1
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Stincone P, Naimi A, Saviola AJ, Reher R, Petras D. Decoding the molecular interplay in the central dogma: An overview of mass spectrometry-based methods to investigate protein-metabolite interactions. Proteomics 2024; 24:e2200533. [PMID: 37929699 DOI: 10.1002/pmic.202200533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 10/15/2023] [Accepted: 10/23/2023] [Indexed: 11/07/2023]
Abstract
With the emergence of next-generation nucleotide sequencing and mass spectrometry-based proteomics and metabolomics tools, we have comprehensive and scalable methods to analyze the genes, transcripts, proteins, and metabolites of a multitude of biological systems. Despite the fascinating new molecular insights at the genome, transcriptome, proteome and metabolome scale, we are still far from fully understanding cellular organization, cell cycles and biology at the molecular level. Significant advances in sensitivity and depth for both sequencing as well as mass spectrometry-based methods allow the analysis at the single cell and single molecule level. At the same time, new tools are emerging that enable the investigation of molecular interactions throughout the central dogma of molecular biology. In this review, we provide an overview of established and recently developed mass spectrometry-based tools to probe metabolite-protein interactions-from individual interaction pairs to interactions at the proteome-metabolome scale.
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Affiliation(s)
- Paolo Stincone
- University of Tuebingen, CMFI Cluster of Excellence, Interfaculty Institute of Microbiology and Infection Medicine, Tuebingen, Germany
- University of Tuebingen, Center for Plant Molecular Biology, Tuebingen, Germany
| | - Amira Naimi
- University of Marburg, Institute of Pharmaceutical Biology and Biotechnology, Marburg, Germany
| | | | - Raphael Reher
- University of Marburg, Institute of Pharmaceutical Biology and Biotechnology, Marburg, Germany
| | - Daniel Petras
- University of Tuebingen, CMFI Cluster of Excellence, Interfaculty Institute of Microbiology and Infection Medicine, Tuebingen, Germany
- University of California Riverside, Department of Biochemistry, Riverside, USA
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2
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Moyung K, Li Y, Hartemink AJ, MacAlpine DM. Genome-wide nucleosome and transcription factor responses to genetic perturbations reveal chromatin-mediated mechanisms of transcriptional regulation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.24.595391. [PMID: 38826400 PMCID: PMC11142231 DOI: 10.1101/2024.05.24.595391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Epigenetic mechanisms contribute to gene regulation by altering chromatin accessibility through changes in transcription factor (TF) and nucleosome occupancy throughout the genome. Despite numerous studies focusing on changes in gene expression, the intricate chromatin-mediated regulatory code remains largely unexplored on a comprehensive scale. We address this by employing a factor-agnostic, reverse-genetics approach that uses MNase-seq to capture genome-wide TF and nucleosome occupancies in response to the individual deletion of 201 transcriptional regulators in Saccharomyces cerevisiae, thereby assaying nearly one million mutant-gene interactions. We develop a principled approach to identify and quantify chromatin changes genome-wide, observing differences in TF and nucleosome occupancy that recapitulate well-established pathways identified by gene expression data. We also discover distinct chromatin signatures associated with the up- and downregulation of genes, and use these signatures to reveal regulatory mechanisms previously unexplored in expression-based studies. Finally, we demonstrate that chromatin features are predictive of transcriptional activity and leverage these features to reconstruct chromatin-based transcriptional regulatory networks. Overall, these results illustrate the power of an approach combining genetic perturbation with high-resolution epigenomic profiling; the latter enables a close examination of the interplay between TFs and nucleosomes genome-wide, providing a deeper, more mechanistic understanding of the complex relationship between chromatin organization and transcription.
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Affiliation(s)
- Kevin Moyung
- Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708
- Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27710
| | - Yulong Li
- Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27710
- Department of Computer Science, Duke University, Durham, NC 27708
| | - Alexander J. Hartemink
- Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708
- Department of Computer Science, Duke University, Durham, NC 27708
| | - David M. MacAlpine
- Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708
- Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27710
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3
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Goshisht MK. Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges. ACS OMEGA 2024; 9:9921-9945. [PMID: 38463314 PMCID: PMC10918679 DOI: 10.1021/acsomega.3c05913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 01/19/2024] [Accepted: 01/30/2024] [Indexed: 03/12/2024]
Abstract
Machine learning (ML), particularly deep learning (DL), has made rapid and substantial progress in synthetic biology in recent years. Biotechnological applications of biosystems, including pathways, enzymes, and whole cells, are being probed frequently with time. The intricacy and interconnectedness of biosystems make it challenging to design them with the desired properties. ML and DL have a synergy with synthetic biology. Synthetic biology can be employed to produce large data sets for training models (for instance, by utilizing DNA synthesis), and ML/DL models can be employed to inform design (for example, by generating new parts or advising unrivaled experiments to perform). This potential has recently been brought to light by research at the intersection of engineering biology and ML/DL through achievements like the design of novel biological components, best experimental design, automated analysis of microscopy data, protein structure prediction, and biomolecular implementations of ANNs (Artificial Neural Networks). I have divided this review into three sections. In the first section, I describe predictive potential and basics of ML along with myriad applications in synthetic biology, especially in engineering cells, activity of proteins, and metabolic pathways. In the second section, I describe fundamental DL architectures and their applications in synthetic biology. Finally, I describe different challenges causing hurdles in the progress of ML/DL and synthetic biology along with their solutions.
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Affiliation(s)
- Manoj Kumar Goshisht
- Department of Chemistry, Natural and
Applied Sciences, University of Wisconsin—Green
Bay, Green
Bay, Wisconsin 54311-7001, United States
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4
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Li J, Xiong Y, Feng S, Pan C, Guo X. CloudProteoAnalyzer: scalable processing of big data from proteomics using cloud computing. BIOINFORMATICS ADVANCES 2024; 4:vbae024. [PMID: 38495055 PMCID: PMC10942798 DOI: 10.1093/bioadv/vbae024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 02/05/2024] [Accepted: 02/21/2024] [Indexed: 03/19/2024]
Abstract
Summary Shotgun proteomics is widely used in many system biology studies to determine the global protein expression profiles of tissues, cultures, and microbiomes. Many non-distributed computer algorithms have been developed for users to process proteomics data on their local computers. However, the amount of data acquired in a typical proteomics study has grown rapidly in recent years, owing to the increasing throughput of mass spectrometry and the expanding scale of study designs. This presents a big data challenge for researchers to process proteomics data in a timely manner. To overcome this challenge, we developed a cloud-based parallel computing application to offer end-to-end proteomics data analysis software as a service (SaaS). A web interface was provided to users to upload mass spectrometry-based proteomics data, configure parameters, submit jobs, and monitor job status. The data processing was distributed across multiple nodes in a supercomputer to achieve scalability for large datasets. Our study demonstrated SaaS for proteomics as a viable solution for the community to scale up the data processing using cloud computing. Availability and implementation This application is available online at https://sipros.oscer.ou.edu/ or https://sipros.unt.edu for free use. The source code is available at https://github.com/Biocomputing-Research-Group/CloudProteoAnalyzer under the GPL version 3.0 license.
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Affiliation(s)
- Jiancheng Li
- Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, United States
| | - Yi Xiong
- School of Biological Sciences, University of Oklahoma, Norman, OK 73019, United States
| | - Shichao Feng
- Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, United States
| | - Chongle Pan
- School of Biological Sciences, University of Oklahoma, Norman, OK 73019, United States
- School of Computer Science, University of Oklahoma, Norman, OK 73019, United States
| | - Xuan Guo
- Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, United States
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5
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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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6
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Zhao M, Ma J, Zhang L, Qi H. Engineering strategies for enhanced heterologous protein production by Saccharomyces cerevisiae. Microb Cell Fact 2024; 23:32. [PMID: 38247006 PMCID: PMC10801990 DOI: 10.1186/s12934-024-02299-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 01/05/2024] [Indexed: 01/23/2024] Open
Abstract
Microbial proteins are promising substitutes for animal- and plant-based proteins. S. cerevisiae, a generally recognized as safe (GRAS) microorganism, has been frequently employed to generate heterologous proteins. However, constructing a universal yeast chassis for efficient protein production is still a challenge due to the varying properties of different proteins. With progress in synthetic biology, a multitude of molecular biology tools and metabolic engineering strategies have been employed to alleviate these issues. This review first analyses the advantages of protein production by S. cerevisiae. The most recent advances in improving heterologous protein yield are summarized and discussed in terms of protein hyperexpression systems, protein secretion engineering, glycosylation pathway engineering and systems metabolic engineering. Furthermore, the prospects for efficient and sustainable heterologous protein production by S. cerevisiae are also provided.
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Affiliation(s)
- Meirong Zhao
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (MOE), Tianjin University, Tianjin, 300350, China
| | - Jianfan Ma
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (MOE), Tianjin University, Tianjin, 300350, China
| | - Lei Zhang
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (MOE), Tianjin University, Tianjin, 300350, China
| | - Haishan Qi
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (MOE), Tianjin University, Tianjin, 300350, China.
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7
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Shimazaki S, Yamada R, Yamamoto Y, Matsumoto T, Ogino H. Building a machine-learning model to predict optimal mevalonate pathway gene expression levels for efficient production of a carotenoid in yeast. Biotechnol J 2024; 19:e2300285. [PMID: 37953664 DOI: 10.1002/biot.202300285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 11/06/2023] [Accepted: 11/08/2023] [Indexed: 11/14/2023]
Abstract
Simultaneous modification of the expression levels of many metabolic enzyme genes results in diverse expression ratios of these genes; however, the relationship between gene expression levels and chemical productivity remains unclear. However, clarification of this relationship is expected to improve the productivity of useful chemicals. Supervised machine learning is considered to be an effective means to clarify this relationship. In this study, to improve the productivity of carotenoids in yeast Saccharomyces cerevisiae, we aimed to build a machine-learning model that can predict the optimal gene expression level for carotenoid production. First, we obtained data on the expression levels of mevalonate pathway enzyme genes and carotenoid production. Then, based on these data, we built a machine-learning model to predict carotenoid productivity based on gene expression levels. The prediction accuracy of 0.6292 (coefficient of determination) was achieved using the test data. The maximum predicted carotenoid productivity was 4.3 times higher in the engineered strain than in the parental strain, suggesting that the expression levels of the mevalonate pathway enzyme genes tHMG1 and ERG8 have a particularly large impact on carotenoid productivity. This study could be one of the important achievements in addressing the uncertainty of genotype-phenotype correlations, which is one of the challenges facing metabolic engineering strategies.
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Affiliation(s)
- Shun Shimazaki
- Department of Chemical Engineering, Osaka Metropolitan University, Sakai, Osaka, Japan
| | - Ryosuke Yamada
- Department of Chemical Engineering, Osaka Metropolitan University, Sakai, Osaka, Japan
| | - Yoshiki Yamamoto
- Department of Chemical Engineering, Osaka Metropolitan University, Sakai, Osaka, Japan
| | - Takuya Matsumoto
- Department of Chemical Engineering, Osaka Metropolitan University, Sakai, Osaka, Japan
| | - Hiroyasu Ogino
- Department of Chemical Engineering, Osaka Metropolitan University, Sakai, Osaka, Japan
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8
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van Lent P, Schmitz J, Abeel T. Simulated Design-Build-Test-Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering. ACS Synth Biol 2023; 12:2588-2599. [PMID: 37616156 PMCID: PMC10510747 DOI: 10.1021/acssynbio.3c00186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Indexed: 08/25/2023]
Abstract
Combinatorial pathway optimization is an important tool in metabolic flux optimization. Simultaneous optimization of a large number of pathway genes often leads to combinatorial explosions. Strain optimization is therefore often performed using iterative design-build-test-learn (DBTL) cycles. The aim of these cycles is to develop a product strain iteratively, every time incorporating learning from the previous cycle. Machine learning methods provide a potentially powerful tool to learn from data and propose new designs for the next DBTL cycle. However, due to the lack of a framework for consistently testing the performance of machine learning methods over multiple DBTL cycles, evaluating the effectiveness of these methods remains a challenge. In this work, we propose a mechanistic kinetic model-based framework to test and optimize machine learning for iterative combinatorial pathway optimization. Using this framework, we show that gradient boosting and random forest models outperform the other tested methods in the low-data regime. We demonstrate that these methods are robust for training set biases and experimental noise. Finally, we introduce an algorithm for recommending new designs using machine learning model predictions. We show that when the number of strains to be built is limited, starting with a large initial DBTL cycle is favorable over building the same number of strains for every cycle.
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Affiliation(s)
- Paul van Lent
- Delft
Bioinformatics Lab, Delft University of
Technology Van Mourik, Delft 2628 XE, The Netherlands
| | - Joep Schmitz
- Department
of Science and Research, Joep Schmitz -
dsm-firmenich, Science & Research, P.O. Box 1, 2600
MA Delft, The Netherlands
| | - Thomas Abeel
- Delft
Bioinformatics Lab, Delft University of
Technology Van Mourik, Delft 2628 XE, The Netherlands
- Infectious
Disease and Microbiome Program, Broad Institute
of MIT and Harvard, Cambridge, Massachusetts 02142, United States
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9
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Weith M, Großbach J, Clement‐Ziza M, Gillet L, Rodríguez‐López M, Marguerat S, Workman CT, Picotti P, Bähler J, Aebersold R, Beyer A. Genetic effects on molecular network states explain complex traits. Mol Syst Biol 2023; 19:e11493. [PMID: 37485750 PMCID: PMC10407735 DOI: 10.15252/msb.202211493] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 06/29/2023] [Accepted: 07/04/2023] [Indexed: 07/25/2023] Open
Abstract
The complexity of many cellular and organismal traits results from the integration of genetic and environmental factors via molecular networks. Network structure and effect propagation are best understood at the level of functional modules, but so far, no concept has been established to include the global network state. Here, we show when and how genetic perturbations lead to molecular changes that are confined to small parts of a network versus when they lead to modulation of network states. Integrating multi-omics profiling of genetically heterogeneous budding and fission yeast strains with an array of cellular traits identified a central state transition of the yeast molecular network that is related to PKA and TOR (PT) signaling. Genetic variants affecting this PT state globally shifted the molecular network along a single-dimensional axis, thereby modulating processes including energy and amino acid metabolism, transcription, translation, cell cycle control, and cellular stress response. We propose that genetic effects can propagate through large parts of molecular networks because of the functional requirement to centrally coordinate the activity of fundamental cellular processes.
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Affiliation(s)
- Matthias Weith
- Excellence Cluster on Cellular Stress Responses in Aging Associated DiseasesUniversity of CologneCologneGermany
| | - Jan Großbach
- Excellence Cluster on Cellular Stress Responses in Aging Associated DiseasesUniversity of CologneCologneGermany
| | | | - Ludovic Gillet
- Department of BiologyInstitute of Molecular Systems Biology, ETH ZürichZürichSwitzerland
| | - María Rodríguez‐López
- Institute of Healthy Ageing and Department of Genetics, Evolution & EnvironmentUniversity College LondonLondonUK
| | - Samuel Marguerat
- Institute of Healthy Ageing and Department of Genetics, Evolution & EnvironmentUniversity College LondonLondonUK
| | - Christopher T Workman
- Department of Biotechnology and BiomedicineTechnical University of DenmarkLyngbyDenmark
| | - Paola Picotti
- Department of BiologyInstitute of Molecular Systems Biology, ETH ZürichZürichSwitzerland
| | - Jürg Bähler
- Institute of Healthy Ageing and Department of Genetics, Evolution & EnvironmentUniversity College LondonLondonUK
| | - Ruedi Aebersold
- Department of BiologyInstitute of Molecular Systems Biology, ETH ZürichZürichSwitzerland
| | - Andreas Beyer
- Excellence Cluster on Cellular Stress Responses in Aging Associated DiseasesUniversity of CologneCologneGermany
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10
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Sen P, Orešič M. Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine. Metabolites 2023; 13:855. [PMID: 37512562 PMCID: PMC10383060 DOI: 10.3390/metabo13070855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.
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Affiliation(s)
- Partho Sen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden
| | - Matej Orešič
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden
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11
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Messner CB, Demichev V, Muenzner J, Aulakh SK, Barthel N, Röhl A, Herrera-Domínguez L, Egger AS, Kamrad S, Hou J, Tan G, Lemke O, Calvani E, Szyrwiel L, Mülleder M, Lilley KS, Boone C, Kustatscher G, Ralser M. The proteomic landscape of genome-wide genetic perturbations. Cell 2023; 186:2018-2034.e21. [PMID: 37080200 PMCID: PMC7615649 DOI: 10.1016/j.cell.2023.03.026] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 01/20/2023] [Accepted: 03/21/2023] [Indexed: 04/22/2023]
Abstract
Functional genomic strategies have become fundamental for annotating gene function and regulatory networks. Here, we combined functional genomics with proteomics by quantifying protein abundances in a genome-scale knockout library in Saccharomyces cerevisiae, using data-independent acquisition mass spectrometry. We find that global protein expression is driven by a complex interplay of (1) general biological properties, including translation rate, protein turnover, the formation of protein complexes, growth rate, and genome architecture, followed by (2) functional properties, such as the connectivity of a protein in genetic, metabolic, and physical interaction networks. Moreover, we show that functional proteomics complements current gene annotation strategies through the assessment of proteome profile similarity, protein covariation, and reverse proteome profiling. Thus, our study reveals principles that govern protein expression and provides a genome-spanning resource for functional annotation.
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Affiliation(s)
- Christoph B Messner
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW1 1AT, UK; Precision Proteomics Center, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, 7265 Davos, Switzerland
| | - Vadim Demichev
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW1 1AT, UK; Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany; Department of Biochemistry, Cambridge Centre for Proteomics, University of Cambridge, Cambridge CB2 1QW, UK
| | - Julia Muenzner
- Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany
| | - Simran K Aulakh
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW1 1AT, UK
| | - Natalie Barthel
- Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany
| | - Annika Röhl
- Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany
| | | | - Anna-Sophia Egger
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW1 1AT, UK
| | - Stephan Kamrad
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW1 1AT, UK
| | - Jing Hou
- The Donnelly Centre, University of Toronto, Toronto, ON M5S3E1, Canada
| | - Guihong Tan
- The Donnelly Centre, University of Toronto, Toronto, ON M5S3E1, Canada
| | - Oliver Lemke
- Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany
| | - Enrica Calvani
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW1 1AT, UK
| | - Lukasz Szyrwiel
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW1 1AT, UK; Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany
| | - Michael Mülleder
- Charité Universitätsmedizin, Core Facility - High Throughput Mass Spectrometry, 10117 Berlin, Germany
| | - Kathryn S Lilley
- Department of Biochemistry, Cambridge Centre for Proteomics, University of Cambridge, Cambridge CB2 1QW, UK
| | - Charles Boone
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S3E1, Canada; The Donnelly Centre, University of Toronto, Toronto, ON M5S3E1, Canada; RIKEN Center for Sustainable Resource Science, Wako, 351-0198 Saitama, Japan
| | - Georg Kustatscher
- Wellcome Centre for Cell Biology, University of Edinburgh, Max Born Crescent, Edinburgh EH9 3BF, Scotland, UK.
| | - Markus Ralser
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW1 1AT, UK; Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany; The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK; Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany.
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12
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Messner CB, Demichev V, Wang Z, Hartl J, Kustatscher G, Mülleder M, Ralser M. Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology. Proteomics 2023; 23:e2200013. [PMID: 36349817 DOI: 10.1002/pmic.202200013] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 10/13/2022] [Accepted: 10/13/2022] [Indexed: 11/11/2022]
Abstract
There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.
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Affiliation(s)
- Christoph B Messner
- Precision Proteomics Center, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
| | - Vadim Demichev
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ziyue Wang
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Johannes Hartl
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Georg Kustatscher
- Wellcome Centre for Cell Biology, University of Edinburgh, Max Born Crescent, Edinburgh, Scotland, UK
| | - Michael Mülleder
- Core Facility High Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Markus Ralser
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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13
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Aharoni A, Goodacre R, Fernie AR. Plant and microbial sciences as key drivers in the development of metabolomics research. Proc Natl Acad Sci U S A 2023; 120:e2217383120. [PMID: 36930598 PMCID: PMC10041103 DOI: 10.1073/pnas.2217383120] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023] Open
Abstract
This year marks the 25th anniversary of the coinage of the term metabolome [S. G. Oliver et al., Trends Biotech. 16, 373-378 (1998)]. As the field rapidly advances, it is important to take stock of the progress which has been made to best inform the disciplines future. While a medical-centric perspective on metabolomics has recently been published [M. Giera et al., Cell Metab. 34, 21-34 (2022)], this largely ignores the pioneering contributions made by the plant and microbial science communities. In this perspective, we provide a contemporary overview of all fields in which metabolomics is employed with particular emphasis on both methodological and application breakthroughs made in plant and microbial sciences that have shaped this evolving research discipline from the very early days of its establishment. This will not cover all types of metabolomics assays currently employed but will focus mainly on those utilizing mass spectrometry-based measurements since they are currently by far the most prominent. Having established the historical context of metabolomics, we will address the key challenges currently facing metabolomics and offer potential approaches by which these can be faced. Most salient among these is the fact that the vast majority of mass features are as yet not annotated with high confidence; what we may refer to as definitive identification. We discuss the potential of both standard compound libraries and artificial intelligence technologies to address this challenge and the use of natural variance-based approaches such as genome-wide association studies in attempt to assign specific functions to the myriad of structurally similar and complex specialized metabolites. We conclude by stating our contention that as these challenges are epic and that they will need far greater cooperative efforts from biologists, chemists, and computer scientists with an interest in all kingdoms of life than have been made to date. Ultimately, a better linkage of metabolome and genome data will likely also be needed particularly considering the Earth BioGenome Project.
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Affiliation(s)
- Asaph Aharoni
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot76100, Israel
| | - Royston Goodacre
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, LiverpoolL69 7BE, UK
| | - Alisdair R. Fernie
- Max-Planck-Institute for Molecular Plant Physiology, Potsdam14476, Germany
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14
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Kurbatov I, Dolgalev G, Arzumanian V, Kiseleva O, Poverennaya E. The Knowns and Unknowns in Protein-Metabolite Interactions. Int J Mol Sci 2023; 24:ijms24044155. [PMID: 36835565 PMCID: PMC9964805 DOI: 10.3390/ijms24044155] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 02/11/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
Increasing attention has been focused on the study of protein-metabolite interactions (PMI), which play a key role in regulating protein functions and directing an orchestra of cellular processes. The investigation of PMIs is complicated by the fact that many such interactions are extremely short-lived, which requires very high resolution in order to detect them. As in the case of protein-protein interactions, protein-metabolite interactions are still not clearly defined. Existing assays for detecting protein-metabolite interactions have an additional limitation in the form of a limited capacity to identify interacting metabolites. Thus, although recent advances in mass spectrometry allow the routine identification and quantification of thousands of proteins and metabolites today, they still need to be improved to provide a complete inventory of biological molecules, as well as all interactions between them. Multiomic studies aimed at deciphering the implementation of genetic information often end with the analysis of changes in metabolic pathways, as they constitute one of the most informative phenotypic layers. In this approach, the quantity and quality of knowledge about PMIs become vital to establishing the full scope of crosstalk between the proteome and the metabolome in a biological object of interest. In this review, we analyze the current state of investigation into the detection and annotation of protein-metabolite interactions, describe the recent progress in developing associated research methods, and attempt to deconstruct the very term "interaction" to advance the field of interactomics further.
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15
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Ting TY, Li Y, Bunawan H, Ramzi AB, Goh HH. Current advancements in systems and synthetic biology studies of Saccharomyces cerevisiae. J Biosci Bioeng 2023; 135:259-265. [PMID: 36803862 DOI: 10.1016/j.jbiosc.2023.01.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 01/03/2023] [Accepted: 01/26/2023] [Indexed: 02/18/2023]
Abstract
Saccharomyces cerevisiae has a long-standing history of biotechnological applications even before the dawn of modern biotechnology. The field is undergoing accelerated advancement with the recent systems and synthetic biology approaches. In this review, we highlight the recent findings in the field with a focus on omics studies of S. cerevisiae to investigate its stress tolerance in different industries. The latest advancements in S. cerevisiae systems and synthetic biology approaches for the development of genome-scale metabolic models (GEMs) and molecular tools such as multiplex Cas9, Cas12a, Cpf1, and Csy4 genome editing tools, modular expression cassette with optimal transcription factors, promoters, and terminator libraries as well as metabolic engineering. Omics data analysis is key to the identification of exploitable native genes/proteins/pathways in S. cerevisiae with the optimization of heterologous pathway implementation and fermentation conditions. Through systems and synthetic biology, various heterologous compound productions that require non-native biosynthetic pathways in a cell factory have been established via different strategies of metabolic engineering integrated with machine learning.
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Affiliation(s)
- Tiew-Yik Ting
- Institute of Systems Biology, University Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
| | - YaDong Li
- Institute of Systems Biology, University Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
| | - Hamidun Bunawan
- Institute of Systems Biology, University Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
| | - Ahmad Bazli Ramzi
- Institute of Systems Biology, University Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
| | - Hoe-Han Goh
- Institute of Systems Biology, University Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia.
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16
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Gabrielli N, Maga-Nteve C, Kafkia E, Rettel M, Loeffler J, Kamrad S, Typas A, Patil KR. Unravelling metabolic cross-feeding in a yeast-bacteria community using 13 C-based proteomics. Mol Syst Biol 2023; 19:e11501. [PMID: 36779294 PMCID: PMC10090948 DOI: 10.15252/msb.202211501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 01/24/2023] [Accepted: 01/25/2023] [Indexed: 02/14/2023] Open
Abstract
Cross-feeding is fundamental to the diversity and function of microbial communities. However, identification of cross-fed metabolites is often challenging due to the universality of metabolic and biosynthetic intermediates. Here, we use 13 C isotope tracing in peptides to elucidate cross-fed metabolites in co-cultures of Saccharomyces cerevisiae and Lactococcus lactis. The community was grown on lactose as the main carbon source with either glucose or galactose fraction of the molecule labelled with 13 C. Data analysis allowing for the possible mass-shifts yielded hundreds of peptides for which we could assign both species identity and labelling degree. The labelling pattern showed that the yeast utilized galactose and, to a lesser extent, lactic acid shared by L. lactis as carbon sources. While the yeast provided essential amino acids to the bacterium as expected, the data also uncovered a complex pattern of amino acid exchange. The identity of the cross-fed metabolites was further supported by metabolite labelling in the co-culture supernatant, and by diminished fitness of a galactose-negative yeast mutant in the community. Together, our results demonstrate the utility of 13 C-based proteomics for uncovering microbial interactions.
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Affiliation(s)
| | | | - Eleni Kafkia
- European Molecular Biology Laboratory, Heidelberg, Germany.,Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
| | - Mandy Rettel
- European Molecular Biology Laboratory, Heidelberg, Germany
| | - Jakob Loeffler
- European Molecular Biology Laboratory, Heidelberg, Germany
| | - Stephan Kamrad
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
| | | | - Kiran Raosaheb Patil
- European Molecular Biology Laboratory, Heidelberg, Germany.,Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
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17
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Brown AJP. Fungal resilience and host-pathogen interactions: Future perspectives and opportunities. Parasite Immunol 2023; 45:e12946. [PMID: 35962618 PMCID: PMC10078341 DOI: 10.1111/pim.12946] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 08/08/2022] [Accepted: 08/10/2022] [Indexed: 01/31/2023]
Abstract
We are constantly exposed to the threat of fungal infection. The outcome-clearance, commensalism or infection-depends largely on the ability of our innate immune defences to clear infecting fungal cells versus the success of the fungus in mounting compensatory adaptive responses. As each seeks to gain advantage during these skirmishes, the interactions between host and fungal pathogen are complex and dynamic. Nevertheless, simply compromising the physiological robustness of fungal pathogens reduces their ability to evade antifungal immunity, their virulence, and their tolerance against antifungal therapy. In this article I argue that this physiological robustness is based on a 'Resilience Network' which mechanistically links and controls fungal growth, metabolism, stress resistance and drug tolerance. The elasticity of this network probably underlies the phenotypic variability of fungal isolates and the heterogeneity of individual cells within clonal populations. Consequently, I suggest that the definition of the fungal Resilience Network represents an important goal for the future which offers the clear potential to reveal drug targets that compromise drug tolerance and synergise with current antifungal therapies.
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Affiliation(s)
- Alistair J P Brown
- Medical Research Council Centre for Medical Mycology at the University of Exeter, Exeter, UK
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18
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Hu R, Li Y, Yang Y, Liu M. Mass spectrometry-based strategies for single-cell metabolomics. MASS SPECTROMETRY REVIEWS 2023; 42:67-94. [PMID: 34028064 DOI: 10.1002/mas.21704] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 05/05/2021] [Accepted: 05/11/2021] [Indexed: 06/12/2023]
Abstract
Single cell analysis has drawn increasing interest from the research community due to its capability to interrogate cellular heterogeneity, allowing refined tissue classification and facilitating novel biomarker discovery. With the advancement of relevant instruments and techniques, it is now possible to perform multiple omics including genomics, transcriptomics, metabolomics or even proteomics at single cell level. In comparison with other omics studies, single-cell metabolomics (SCM) represents a significant challenge since it involves many types of dynamically changing compounds with a wide range of concentrations. In addition, metabolites cannot be amplified. Although difficult, considerable progress has been made over the past decade in mass spectrometry (MS)-based SCM in terms of processing technologies and biochemical applications. In this review, we will summarize recent progress in the development of promising MS platforms, sample preparation methods and SCM analysis of various cell types (including plant cell, cancer cell, neuron, embryo cell, and yeast cell). Current limitations and future research directions in the field of SCM will also be discussed.
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Affiliation(s)
- Rui Hu
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Ying Li
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yunhuang Yang
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Maili Liu
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
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19
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Volk MJ, Tran VG, Tan SI, Mishra S, Fatma Z, Boob A, Li H, Xue P, Martin TA, Zhao H. Metabolic Engineering: Methodologies and Applications. Chem Rev 2022; 123:5521-5570. [PMID: 36584306 DOI: 10.1021/acs.chemrev.2c00403] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Metabolic engineering aims to improve the production of economically valuable molecules through the genetic manipulation of microbial metabolism. While the discipline is a little over 30 years old, advancements in metabolic engineering have given way to industrial-level molecule production benefitting multiple industries such as chemical, agriculture, food, pharmaceutical, and energy industries. This review describes the design, build, test, and learn steps necessary for leading a successful metabolic engineering campaign. Moreover, we highlight major applications of metabolic engineering, including synthesizing chemicals and fuels, broadening substrate utilization, and improving host robustness with a focus on specific case studies. Finally, we conclude with a discussion on perspectives and future challenges related to metabolic engineering.
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Affiliation(s)
- Michael J Volk
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Vinh G Tran
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Shih-I Tan
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemical Engineering, National Cheng Kung University, Tainan 70101, Taiwan
| | - Shekhar Mishra
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Zia Fatma
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Aashutosh Boob
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Hongxiang Li
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Pu Xue
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Teresa A Martin
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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20
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Chantada-Vázquez MDP, Bravo SB, Barbosa-Gouveia S, Alvarez JV, Couce ML. Proteomics in Inherited Metabolic Disorders. Int J Mol Sci 2022; 23:ijms232314744. [PMID: 36499071 PMCID: PMC9740208 DOI: 10.3390/ijms232314744] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 11/17/2022] [Accepted: 11/22/2022] [Indexed: 11/29/2022] Open
Abstract
Inherited metabolic disorders (IMD) are rare medical conditions caused by genetic defects that interfere with the body's metabolism. The clinical phenotype is highly variable and can present at any age, although it more often manifests in childhood. The number of treatable IMDs has increased in recent years, making early diagnosis and a better understanding of the natural history of the disease more important than ever. In this review, we discuss the main challenges faced in applying proteomics to the study of IMDs, and the key advances achieved in this field using tandem mass spectrometry (MS/MS). This technology enables the analysis of large numbers of proteins in different body fluids (serum, plasma, urine, saliva, tears) with a single analysis of each sample, and can even be applied to dried samples. MS/MS has thus emerged as the tool of choice for proteome characterization and has provided new insights into many diseases and biological systems. In the last 10 years, sequential window acquisition of all theoretical fragmentation spectra mass spectrometry (SWATH-MS) has emerged as an accurate, high-resolution technique for the identification and quantification of proteins differentially expressed between healthy controls and IMD patients. Proteomics is a particularly promising approach to help obtain more information on rare genetic diseases, including identification of biomarkers to aid early diagnosis and better understanding of the underlying pathophysiology to guide the development of new therapies. Here, we summarize new and emerging proteomic technologies and discuss current uses and limitations of this approach to identify and quantify proteins. Moreover, we describe the use of proteomics to identify the mechanisms regulating complex IMD phenotypes; an area of research essential to better understand these rare disorders and many other human diseases.
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Affiliation(s)
- Maria del Pilar Chantada-Vázquez
- Proteomic Platform, Health Research Institute of Santiago de Compostela (IDIS), Hospital Clínico Universitario de Santiago de Compostela, 15706 Santiago de Compostela, Spain
| | - Susana B. Bravo
- Proteomic Platform, Health Research Institute of Santiago de Compostela (IDIS), Hospital Clínico Universitario de Santiago de Compostela, 15706 Santiago de Compostela, Spain
| | - Sofía Barbosa-Gouveia
- Department of Forensic Sciences, Pathology, Gynecology and Obstetrics, Pediatrics, Neonatology Service, Department of Pediatrics, Hospital Clínico Universitario de Santiago de Compostela, Health Research Institute of Santiago de Compostela (IDIS), CIBERER, MetabERN, 15706 Santiago de Compostela, Spain
| | - José V. Alvarez
- Department of Forensic Sciences, Pathology, Gynecology and Obstetrics, Pediatrics, Neonatology Service, Department of Pediatrics, Hospital Clínico Universitario de Santiago de Compostela, Health Research Institute of Santiago de Compostela (IDIS), CIBERER, MetabERN, 15706 Santiago de Compostela, Spain
| | - María L. Couce
- Department of Forensic Sciences, Pathology, Gynecology and Obstetrics, Pediatrics, Neonatology Service, Department of Pediatrics, Hospital Clínico Universitario de Santiago de Compostela, Health Research Institute of Santiago de Compostela (IDIS), CIBERER, MetabERN, 15706 Santiago de Compostela, Spain
- Correspondence: ; Tel.: +349-81-951-100
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21
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Dickinson Q, Aufschnaiter A, Ott M, Meyer JG. Multi-omic integration by machine learning (MIMaL). Bioinformatics 2022; 38:4908-4918. [PMID: 36106996 PMCID: PMC9801967 DOI: 10.1093/bioinformatics/btac631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 08/17/2022] [Accepted: 09/14/2022] [Indexed: 01/05/2023] Open
Abstract
MOTIVATION Cells respond to environments by regulating gene expression to exploit resources optimally. Recent advances in technologies allow for measuring the abundances of RNA, proteins, lipids and metabolites. These highly complex datasets reflect the states of the different layers in a biological system. Multi-omics is the integration of these disparate methods and data to gain a clearer picture of the biological state. Multi-omic studies of the proteome and metabolome are becoming more common as mass spectrometry technology continues to be democratized. However, knowledge extraction through the integration of these data remains challenging. RESULTS Connections between molecules in different omic layers were discovered through a combination of machine learning and model interpretation. Discovered connections reflected protein control (ProC) over metabolites. Proteins discovered to control citrate were mapped onto known genetic and metabolic networks, revealing that these protein regulators are novel. Further, clustering the magnitudes of ProC over all metabolites enabled the prediction of five gene functions, each of which was validated experimentally. Two uncharacterized genes, YJR120W and YDL157C, were accurately predicted to modulate mitochondrial translation. Functions for three incompletely characterized genes were also predicted and validated, including SDH9, ISC1 and FMP52. A website enables results exploration and also MIMaL analysis of user-supplied multi-omic data. AVAILABILITY AND IMPLEMENTATION The website for MIMaL is at https://mimal.app. Code for the website is at https://github.com/qdickinson/mimal-website. Code to implement MIMaL is at https://github.com/jessegmeyerlab/MIMaL. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Quinn Dickinson
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Andreas Aufschnaiter
- Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | - Martin Ott
- Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
| | - Jesse G Meyer
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
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22
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Metabolomics and modelling approaches for systems metabolic engineering. Metab Eng Commun 2022; 15:e00209. [PMID: 36281261 PMCID: PMC9587336 DOI: 10.1016/j.mec.2022.e00209] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 10/13/2022] [Accepted: 10/14/2022] [Indexed: 11/21/2022] Open
Abstract
Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.
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23
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Karlstaedt A, Taegtmeyer H. Cardio-Onco-Metabolism - Metabolic vulnerabilities in cancer and the heart. J Mol Cell Cardiol 2022; 171:71-80. [PMID: 35777454 PMCID: PMC10193535 DOI: 10.1016/j.yjmcc.2022.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 02/05/2022] [Accepted: 06/21/2022] [Indexed: 10/17/2022]
Abstract
Cancer and cardiovascular diseases (CVDs) are the leading cause of death worldwide. Metabolic remodeling is a hallmark of both cancer and the failing heart. Tumors reprogram metabolism to optimize nutrient utilization and meet increased demands for energy provision, biosynthetic pathways, and proliferation. Shared risk factors for cancer and CVDs suggest intersecting mechanisms for disease pathogenesis and progression. In this review, we aim to highlight the role of metabolic remodeling in cancer and its potential to impair cardiac function. Understanding these mechanisms will help us develop biomarkers, better therapies, and identify patients at risk of developing heart disease after surviving cancer.
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Affiliation(s)
- Anja Karlstaedt
- Smidt Heart Institute, Department of Cardiology, Cedars Sinai Medical Center, Los Angeles, California, USA.
| | - Heinrich Taegtmeyer
- Department of Internal Medicine, Division of Cardiology, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, Texas, USA
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24
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Wang Z, Tober-Lau P, Farztdinov V, Lemke O, Schwecke T, Steinbrecher S, Muenzner J, Kriedemann H, Sander LE, Hartl J, Mülleder M, Ralser M, Kurth F. The human host response to monkeypox infection: a proteomic case series study. EMBO Mol Med 2022; 14:e16643. [PMID: 36169042 PMCID: PMC9641420 DOI: 10.15252/emmm.202216643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 09/09/2022] [Accepted: 09/09/2022] [Indexed: 11/23/2022] Open
Abstract
The rapid rise of monkeypox (MPX) cases outside previously endemic areas prompts for a better understanding of the disease. We studied the plasma proteome of a group of MPX patients with a similar infection history and clinical manifestation typical for the current outbreak. We report that MPX in this case series is associated with a strong plasma proteomic response among nutritional and acute phase response proteins. Moreover, we report a correlation between plasma proteins and disease severity. Contrasting the MPX host response with that of COVID‐19, we find a range of similarities, but also important differences. For instance, CFHR1 is induced in COVID‐19, but suppressed in MPX, reflecting the different roles of the complement system in the two infectious diseases. Of note, the spatial overlap in response proteins suggested that a COVID‐19 biomarker panel assay could be repurposed for MPX. Applying a targeted protein panel assay provided encouraging results and distinguished MPX cases from healthy controls. Hence, our results provide a first proteomic characterization of the MPX human host response and encourage further research on protein‐panel assays in emerging infectious diseases.
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Affiliation(s)
- Ziyue Wang
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Pinkus Tober-Lau
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Vadim Farztdinov
- Core Facility High Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Oliver Lemke
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Torsten Schwecke
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Sarah Steinbrecher
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Julia Muenzner
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Helene Kriedemann
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Leif Erik Sander
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute of Health, Berlin, Germany
| | - Johannes Hartl
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Michael Mülleder
- Core Facility High Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Markus Ralser
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute of Health, Berlin, Germany.,The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Florian Kurth
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
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25
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26
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Lo-Thong-Viramoutou O, Charton P, Cadet XF, Grondin-Perez B, Saavedra E, Damour C, Cadet F. Non-linearity of Metabolic Pathways Critically Influences the Choice of Machine Learning Model. Front Artif Intell 2022; 5:744755. [PMID: 35757298 PMCID: PMC9226554 DOI: 10.3389/frai.2022.744755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 04/29/2022] [Indexed: 11/13/2022] Open
Abstract
The use of machine learning (ML) in life sciences has gained wide interest over the past years, as it speeds up the development of high performing models. Important modeling tools in biology have proven their worth for pathway design, such as mechanistic models and metabolic networks, as they allow better understanding of mechanisms involved in the functioning of organisms. However, little has been done on the use of ML to model metabolic pathways, and the degree of non-linearity associated with them is not clear. Here, we report the construction of different metabolic pathways with several linear and non-linear ML models. Different types of data are used; they lead to the prediction of important biological data, such as pathway flux and final product concentration. A comparison reveals that the data features impact model performance and highlight the effectiveness of non-linear models (e.g., QRF: RMSE = 0.021 nmol·min-1 and R2 = 1 vs. Bayesian GLM: RMSE = 1.379 nmol·min-1 R2 = 0.823). It turns out that the greater the degree of non-linearity of the pathway, the better suited a non-linear model will be. Therefore, a decision-making support for pathway modeling is established. These findings generally support the hypothesis that non-linear aspects predominate within the metabolic pathways. This must be taken into account when devising possible applications of these pathways for the identification of biomarkers of diseases (e.g., infections, cancer, neurodegenerative diseases) or the optimization of industrial production processes.
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Affiliation(s)
- Ophélie Lo-Thong-Viramoutou
- University of Paris, BIGR—Biologie Intégrée du Globule Rouge, Inserm, UMR_S1134, Paris, France
- Laboratory of Excellence GR-Ex, Paris, France
- Laboratory DSIMB, UMR_S1134, BIGR, Inserm, Faculty of Sciences and Technology, University of La Reunion, Saint-Denis, France
| | - Philippe Charton
- University of Paris, BIGR—Biologie Intégrée du Globule Rouge, Inserm, UMR_S1134, Paris, France
- Laboratory of Excellence GR-Ex, Paris, France
- Laboratory DSIMB, UMR_S1134, BIGR, Inserm, Faculty of Sciences and Technology, University of La Reunion, Saint-Denis, France
| | | | - Brigitte Grondin-Perez
- EnergyLab, EA 4079, Faculty of Sciences and Technology, University of La Reunion, Saint-Denis, France
| | - Emma Saavedra
- Departamento de Bioquímica, Instituto Nacional de Cardiología Ignacio Chávez, Mexico City, Mexico
| | - Cédric Damour
- EnergyLab, EA 4079, Faculty of Sciences and Technology, University of La Reunion, Saint-Denis, France
| | - Frédéric Cadet
- University of Paris, BIGR—Biologie Intégrée du Globule Rouge, Inserm, UMR_S1134, Paris, France
- Laboratory of Excellence GR-Ex, Paris, France
- Laboratory DSIMB, UMR_S1134, BIGR, Inserm, Faculty of Sciences and Technology, University of La Reunion, Saint-Denis, France
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27
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Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines. Int J Mol Sci 2022; 23:ijms23073867. [PMID: 35409231 PMCID: PMC8998886 DOI: 10.3390/ijms23073867] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 03/24/2022] [Accepted: 03/29/2022] [Indexed: 02/01/2023] Open
Abstract
The Metabolome and Transcriptome are mutually communicating within cancer cells, and this interplay is translated into the existence of quantifiable correlation structures between gene expression and metabolite abundance levels. Studying these correlations could provide a novel venue of understanding cancer and the discovery of novel biomarkers and pharmacological strategies, as well as laying the foundation for the prediction of metabolite quantities by leveraging information from the more widespread transcriptomics data. In the current paper, we investigate the correlation between gene expression and metabolite levels in the Cancer Cell Line Encyclopedia dataset, building a direct correlation network between the two molecular ensembles. We show that a metabolite/transcript correlation network can be used to predict metabolite levels in different samples and datasets, such as the NCI-60 cancer cell line dataset, both on a sample-by-sample basis and in differential contrasts. We also show that metabolite levels can be predicted in principle on any sample and dataset for which transcriptomics data are available, such as the Cancer Genome Atlas (TCGA).
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28
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Yu JSL, Correia-Melo C, Zorrilla F, Herrera-Dominguez L, Wu MY, Hartl J, Campbell K, Blasche S, Kreidl M, Egger AS, Messner CB, Demichev V, Freiwald A, Mülleder M, Howell M, Berman J, Patil KR, Alam MT, Ralser M. Microbial communities form rich extracellular metabolomes that foster metabolic interactions and promote drug tolerance. Nat Microbiol 2022; 7:542-555. [PMID: 35314781 PMCID: PMC8975748 DOI: 10.1038/s41564-022-01072-5] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 01/28/2022] [Indexed: 12/30/2022]
Abstract
Microbial communities are composed of cells of varying metabolic capacity, and regularly include auxotrophs that lack essential metabolic pathways. Through analysis of auxotrophs for amino acid biosynthesis pathways in microbiome data derived from >12,000 natural microbial communities obtained as part of the Earth Microbiome Project (EMP), and study of auxotrophic–prototrophic interactions in self-establishing metabolically cooperating yeast communities (SeMeCos), we reveal a metabolically imprinted mechanism that links the presence of auxotrophs to an increase in metabolic interactions and gains in antimicrobial drug tolerance. As a consequence of the metabolic adaptations necessary to uptake specific metabolites, auxotrophs obtain altered metabolic flux distributions, export more metabolites and, in this way, enrich community environments in metabolites. Moreover, increased efflux activities reduce intracellular drug concentrations, allowing cells to grow in the presence of drug levels above minimal inhibitory concentrations. For example, we show that the antifungal action of azoles is greatly diminished in yeast cells that uptake metabolites from a metabolically enriched environment. Our results hence provide a mechanism that explains why cells are more robust to drug exposure when they interact metabolically. Using microbiome data analysis and a self-establishing metabolically cooperating yeast community model, the authors show that the presence of auxotrophs in a microbial community increases metabolic interactions between cells and fosters antimicrobial drug tolerance.
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Affiliation(s)
- Jason S L Yu
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Clara Correia-Melo
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK.,Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Francisco Zorrilla
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK.,Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Lucia Herrera-Dominguez
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK.,Department of Biochemistry, Charité University Medicine, Berlin, Germany
| | - Mary Y Wu
- High-Throughput Screening, The Francis Crick Institute, London, UK
| | - Johannes Hartl
- Department of Biochemistry, Charité University Medicine, Berlin, Germany
| | - Kate Campbell
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Sonja Blasche
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK.,Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Marco Kreidl
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Anna-Sophia Egger
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Christoph B Messner
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK.,Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Vadim Demichev
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK.,Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Anja Freiwald
- Department of Biochemistry, Charité University Medicine, Berlin, Germany.,Core Facility - High Throughput Mass Spectrometry, Charité University Medicine, Berlin, Germany
| | - Michael Mülleder
- Core Facility - High Throughput Mass Spectrometry, Charité University Medicine, Berlin, Germany
| | - Michael Howell
- High-Throughput Screening, The Francis Crick Institute, London, UK
| | - Judith Berman
- Shmunis School of Biomedical and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv, Israel
| | - Kiran R Patil
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK.,Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Mohammad Tauqeer Alam
- Department of Biology, College of Science, United Arab Emirates University, Al-Ain, UAE. .,Warwick Medical School, University of Warwick, Coventry, UK.
| | - Markus Ralser
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK. .,Department of Biochemistry, Charité University Medicine, Berlin, Germany. .,Core Facility - High Throughput Mass Spectrometry, Charité University Medicine, Berlin, Germany.
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29
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Li X, Yilmaz LS, Walhout AJ. Compartmentalization of metabolism between cell types in multicellular organisms: a computational perspective. CURRENT OPINION IN SYSTEMS BIOLOGY 2022; 29:100407. [PMID: 35224313 PMCID: PMC8865431 DOI: 10.1016/j.coisb.2021.100407] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
In multicellular organisms, metabolism is compartmentalized at many levels, including tissues and organs, different cell types, and subcellular compartments. Compartmentalization creates a coordinated homeostatic system where each compartment contributes to the production of energy and biomolecules the organism needs to carrying out specific metabolic tasks. Experimentally studying metabolic compartmentalization and metabolic interactions between cells and tissues in multicellular organisms is challenging at a systems level. However, recent progress in computational modeling provides an alternative approach to this problem. Here we discuss how integrating metabolic network modeling with omics data offers an opportunity to reveal metabolic states at the level of organs, tissues and, ultimately, individual cells. We review the current status of genome-scale metabolic network models in multicellular organisms, methods to study metabolic compartmentalization in silico, and insights gained from computational analyses. We also discuss outstanding challenges and provide perspectives for the future directions of the field.
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30
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Wang Z, Mülleder M, Batruch I, Chelur A, Textoris-Taube K, Schwecke T, Hartl J, Causon J, Castro-Perez J, Demichev V, Tate S, Ralser M. High-throughput proteomics of nanogram-scale samples with Zeno SWATH MS. eLife 2022; 11:83947. [PMID: 36449390 PMCID: PMC9711518 DOI: 10.7554/elife.83947] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 11/08/2022] [Indexed: 12/03/2022] Open
Abstract
The possibility to record proteomes in high throughput and at high quality has opened new avenues for biomedical research, drug discovery, systems biology, and clinical translation. However, high-throughput proteomic experiments often require high sample amounts and can be less sensitive compared to conventional proteomic experiments. Here, we introduce and benchmark Zeno SWATH MS, a data-independent acquisition technique that employs a linear ion trap pulsing (Zeno trap pulsing) to increase the sensitivity in high-throughput proteomic experiments. We demonstrate that when combined with fast micro- or analytical flow-rate chromatography, Zeno SWATH MS increases protein identification with low sample amounts. For instance, using 20 min micro-flow-rate chromatography, Zeno SWATH MS identified more than 5000 proteins consistently, and with a coefficient of variation of 6%, from a 62.5 ng load of human cell line tryptic digest. Using 5 min analytical flow-rate chromatography (800 µl/min), Zeno SWATH MS identified 4907 proteins from a triplicate injection of 2 µg of a human cell lysate, or more than 3000 proteins from a 250 ng tryptic digest. Zeno SWATH MS hence facilitates sensitive high-throughput proteomic experiments with low sample amounts, mitigating the current bottlenecks of high-throughput proteomics.
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Affiliation(s)
- Ziyue Wang
- Department of Biochemistry, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu BerlinBerlinGermany
| | - Michael Mülleder
- Core Facility – High-Throughput Mass Spectrometry, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Core Facility – High-Throughput Mass SpectrometryBerlinGermany
| | | | | | - Kathrin Textoris-Taube
- Department of Biochemistry, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu BerlinBerlinGermany,Core Facility – High-Throughput Mass Spectrometry, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Core Facility – High-Throughput Mass SpectrometryBerlinGermany
| | - Torsten Schwecke
- Department of Biochemistry, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu BerlinBerlinGermany
| | - Johannes Hartl
- Department of Biochemistry, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu BerlinBerlinGermany
| | | | | | - Vadim Demichev
- Department of Biochemistry, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu BerlinBerlinGermany
| | | | - Markus Ralser
- Department of Biochemistry, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu BerlinBerlinGermany,The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of OxfordOxfordUnited Kingdom
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31
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Vowinckel J, Hartl J, Marx H, Kerick M, Runggatscher K, Keller MA, Mülleder M, Day J, Weber M, Rinnerthaler M, Yu JSL, Aulakh SK, Lehmann A, Mattanovich D, Timmermann B, Zhang N, Dunn CD, MacRae JI, Breitenbach M, Ralser M. The metabolic growth limitations of petite cells lacking the mitochondrial genome. Nat Metab 2021; 3:1521-1535. [PMID: 34799698 PMCID: PMC7612105 DOI: 10.1038/s42255-021-00477-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 09/10/2021] [Indexed: 12/25/2022]
Abstract
Eukaryotic cells can survive the loss of their mitochondrial genome, but consequently suffer from severe growth defects. 'Petite yeasts', characterized by mitochondrial genome loss, are instrumental for studying mitochondrial function and physiology. However, the molecular cause of their reduced growth rate remains an open question. Here we show that petite cells suffer from an insufficient capacity to synthesize glutamate, glutamine, leucine and arginine, negatively impacting their growth. Using a combination of molecular genetics and omics approaches, we demonstrate the evolution of fast growth overcomes these amino acid deficiencies, by alleviating a perturbation in mitochondrial iron metabolism and by restoring a defect in the mitochondrial tricarboxylic acid cycle, caused by aconitase inhibition. Our results hence explain the slow growth of mitochondrial genome-deficient cells with a partial auxotrophy in four amino acids that results from distorted iron metabolism and an inhibited tricarboxylic acid cycle.
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Affiliation(s)
- Jakob Vowinckel
- Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK
- Biognosys AG, Schlieren, Switzerland
| | - Johannes Hartl
- Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Biochemistry, Berlin, Germany
| | - Hans Marx
- Department of Biotechnology, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
| | - Martin Kerick
- Sequencing Core Facility, Max Planck Institute for Molecular Genetics and Max Planck Unit for the Science of Pathogens, Berlin, Germany
- Institute of Parasitology and Biomedicine 'López-Neyra' (IPBLN, CSIC), Granada, Spain
| | - Kathrin Runggatscher
- Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK
| | - Markus A Keller
- Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK
- Institute of Human Genetics, Medical University of Innsbruck, Innsbruck, Austria
| | - Michael Mülleder
- Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Biochemistry, Berlin, Germany
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Jason Day
- Department of Earth Sciences, University of Cambridge, Cambridge, UK
| | - Manuela Weber
- Department of Biosciences, University of Salzburg, Salzburg, Austria
| | - Mark Rinnerthaler
- Department of Biosciences, University of Salzburg, Salzburg, Austria
| | - Jason S L Yu
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Simran Kaur Aulakh
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Andrea Lehmann
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Biochemistry, Berlin, Germany
| | - Diethard Mattanovich
- Department of Biotechnology, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
| | - Bernd Timmermann
- Sequencing Core Facility, Max Planck Institute for Molecular Genetics and Max Planck Unit for the Science of Pathogens, Berlin, Germany
| | - Nianshu Zhang
- Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK
| | - Cory D Dunn
- Institute of Biotechnology, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Molecular Biology and Genetics, Koç University, İstanbul, Turkey
| | - James I MacRae
- Metabolomics Laboratory, The Francis Crick Institute, London, UK
| | | | - Markus Ralser
- Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK.
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Biochemistry, Berlin, Germany.
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK.
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32
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Blombach B, Grünberger A, Centler F, Wierckx N, Schmid J. Exploiting unconventional prokaryotic hosts for industrial biotechnology. Trends Biotechnol 2021; 40:385-397. [PMID: 34482995 DOI: 10.1016/j.tibtech.2021.08.003] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 08/06/2021] [Accepted: 08/06/2021] [Indexed: 12/13/2022]
Abstract
Developing cost-efficient biotechnological processes is a major challenge in replacing fossil-based industrial production processes. The remarkable progress in genetic engineering ensures efficient and fast tailoring of microbial metabolism for a wide range of bioconversions. However, improving intrinsic properties such as tolerance, handling, growth, and substrate consumption rates is still challenging. At the same time, synthetic biology tools are becoming easier applicable and transferable to nonmodel organisms. These trends have resulted in the exploitation of new and unconventional microbial systems with sophisticated properties, which render them promising hosts for the bio-based industry. Here, we highlight the metabolic and cellular capabilities of representative prokaryotic newcomers and discuss the potential and drawbacks of these hosts for industrial application.
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Affiliation(s)
- Bastian Blombach
- Microbial Biotechnology, Campus Straubing for Biotechnology and Sustainability, Technical University of Munich, Straubing, Germany; SynBiofoundry@TUM, Technical University of Munich, Straubing, Germany
| | | | - Florian Centler
- Department of Environmental Microbiology, UFZ - Helmholtz Centre for Environmental Research, Leipzig, Germany
| | - Nick Wierckx
- Forschungszentrum Jülich, Institute of Bio- and Geosciences IBG-1: Biotechnology, Jülich, Germany
| | - Jochen Schmid
- Institute of Molecular Microbiology and Biotechnology, University of Münster, Münster, Germany.
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33
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Cable J, Pourquié O, Wellen KE, Finley LWS, Aulehla A, Gould AP, Teleman A, Tu WB, Garrett WS, Miguel-Aliaga I, Perrimon N, Hooper LV, Walhout AJM, Wei W, Alexandrov T, Erez A, Ralser M, Rabinowitz JD, Hemalatha A, Gutiérrez-Pérez P, Chandel NS, Rutter J, Locasale JW, Landoni JC, Christofk H. Metabolic decisions in development and disease-a Keystone Symposia report. Ann N Y Acad Sci 2021; 1506:55-73. [PMID: 34414571 DOI: 10.1111/nyas.14678] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 07/31/2021] [Indexed: 12/11/2022]
Abstract
There is an increasing appreciation for the role of metabolism in cell signaling and cell decision making. Precise metabolic control is essential in development, as evident by the disorders caused by mutations in metabolic enzymes. The metabolic profile of cells is often cell-type specific, changing as cells differentiate or during tumorigenesis. Recent evidence has shown that changes in metabolism are not merely a consequence of changes in cell state but that metabolites can serve to promote and/or inhibit these changes. Metabolites can link metabolic pathways with cell signaling pathways via several mechanisms, for example, by serving as substrates for protein post-translational modifications, by affecting enzyme activity via allosteric mechanisms, or by altering epigenetic markers. Unraveling the complex interactions governing metabolism, gene expression, and protein activity that ultimately govern a cell's fate will require new tools and interactions across disciplines. On March 24 and 25, 2021, experts in cell metabolism, developmental biology, and human disease met virtually for the Keystone eSymposium, "Metabolic Decisions in Development and Disease." The discussions explored how metabolites impact cellular and developmental decisions in a diverse range of model systems used to investigate normal development, developmental disorders, dietary effects, and cancer-mediated changes in metabolism.
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Affiliation(s)
| | - Olivier Pourquié
- Department of Genetics, Harvard Medical School, Boston, Massachusetts.,Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts.,Harvard Stem Cell Institute, Boston, Massachusetts
| | - Kathryn E Wellen
- Department of Cancer Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Lydia W S Finley
- Cell Biology Program and Center for Epigenetics Research, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Alexander Aulehla
- Developmental Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | | | - Aurelio Teleman
- German Cancer Research Center (DKFZ) and Heidelberg University, Heidelberg, Germany
| | - William B Tu
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Wendy Sarah Garrett
- Harvard T. H. Chan School of Public Health and Dana-Farber Cancer, Boston, Massachusetts
| | - Irene Miguel-Aliaga
- MRC London Institute of Medical Sciences and Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK
| | - Norbert Perrimon
- Department of Genetics, Blavatnik Institute, Harvard Medical School and Howard Hughes Institute, Boston, Massachusetts
| | - Lora V Hooper
- Department of Immunology and Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, Texas
| | - A J Marian Walhout
- Program in Systems Biology and Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Wei Wei
- Department of Pathology, Stanford University School of Medicine, Stanford, California.,Department of Biology and Stanford ChEM-H, Stanford University, Stanford, California
| | - Theodore Alexandrov
- Structural and Computational Biology Unit and Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, Heidelberg, Germany.,Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California
| | - Ayelet Erez
- Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel
| | - Markus Ralser
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK.,Department of Biochemistry, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Joshua D Rabinowitz
- Department of Chemistry and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey
| | - Anupama Hemalatha
- Department of Genetics, Yale School of Medicine, New Haven, Connecticut
| | - Paula Gutiérrez-Pérez
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria
| | - Navdeep S Chandel
- Department of Medicine, Robert H. Lurie Cancer Center, Chicago, Illinois.,Department of Biochemistry and Molecular Genetics, Robert H. Lurie Cancer Center Metabolomics Core, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Jared Rutter
- Department of Biochemistry and Howard Hughes Medical Institute, University of Utah, Salt Lake City, Utah
| | - Jason W Locasale
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, North Carolina
| | - Juan C Landoni
- Research Program in Stem Cells and Metabolism, University of Helsinki, Helsinki, Finland
| | - Heather Christofk
- Departments of Biological Chemistry and Molecular & Medical Pharmacology, David Geffen School of Medicine at UCLA, Los Angeles, California
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34
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Messner CB, Demichev V, Bloomfield N, Yu JSL, White M, Kreidl M, Egger AS, Freiwald A, Ivosev G, Wasim F, Zelezniak A, Jürgens L, Suttorp N, Sander LE, Kurth F, Lilley KS, Mülleder M, Tate S, Ralser M. Ultra-fast proteomics with Scanning SWATH. Nat Biotechnol 2021; 39:846-854. [PMID: 33767396 PMCID: PMC7611254 DOI: 10.1038/s41587-021-00860-4] [Citation(s) in RCA: 130] [Impact Index Per Article: 43.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 02/18/2021] [Indexed: 01/31/2023]
Abstract
Accurate quantification of the proteome remains challenging for large sample series and longitudinal experiments. We report a data-independent acquisition method, Scanning SWATH, that accelerates mass spectrometric (MS) duty cycles, yielding quantitative proteomes in combination with short gradients and high-flow (800 µl min-1) chromatography. Exploiting a continuous movement of the precursor isolation window to assign precursor masses to tandem mass spectrometry (MS/MS) fragment traces, Scanning SWATH increases precursor identifications by ~70% compared to conventional data-independent acquisition (DIA) methods on 0.5-5-min chromatographic gradients. We demonstrate the application of ultra-fast proteomics in drug mode-of-action screening and plasma proteomics. Scanning SWATH proteomes capture the mode of action of fungistatic azoles and statins. Moreover, we confirm 43 and identify 11 new plasma proteome biomarkers of COVID-19 severity, advancing patient classification and biomarker discovery. Thus, our results demonstrate a substantial acceleration and increased depth in fast proteomic experiments that facilitate proteomic drug screens and clinical studies.
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Affiliation(s)
- Christoph B Messner
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Vadim Demichev
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Biochemistry, Cambridge Centre for Proteomics, University of Cambridge, Cambridge, UK
| | | | - Jason S L Yu
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Matthew White
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Marco Kreidl
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Anna-Sophia Egger
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Anja Freiwald
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Core Facility - High Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | | | | | - Aleksej Zelezniak
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Linda Jürgens
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Norbert Suttorp
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Leif Erik Sander
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Florian Kurth
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Tropical Medicine, Bernhard Nocht Institute for Tropical Medicine & I. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Kathryn S Lilley
- Department of Biochemistry, Cambridge Centre for Proteomics, University of Cambridge, Cambridge, UK
| | - Michael Mülleder
- Core Facility - High Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | | | - Markus Ralser
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK.
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
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35
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Li G, Zrimec J, Ji B, Geng J, Larsbrink J, Zelezniak A, Nielsen J, Engqvist MK. Performance of Regression Models as a Function of Experiment Noise. Bioinform Biol Insights 2021; 15:11779322211020315. [PMID: 34262264 PMCID: PMC8243133 DOI: 10.1177/11779322211020315] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 04/29/2021] [Indexed: 11/21/2022] Open
Abstract
Background: A challenge in developing machine learning regression models is that it is
difficult to know whether maximal performance has been reached on the test
dataset, or whether further model improvement is possible. In biology, this
problem is particularly pronounced as sample labels (response variables) are
typically obtained through experiments and therefore have experiment noise
associated with them. Such label noise puts a fundamental limit to the
metrics of performance attainable by regression models on the test
dataset. Results: We address this challenge by deriving an expected upper bound for the
coefficient of determination (R2) for regression
models when tested on the holdout dataset. This upper bound depends only on
the noise associated with the response variable in a dataset as well as its
variance. The upper bound estimate was validated via Monte Carlo simulations
and then used as a tool to bootstrap performance of regression models
trained on biological datasets, including protein sequence data,
transcriptomic data, and genomic data. Conclusions: The new method for estimating upper bounds for model performance on test data
should aid researchers in developing ML regression models that reach their
maximum potential. Although we study biological datasets in this work, the
new upper bound estimates will hold true for regression models from any
research field or application area where response variables have associated
noise.
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Affiliation(s)
- Gang Li
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Jan Zrimec
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Boyang Ji
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Jun Geng
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Johan Larsbrink
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Aleksej Zelezniak
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.,Science for Life Laboratory, Stockholm, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark.,BioInnovation Institute, Copenhagen N, Denmark
| | - Martin Km Engqvist
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
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36
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Zrimec J, Buric F, Kokina M, Garcia V, Zelezniak A. Learning the Regulatory Code of Gene Expression. Front Mol Biosci 2021; 8:673363. [PMID: 34179082 PMCID: PMC8223075 DOI: 10.3389/fmolb.2021.673363] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 05/24/2021] [Indexed: 11/13/2022] Open
Abstract
Data-driven machine learning is the method of choice for predicting molecular phenotypes from nucleotide sequence, modeling gene expression events including protein-DNA binding, chromatin states as well as mRNA and protein levels. Deep neural networks automatically learn informative sequence representations and interpreting them enables us to improve our understanding of the regulatory code governing gene expression. Here, we review the latest developments that apply shallow or deep learning to quantify molecular phenotypes and decode the cis-regulatory grammar from prokaryotic and eukaryotic sequencing data. Our approach is to build from the ground up, first focusing on the initiating protein-DNA interactions, then specific coding and non-coding regions, and finally on advances that combine multiple parts of the gene and mRNA regulatory structures, achieving unprecedented performance. We thus provide a quantitative view of gene expression regulation from nucleotide sequence, concluding with an information-centric overview of the central dogma of molecular biology.
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Affiliation(s)
- Jan Zrimec
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Filip Buric
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Mariia Kokina
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Victor Garcia
- School of Life Sciences and Facility Management, Zurich University of Applied Sciences, Wädenswil, Switzerland
| | - Aleksej Zelezniak
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Science for Life Laboratory, Stockholm, Sweden
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37
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Buric F, Zrimec J, Zelezniak A. Parallel Factor Analysis Enables Quantification and Identification of Highly Convolved Data-Independent-Acquired Protein Spectra. PATTERNS (NEW YORK, N.Y.) 2020; 1:100137. [PMID: 33336195 PMCID: PMC7733873 DOI: 10.1016/j.patter.2020.100137] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 09/14/2020] [Accepted: 10/12/2020] [Indexed: 11/26/2022]
Abstract
High-throughput data-independent acquisition (DIA) is the method of choice for quantitative proteomics, combining the best practices of targeted and shotgun approaches. The resultant DIA spectra are, however, highly convolved and with no direct precursor-fragment correspondence, complicating biological sample analysis. Here, we present CANDIA (canonical decomposition of data-independent-acquired spectra), a GPU-powered unsupervised multiway factor analysis framework that deconvolves multispectral scans to individual analyte spectra, chromatographic profiles, and sample abundances, using parallel factor analysis. The deconvolved spectra can be annotated with traditional database search engines or used as high-quality input for de novo sequencing methods. We demonstrate that spectral libraries generated with CANDIA substantially reduce the false discovery rate underlying the validation of spectral quantification. CANDIA covers up to 33 times more total ion current than library-based approaches, which typically use less than 5% of total recorded ions, thus allowing quantification and identification of signals from unexplored DIA spectra. Conventional DIA spectral libraries cover less than 3% of a scan's total ion count CANDIA deconvolves peptide signals by leveraging all scan data CANDIA uses GPUs to enable tensor algebra on massive DIA mass spectrometry data CANDIA output enables high-confidence and precise quantitative proteomics
The latest high-throughput mass spectrometry-based technologies can record virtually all molecules from complex biological samples, providing a holistic picture of proteomes in cells and tissues and enabling an evaluation of the overall status of a person's health. However, current best practices are still only scratching the surface of the wealth of available information obtained from the massive proteome datasets, and efficient novel data-driven strategies are needed. Powered by advances in GPU hardware and open-source machine-learning frameworks, we developed a data-driven approach, CANDIA, which disassembles highly complex proteomics data into the elementary molecular signatures of the proteins in biological samples. Our work provides a performant and adaptable solution that complements existing mass spectrometry techniques. As the central mathematical methods are generic, other scientific fields that are dealing with highly convolved datasets will benefit from this work.
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Affiliation(s)
- Filip Buric
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, Gothenburg 412 96, Sweden
| | - Jan Zrimec
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, Gothenburg 412 96, Sweden
| | - Aleksej Zelezniak
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, Gothenburg 412 96, Sweden.,Science for Life Laboratory, Tomtebodavägen 23a, Stockholm 171 65, Sweden
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38
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Zrimec J, Börlin CS, Buric F, Muhammad AS, Chen R, Siewers V, Verendel V, Nielsen J, Töpel M, Zelezniak A. Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure. Nat Commun 2020; 11:6141. [PMID: 33262328 PMCID: PMC7708451 DOI: 10.1038/s41467-020-19921-4] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 11/02/2020] [Indexed: 12/31/2022] Open
Abstract
Understanding the genetic regulatory code governing gene expression is an important challenge in molecular biology. However, how individual coding and non-coding regions of the gene regulatory structure interact and contribute to mRNA expression levels remains unclear. Here we apply deep learning on over 20,000 mRNA datasets to examine the genetic regulatory code controlling mRNA abundance in 7 model organisms ranging from bacteria to Human. In all organisms, we can predict mRNA abundance directly from DNA sequence, with up to 82% of the variation of transcript levels encoded in the gene regulatory structure. By searching for DNA regulatory motifs across the gene regulatory structure, we discover that motif interactions could explain the whole dynamic range of mRNA levels. Co-evolution across coding and non-coding regions suggests that it is not single motifs or regions, but the entire gene regulatory structure and specific combination of regulatory elements that define gene expression levels.
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Affiliation(s)
- Jan Zrimec
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
| | - Christoph S Börlin
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
| | - Filip Buric
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
| | - Azam Sheikh Muhammad
- Computer Science and Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
| | - Rhongzen Chen
- Computer Science and Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
| | - Verena Siewers
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
| | - Vilhelm Verendel
- Computer Science and Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
| | - Mats Töpel
- Department of Marine Sciences, University of Gothenburg, Box 461, SE-405 30, Gothenburg, Sweden
- Gothenburg Global Biodiversity Center (GGBC), Box 461, 40530, Gothenburg, Sweden
| | - Aleksej Zelezniak
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden.
- Science for Life Laboratory, Tomtebodavägen 23a, SE-171 65, Stockholm, Sweden.
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39
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Helmy M, Smith D, Selvarajoo K. Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering. Metab Eng Commun 2020; 11:e00149. [PMID: 33072513 PMCID: PMC7546651 DOI: 10.1016/j.mec.2020.e00149] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 10/01/2020] [Accepted: 10/07/2020] [Indexed: 12/05/2022] Open
Abstract
Metabolic engineering aims to maximize the production of bio-economically important substances (compounds, enzymes, or other proteins) through the optimization of the genetics, cellular processes and growth conditions of microorganisms. This requires detailed understanding of underlying metabolic pathways involved in the production of the targeted substances, and how the cellular processes or growth conditions are regulated by the engineering. To achieve this goal, a large system of experimental techniques, compound libraries, computational methods and data resources, including multi-omics data, are used. The recent advent of multi-omics systems biology approaches significantly impacted the field by opening new avenues to perform dynamic and large-scale analyses that deepen our knowledge on the manipulations. However, with the enormous transcriptomics, proteomics and metabolomics available, it is a daunting task to integrate the data for a more holistic understanding. Novel data mining and analytics approaches, including Artificial Intelligence (AI), can provide breakthroughs where traditional low-throughput experiment-alone methods cannot easily achieve. Here, we review the latest attempts of combining systems biology and AI in metabolic engineering research, and highlight how this alliance can help overcome the current challenges facing industrial biotechnology, especially for food-related substances and compounds using microorganisms.
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Affiliation(s)
- Mohamed Helmy
- Singapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore
| | - Derek Smith
- Singapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore
| | - Kumar Selvarajoo
- Singapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore
- Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore (NUS), Singapore, Singapore
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40
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Donati S, Kuntz M, Pahl V, Farke N, Beuter D, Glatter T, Gomes-Filho JV, Randau L, Wang CY, Link H. Multi-omics Analysis of CRISPRi-Knockdowns Identifies Mechanisms that Buffer Decreases of Enzymes in E. coli Metabolism. Cell Syst 2020; 12:56-67.e6. [PMID: 33238135 DOI: 10.1016/j.cels.2020.10.011] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 09/23/2020] [Accepted: 10/29/2020] [Indexed: 12/21/2022]
Abstract
Enzymes maintain metabolism, and their concentration affects cellular fitness: high enzyme levels are costly, and low enzyme levels can limit metabolic flux. Here, we used CRISPR interference (CRISPRi) to study the consequences of decreasing E. coli enzymes below wild-type levels. A pooled CRISPRi screen with 7,177 strains demonstrates that metabolism buffers fitness defects for hours after the induction of CRISPRi. We characterized the metabolome and proteome responses in 30 CRISPRi strains and elucidated three gene-specific buffering mechanisms: ornithine buffered the knockdown of carbamoyl phosphate synthetase (CarAB) by increasing CarAB activity, S-adenosylmethionine buffered the knockdown of homocysteine transmethylase (MetE) by de-repressing expression of the methionine pathway, and 6-phosphogluconate buffered the knockdown of 6-phosphogluconate dehydrogenase (Gnd) by activating a bypass. In total, this work demonstrates that CRISPRi screens can reveal global sources of metabolic robustness and identify local regulatory mechanisms that buffer decreases of specific enzymes. A record of this paper's transparent peer review process is included in the Supplemental Information.
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Affiliation(s)
- Stefano Donati
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Michelle Kuntz
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Vanessa Pahl
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Niklas Farke
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Dominik Beuter
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Timo Glatter
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | | | - Lennart Randau
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Chun-Ying Wang
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Hannes Link
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.
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41
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Lawson CE, Martí JM, Radivojevic T, Jonnalagadda SVR, Gentz R, Hillson NJ, Peisert S, Kim J, Simmons BA, Petzold CJ, Singer SW, Mukhopadhyay A, Tanjore D, Dunn JG, Garcia Martin H. Machine learning for metabolic engineering: A review. Metab Eng 2020; 63:34-60. [PMID: 33221420 DOI: 10.1016/j.ymben.2020.10.005] [Citation(s) in RCA: 86] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 10/22/2020] [Accepted: 10/31/2020] [Indexed: 12/14/2022]
Abstract
Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.
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Affiliation(s)
- Christopher E Lawson
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA
| | - Jose Manuel Martí
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Tijana Radivojevic
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Sai Vamshi R Jonnalagadda
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Reinhard Gentz
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Nathan J Hillson
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Sean Peisert
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; University of California Davis, Davis, CA, 95616, USA
| | - Joonhoon Kim
- Joint BioEnergy Institute, Emeryville, CA, 94608, USA; Pacific Northwest National Laboratory, Richland, 99354, WA, USA
| | - Blake A Simmons
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Christopher J Petzold
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Steven W Singer
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA
| | - Aindrila Mukhopadhyay
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, USA
| | - Deepti Tanjore
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Advanced Biofuels and Bioproducts Process Development Unit, Emeryville, CA, 94608, USA
| | | | - Hector Garcia Martin
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA; Basque Center for Applied Mathematics, 48009, Bilbao, Spain; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, USA.
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42
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Sailwal M, Das AJ, Gazara RK, Dasgupta D, Bhaskar T, Hazra S, Ghosh D. Connecting the dots: Advances in modern metabolomics and its application in yeast system. Biotechnol Adv 2020; 44:107616. [DOI: 10.1016/j.biotechadv.2020.107616] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 08/15/2020] [Accepted: 08/17/2020] [Indexed: 12/15/2022]
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43
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Antonakoudis A, Barbosa R, Kotidis P, Kontoravdi C. The era of big data: Genome-scale modelling meets machine learning. Comput Struct Biotechnol J 2020; 18:3287-3300. [PMID: 33240470 PMCID: PMC7663219 DOI: 10.1016/j.csbj.2020.10.011] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 10/07/2020] [Accepted: 10/08/2020] [Indexed: 12/15/2022] Open
Abstract
With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling.
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Affiliation(s)
| | | | | | - Cleo Kontoravdi
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
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44
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Baranwal M, Magner A, Elvati P, Saldinger J, Violi A, Hero AO. A deep learning architecture for metabolic pathway prediction. Bioinformatics 2020; 36:2547-2553. [PMID: 31879763 DOI: 10.1093/bioinformatics/btz954] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 12/02/2019] [Accepted: 12/22/2019] [Indexed: 01/14/2023] Open
Abstract
MOTIVATION Understanding the mechanisms and structural mappings between molecules and pathway classes are critical for design of reaction predictors for synthesizing new molecules. This article studies the problem of prediction of classes of metabolic pathways (series of chemical reactions occurring within a cell) in which a given biochemical compound participates. We apply a hybrid machine learning approach consisting of graph convolutional networks used to extract molecular shape features as input to a random forest classifier. In contrast to previously applied machine learning methods for this problem, our framework automatically extracts relevant shape features directly from input SMILES representations, which are atom-bond specifications of chemical structures composing the molecules. RESULTS Our method is capable of correctly predicting the respective metabolic pathway class of 95.16% of tested compounds, whereas competing methods only achieve an accuracy of 84.92% or less. Furthermore, our framework extends to the task of classification of compounds having mixed membership in multiple pathway classes. Our prediction accuracy for this multi-label task is 97.61%. We analyze the relative importance of various global physicochemical features to the pathway class prediction problem and show that simple linear/logistic regression models can predict the values of these global features from the shape features extracted using our framework. AVAILABILITY AND IMPLEMENTATION https://github.com/baranwa2/MetabolicPathwayPrediction. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mayank Baranwal
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA
| | - Abram Magner
- Department of Computer Science, University at Albany, SUNY, Albany, NY 12222, USA
| | | | | | - Angela Violi
- Department of Mechanical Engineering.,Department of Chemical Engineering and Biophysics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Alfred O Hero
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA
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Li J, Paulo JA, Nusinow DP, Huttlin EL, Gygi SP. Investigation of Proteomic and Phosphoproteomic Responses to Signaling Network Perturbations Reveals Functional Pathway Organizations in Yeast. Cell Rep 2020; 29:2092-2104.e4. [PMID: 31722220 PMCID: PMC7382779 DOI: 10.1016/j.celrep.2019.10.034] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 05/30/2019] [Accepted: 10/09/2019] [Indexed: 12/25/2022] Open
Abstract
Governance of protein phosphorylation by kinases and phosphatases constitutes an essential regulatory network in eukaryotic cells. Network dysregulation leads to severe consequences and is often a key factor in disease pathogenesis. Previous studies revealed multiple roles for protein phosphorylation and pathway structures in cellular functions from different perspectives. We seek to understand the roles of kinases and phosphatases from a protein homeostasis point of view. Using a streamlined tandem mass tag (SL-TMT) strategy, we systematically measure proteomic and phosphoproteomic responses to perturbations of phosphorylation signaling networks in yeast deletion strains. Our results emphasize the requirement for protein normalization for more complete interpretation of phosphorylation data. Functional relationships between kinases and phosphatases were characterized at both proteome and phosphoproteome levels in three ways: (1) Gene Ontology enrichment analysis, (2) Δgene-Δgene correlation networks, and (3) molecule covariance networks. This resource illuminates kinase and phosphatase functions and pathway organizations.
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Affiliation(s)
- Jiaming Li
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Joao A Paulo
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - David P Nusinow
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Edward L Huttlin
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Steven P Gygi
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA.
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Spirulina-in Silico-Mutations and Their Comparative Analyses in the Metabolomics Scale by Using Proteome-Based Flux Balance Analysis. Cells 2020; 9:cells9092097. [PMID: 32942547 PMCID: PMC7563286 DOI: 10.3390/cells9092097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 08/28/2020] [Accepted: 09/05/2020] [Indexed: 11/17/2022] Open
Abstract
This study used an in silico metabolic engineering strategy for modifying the metabolic capabilities of Spirulina under specific conditions as an approach to modifying culture conditions in order to generate the intended outputs. In metabolic models, the basic metabolic fluxes in steady-state metabolic networks have generally been controlled by stoichiometric reactions; however, this approach does not consider the regulatory mechanism of the proteins responsible for the metabolic reactions. The protein regulatory network plays a critical role in the response to stresses, including environmental stress, encountered by an organism. Thus, the integration of the response mechanism of Spirulina to growth temperature stresses was investigated via simulation of a proteome-based GSMM, in which the boundaries were established by using protein expression levels obtained from quantitative proteomic analysis. The proteome-based flux balance analysis (FBA) under an optimal growth temperature (35 °C), a low growth temperature (22 °C) and a high growth temperature (40 °C) showed biomass yields that closely fit the experimental data obtained in previous research. Moreover, the response mechanism was analyzed by the integration of the proteome and protein-protein interaction (PPI) network, and those data were used to support in silico knockout/overexpression of selected proteins involved in the PPI network. The Spirulina, wild-type, proteome fluxes under different growth temperatures and those of mutants were compared, and the proteins/enzymes catalyzing the different flux levels were mapped onto their designated pathways for biological interpretation.
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Rana P, Berry C, Ghosh P, Fong SS. Recent advances on constraint-based models by integrating machine learning. Curr Opin Biotechnol 2020; 64:85-91. [DOI: 10.1016/j.copbio.2019.11.007] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Revised: 11/04/2019] [Accepted: 11/06/2019] [Indexed: 01/06/2023]
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48
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Buccitelli C, Selbach M. mRNAs, proteins and the emerging principles of gene expression control. Nat Rev Genet 2020; 21:630-644. [PMID: 32709985 DOI: 10.1038/s41576-020-0258-4] [Citation(s) in RCA: 470] [Impact Index Per Article: 117.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/15/2020] [Indexed: 12/15/2022]
Abstract
Gene expression involves transcription, translation and the turnover of mRNAs and proteins. The degree to which protein abundances scale with mRNA levels and the implications in cases where this dependency breaks down remain an intensely debated topic. Here we review recent mRNA-protein correlation studies in the light of the quantitative parameters of the gene expression pathway, contextual confounders and buffering mechanisms. Although protein and mRNA levels typically show reasonable correlation, we describe how transcriptomics and proteomics provide useful non-redundant readouts. Integrating both types of data can reveal exciting biology and is an essential step in refining our understanding of the principles of gene expression control.
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Affiliation(s)
| | - Matthias Selbach
- Proteome Dynamics, Max Delbrück Center for Molecular Medicine, Berlin, Germany. .,Charité - Universitätsmedizin Berlin, Berlin, Germany.
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Volk MJ, Lourentzou I, Mishra S, Vo LT, Zhai C, Zhao H. Biosystems Design by Machine Learning. ACS Synth Biol 2020; 9:1514-1533. [PMID: 32485108 DOI: 10.1021/acssynbio.0c00129] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. However, the intricate connectivity and resulting complexity of biosystems poses a major hurdle in designing biosystems with desirable features. As -omics and other high throughput technologies have been rapidly developed, the promise of applying machine learning (ML) techniques in biosystems design has started to become a reality. ML models enable the identification of patterns within complicated biological data across multiple scales of analysis and can augment biosystems design applications by predicting new candidates for optimized performance. ML is being used at every stage of biosystems design to help find nonobvious engineering solutions with fewer design iterations. In this review, we first describe commonly used models and modeling paradigms within ML. We then discuss some applications of these models that have already shown success in biotechnological applications. Moreover, we discuss successful applications at all scales of biosystems design, including nucleic acids, genetic circuits, proteins, pathways, genomes, and bioprocesses. Finally, we discuss some limitations of these methods and potential solutions as well as prospects of the combination of ML and biosystems design.
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Rational flux-tuning of Halomonas bluephagenesis for co-production of bioplastic PHB and ectoine. Nat Commun 2020; 11:3313. [PMID: 32620759 PMCID: PMC7334215 DOI: 10.1038/s41467-020-17223-3] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 06/16/2020] [Indexed: 11/21/2022] Open
Abstract
Ectoine, a compatible solute synthesized by many halophiles for hypersalinity resistance, has been successfully produced by metabolically engineered Halomonas bluephagenesis, which is a bioplastic poly(3-hydroxybutyrate) producer allowing open unsterile and continuous conditions. Here we report a de novo synthesis pathway for ectoine constructed into the chromosome of H. bluephagenesis utilizing two inducible systems, which serve to fine-tune the transcription levels of three clusters related to ectoine synthesis, including ectABC, lysC and asd based on a GFP-mediated transcriptional tuning approach. Combined with bypasses deletion, the resulting recombinant H. bluephagenesis TD-ADEL-58 is able to produce 28 g L−1 ectoine during a 28 h fed-batch growth process. Co-production of ectoine and PHB is achieved to 8 g L−1 ectoine and 32 g L−1 dry cell mass containing 75% PHB after a 44 h growth. H. bluephagenesis demonstrates to be a suitable co-production chassis for polyhydroxyalkanoates and non-polymer chemicals such as ectoine. Halomonas bluephagenesis is a halophilic platform bacterium for next generation industrial biotechnology. Here, the authors employ a stimulus response-based flux-tuning method for coproduction of bioplastic PHB and ectoine under open unsterile and continuous growth conditions.
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