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Xavier JB. Machine learning of cellular metabolic rewiring. Biol Methods Protoc 2024; 9:bpae048. [PMID: 39011352 PMCID: PMC11249387 DOI: 10.1093/biomethods/bpae048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 06/14/2024] [Accepted: 07/01/2024] [Indexed: 07/17/2024] Open
Abstract
Metabolic rewiring allows cells to adapt their metabolism in response to evolving environmental conditions. Traditional metabolomics techniques, whether targeted or untargeted, often struggle to interpret these adaptive shifts. Here, we introduce MetaboLiteLearner, a lightweight machine learning framework that harnesses the detailed fragmentation patterns from electron ionization (EI) collected in scan mode during gas chromatography/mass spectrometry to predict changes in the metabolite composition of metabolically adapted cells. When tested on breast cancer cells with different preferences to metastasize to specific organs, MetaboLiteLearner predicted the impact of metabolic rewiring on metabolites withheld from the training dataset using only the EI spectra, without metabolite identification or pre-existing knowledge of metabolic networks. Despite its simplicity, the model learned captured shared and unique metabolomic shifts between brain- and lung-homing metastatic lineages, suggesting cellular adaptations associated with metastasis to specific organs. Integrating machine learning and metabolomics paves the way for new insights into complex cellular adaptations.
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Affiliation(s)
- Joao B Xavier
- Program for Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Xavier JB. Machine learning of cellular metabolic rewiring. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.11.552957. [PMID: 37645838 PMCID: PMC10462012 DOI: 10.1101/2023.08.11.552957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Metabolic rewiring allows cells to adapt their metabolism in response to evolving environmental conditions. Traditional metabolomics techniques, whether targeted or untargeted, often struggle to interpret these adaptive shifts. Here, we introduce MetaboLiteLearner, a machine learning framework that harnesses the detailed fragmentation patterns from electron ionization (EI) collected in scan mode during gas chromatography/mass spectrometry (GC/MS) to predict abundance changes in metabolically adapted cells. When tested on breast cancer cells with different preferences to metastasize to specific organs, MetaboLiteLearner predicted the impact of metabolic rewiring on metabolites withheld from the training dataset using only the EI spectra, without metabolite identification or pre-existing knowledge of metabolic networks. The model learned captures shared and unique metabolomic shifts between brain- and lung-homing metastatic lineages, suggesting potential organ-tailored cellular adaptations. Integrating machine learning and metabolomics paves the way for new insights into complex cellular adaptations.
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Affiliation(s)
- Joao B Xavier
- Program for Computational and Systems Biology, Sloan Kettering Institute for Cancer Research
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Zou N, Baydogan M, Zhu Y, Wang W, Zhu J, Li J. A Transfer Learning Approach for Predictive Modeling of Degenerate Biological Systems. Technometrics 2015; 57:362-373. [PMID: 27330227 DOI: 10.1080/00401706.2015.1044117] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Modeling of a new domain can be challenging due to scarce data and high-dimensionality. Transfer learning aims to integrate data of the new domain with knowledge about some related old domains, in order to model the new domain better. This paper studies transfer learning for degenerate biological systems. Degeneracy refers to the phenomenon that structurally different elements of the system perform the same/similar function or yield the same/similar output. Degeneracy exits in various biological systems and contributes to the heterogeneity, complexity, and robustness of the systems. Modeling of degenerate biological systems is challenging and models enabling transfer learning in such systems have been little studied. In this paper, we propose a predictive model that integrates transfer learning and degeneracy under a Bayesian framework. Theoretical properties of the proposed model are studied. Finally, we present an application of modeling the predictive relationship between transcription factors and gene expression across multiple cell lines. The model achieves good prediction accuracy, and identifies known and possibly new degenerate mechanisms of the system.
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Affiliation(s)
- Na Zou
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University
| | - Mustafa Baydogan
- Department of Industrial Engineering, Bogazici University, Turkey
| | - Yun Zhu
- Department of Chemistry and Biochemistry, University of California San Diego
| | - Wei Wang
- Department of Chemistry and Biochemistry, University of California San Diego
| | - Ji Zhu
- Department of Statistics, University of Michigan
| | - Jing Li
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University
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Setty M, Helmy K, Khan AA, Silber J, Arvey A, Neezen F, Agius P, Huse JT, Holland EC, Leslie CS. Inferring transcriptional and microRNA-mediated regulatory programs in glioblastoma. Mol Syst Biol 2012; 8:605. [PMID: 22929615 PMCID: PMC3435504 DOI: 10.1038/msb.2012.37] [Citation(s) in RCA: 74] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2011] [Accepted: 07/25/2012] [Indexed: 01/07/2023] Open
Abstract
Large-scale cancer genomics projects are profiling hundreds of tumors at multiple molecular layers, including copy number, mRNA and miRNA expression, but the mechanistic relationships between these layers are often excluded from computational models. We developed a supervised learning framework for integrating molecular profiles with regulatory sequence information to reveal regulatory programs in cancer, including miRNA-mediated regulation. We applied our approach to 320 glioblastoma profiles and identified key miRNAs and transcription factors as common or subtype-specific drivers of expression changes. We confirmed that predicted gene expression signatures for proneural subtype regulators were consistent with in vivo expression changes in a PDGF-driven mouse model. We tested two predicted proneural drivers, miR-124 and miR-132, both underexpressed in proneural tumors, by overexpression in neurospheres and observed a partial reversal of corresponding tumor expression changes. Computationally dissecting the role of miRNAs in cancer may ultimately lead to small RNA therapeutics tailored to subtype or individual.
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Affiliation(s)
- Manu Setty
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Karim Helmy
- Cancer Biology and Genetics Program, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Aly A Khan
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Joachim Silber
- Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Aaron Arvey
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Frank Neezen
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Phaedra Agius
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Jason T Huse
- Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Eric C Holland
- Cancer Biology and Genetics Program, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Christina S Leslie
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
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Spencer WC, Zeller G, Watson JD, Henz SR, Watkins KL, McWhirter RD, Petersen S, Sreedharan VT, Widmer C, Jo J, Reinke V, Petrella L, Strome S, Von Stetina SE, Katz M, Shaham S, Rätsch G, Miller DM. A spatial and temporal map of C. elegans gene expression. Genome Res 2011; 21:325-41. [PMID: 21177967 PMCID: PMC3032935 DOI: 10.1101/gr.114595.110] [Citation(s) in RCA: 211] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2010] [Accepted: 12/08/2010] [Indexed: 01/31/2023]
Abstract
The C. elegans genome has been completely sequenced, and the developmental anatomy of this model organism is described at single-cell resolution. Here we utilize strategies that exploit this precisely defined architecture to link gene expression to cell type. We obtained RNAs from specific cells and from each developmental stage using tissue-specific promoters to mark cells for isolation by FACS or for mRNA extraction by the mRNA-tagging method. We then generated gene expression profiles of more than 30 different cells and developmental stages using tiling arrays. Machine-learning-based analysis detected transcripts corresponding to established gene models and revealed novel transcriptionally active regions (TARs) in noncoding domains that comprise at least 10% of the total C. elegans genome. Our results show that about 75% of transcripts with detectable expression are differentially expressed among developmental stages and across cell types. Examination of known tissue- and cell-specific transcripts validates these data sets and suggests that newly identified TARs may exercise cell-specific functions. Additionally, we used self-organizing maps to define groups of coregulated transcripts and applied regulatory element analysis to identify known transcription factor- and miRNA-binding sites, as well as novel motifs that likely function to control subsets of these genes. By using cell-specific, whole-genome profiling strategies, we have detected a large number of novel transcripts and produced high-resolution gene expression maps that provide a basis for establishing the roles of individual genes in cellular differentiation.
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Affiliation(s)
- W. Clay Spencer
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee 37232, USA
| | - Georg Zeller
- Friedrich Miescher Laboratory of the Max Planck Society, 72076 Tübingen, Germany
- Department of Molecular Biology, Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany
| | - Joseph D. Watson
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee 37232, USA
| | - Stefan R. Henz
- Department of Molecular Biology, Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany
| | - Kathie L. Watkins
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee 37232, USA
| | - Rebecca D. McWhirter
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee 37232, USA
| | - Sarah Petersen
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee 37232, USA
| | - Vipin T. Sreedharan
- Friedrich Miescher Laboratory of the Max Planck Society, 72076 Tübingen, Germany
| | - Christian Widmer
- Friedrich Miescher Laboratory of the Max Planck Society, 72076 Tübingen, Germany
| | - Jeanyoung Jo
- Department of Genetics, Yale University School of Medicine, New Haven, Connecticut 06520, USA
| | - Valerie Reinke
- Department of Genetics, Yale University School of Medicine, New Haven, Connecticut 06520, USA
| | - Lisa Petrella
- Department of MCD Biology, University of California Santa Cruz, Santa Cruz, California 95064, USA
| | - Susan Strome
- Department of MCD Biology, University of California Santa Cruz, Santa Cruz, California 95064, USA
| | - Stephen E. Von Stetina
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee 37232, USA
| | - Menachem Katz
- Laboratory of Developmental Genetics, The Rockefeller University, New York, New York 10065, USA
| | - Shai Shaham
- Laboratory of Developmental Genetics, The Rockefeller University, New York, New York 10065, USA
| | - Gunnar Rätsch
- Friedrich Miescher Laboratory of the Max Planck Society, 72076 Tübingen, Germany
| | - David M. Miller
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee 37232, USA
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