis dynamics at a cellular and subcellular level.
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Affiliation(s)
- Nitin Patil
- FOCAS Research Institute, Technological University Dublin, City Campus, Camden Row, Dublin 8, Ireland; School of Physics and Optometric & Clinical Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, Ireland.
| | - Orla Howe
- School of Biological and Health Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, Ireland
| | - Paul Cahill
- School of Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland
| | - Hugh J Byrne
- FOCAS Research Institute, Technological University Dublin, City Campus, Camden Row, Dublin 8, Ireland
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7
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Yang XL, Shi Y, Zhang DD, Xin R, Deng J, Wu TM, Wang HM, Wang PY, Liu JB, Li W, Ma YS, Fu D. Quantitative proteomics characterization of cancer biomarkers and treatment. Mol Ther Oncolytics 2021; 21:255-63. [PMID: 34095463 DOI: 10.1016/j.omto.2021.04.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Cancer accounted for 16% of all death worldwide in 2018. Significant progress has been made in understanding tumor occurrence, progression, diagnosis, treatment, and prognosis at the molecular level. However, genomics changes cannot truly reflect the state of protein activity in the body due to the poor correlation between genes and proteins. Quantitative proteomics, capable of quantifying the relatively different protein abundance in cancer patients, has been increasingly adopted in cancer research. Quantitative proteomics has great application potentials, including cancer diagnosis, personalized therapeutic drug selection, real-time therapeutic effects and toxicity evaluation, prognosis and drug resistance evaluation, and new therapeutic target discovery. In this review, the development, testing samples, and detection methods of quantitative proteomics are introduced. The biomarkers identified by quantitative proteomics for clinical diagnosis, prognosis, and drug resistance are reviewed. The challenges and prospects of quantitative proteomics for personalized medicine are also discussed.
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