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Reder GK, Bjurström EY, Brunnsåker D, Kronström F, Lasin P, Tiukova I, Savolainen OI, Dodds JN, May JC, Wikswo JP, McLean JA, King RD. AutonoMS: Automated Ion Mobility Metabolomic Fingerprinting. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024; 35:542-550. [PMID: 38310603 PMCID: PMC10921458 DOI: 10.1021/jasms.3c00396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/11/2024] [Accepted: 01/17/2024] [Indexed: 02/06/2024]
Abstract
Automation is dramatically changing the nature of laboratory life science. Robotic lab hardware that can perform manual operations with greater speed, endurance, and reproducibility opens an avenue for faster scientific discovery with less time spent on laborious repetitive tasks. A major bottleneck remains in integrating cutting-edge laboratory equipment into automated workflows, notably specialized analytical equipment, which is designed for human usage. Here we present AutonoMS, a platform for automatically running, processing, and analyzing high-throughput mass spectrometry experiments. AutonoMS is currently written around an ion mobility mass spectrometry (IM-MS) platform and can be adapted to additional analytical instruments and data processing flows. AutonoMS enables automated software agent-controlled end-to-end measurement and analysis runs from experimental specification files that can be produced by human users or upstream software processes. We demonstrate the use and abilities of AutonoMS in a high-throughput flow-injection ion mobility configuration with 5 s sample analysis time, processing robotically prepared chemical standards and cultured yeast samples in targeted and untargeted metabolomics applications. The platform exhibited consistency, reliability, and ease of use while eliminating the need for human intervention in the process of sample injection, data processing, and analysis. The platform paves the way toward a more fully automated mass spectrometry analysis and ultimately closed-loop laboratory workflows involving automated experimentation and analysis coupled to AI-driven experimentation utilizing cutting-edge analytical instrumentation. AutonoMS documentation is available at https://autonoms.readthedocs.io.
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Affiliation(s)
- Gabriel K. Reder
- Department
of Computer Science and Engineering, Chalmers
University of Technology, Gothenburg 412 96, Sweden
- Department
of Applied Physics, SciLifeLab, KTH Royal
Institute of Technology, Solna 171 21, Sweden
| | - Erik Y. Bjurström
- Department
of Life Sciences, Chalmers University of
Technology, Gothenburg 412 96, Sweden
| | - Daniel Brunnsåker
- Department
of Computer Science and Engineering, Chalmers
University of Technology, Gothenburg 412 96, Sweden
| | - Filip Kronström
- Department
of Computer Science and Engineering, Chalmers
University of Technology, Gothenburg 412 96, Sweden
| | - Praphapan Lasin
- Department
of Life Sciences, Chalmers University of
Technology, Gothenburg 412 96, Sweden
| | - Ievgeniia Tiukova
- Department
of Life Sciences, Chalmers University of
Technology, Gothenburg 412 96, Sweden
| | - Otto I. Savolainen
- Department
of Life Sciences, Chalmers University of
Technology, Gothenburg 412 96, Sweden
- Institute
of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio 702 11, Finland
| | - James N. Dodds
- Chemistry
Department, The University of North Carolina
at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Jody C. May
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Center
for Innovative Technology, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - John P. Wikswo
- Vanderbilt
Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department
of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department
of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department
of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee 37240, United States
| | - John A. McLean
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Center
for Innovative Technology, Vanderbilt University, Nashville, Tennessee 37235, United States
- Vanderbilt
Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Ross D. King
- Department
of Computer Science and Engineering, Chalmers
University of Technology, Gothenburg 412 96, Sweden
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, U.K.
- The Alan
Turing Institute, London NW1 2DB, U.K.
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Tanaka K, Bamba T, Kondo A, Hasunuma T. Metabolomics-based development of bioproduction processes toward industrial-scale production. Curr Opin Biotechnol 2024; 85:103057. [PMID: 38154323 DOI: 10.1016/j.copbio.2023.103057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 12/04/2023] [Accepted: 12/04/2023] [Indexed: 12/30/2023]
Abstract
Microbial biomanufacturing offers a promising, environment-friendly platform for next-generation chemical production. However, its limited industrial implementation is attributed to the slow production rates of target compounds and the time-intensive engineering of high-yield strains. This review highlights how metabolomics expedites bioproduction development, as demonstrated through case studies of its integration into microbial strain engineering, culture optimization, and model construction. The Design-Build-Test-Learn (DBTL) cycle serves as a standard workflow for strain engineering. Process development, including the optimization of culture conditions and scale-up, is crucial for industrial production. In silico models facilitate the development of strains and processes. Metabolomics is a powerful driver of the DBTL framework, process development, and model construction.
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Affiliation(s)
- Kenya Tanaka
- Engineering Biology Research Center, Kobe University, 1-1 Rokkodai, Nada, Kobe 657-8501, Japan; Graduate School of Science, Technology and Innovation, Kobe University, 1-1 Rokkodai, Nada, Kobe 657-8501, Japan
| | - Takahiro Bamba
- Engineering Biology Research Center, Kobe University, 1-1 Rokkodai, Nada, Kobe 657-8501, Japan
| | - Akihiko Kondo
- Engineering Biology Research Center, Kobe University, 1-1 Rokkodai, Nada, Kobe 657-8501, Japan; Graduate School of Science, Technology and Innovation, Kobe University, 1-1 Rokkodai, Nada, Kobe 657-8501, Japan; RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Tomohisa Hasunuma
- Engineering Biology Research Center, Kobe University, 1-1 Rokkodai, Nada, Kobe 657-8501, Japan; Graduate School of Science, Technology and Innovation, Kobe University, 1-1 Rokkodai, Nada, Kobe 657-8501, Japan; RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.
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Yang Y, Hsiao YC, Liu CW, Lu K. The Role of the Nuclear Receptor FXR in Arsenic-Induced Glucose Intolerance in Mice. TOXICS 2023; 11:833. [PMID: 37888683 PMCID: PMC10611046 DOI: 10.3390/toxics11100833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/26/2023] [Accepted: 09/28/2023] [Indexed: 10/28/2023]
Abstract
Inorganic arsenic in drinking water is prioritized as a top environmental contaminant by the World Health Organization, with over 230 million people potentially being exposed. Arsenic toxicity has been well documented and is associated with a plethora of human diseases, including diabetes, as established in numerous animal and epidemiological studies. Our previous study revealed that arsenic exposure leads to the inhibition of nuclear receptors, including LXR/RXR. To this end, FXR is a nuclear receptor central to glucose and lipid metabolism. However, limited studies are available for understanding arsenic exposure-FXR interactions. Herein, we report that FXR knockout mice developed more profound glucose intolerance than wild-type mice upon arsenic exposure, supporting the regulatory role of FXR in arsenic-induced glucose intolerance. We further exposed mice to arsenic and tested if GW4064, a FXR agonist, could improve glucose intolerance and dysregulation of hepatic proteins and serum metabolites. Our data showed arsenic-induced glucose intolerance was remarkably diminished by GW4064, accompanied by a significant ratio of alleviation of dysregulation in hepatic proteins (83%) and annotated serum metabolites (58%). In particular, hepatic proteins "rescued" from arsenic toxicity by GW4064 featured members of glucose and lipid utilization. For instance, the expression of PCK1, a candidate gene for diabetes and obesity that facilitates gluconeogenesis, was repressed under arsenic exposure in the liver, but revived with the GW4064 supplement. Together, our comprehensive dataset indicates FXR plays a key role and may serve as a potential therapeutic for arsenic-induced metabolic disorders.
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Affiliation(s)
| | | | | | - Kun Lu
- Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, NC 27599, USA
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