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Meng W, Pan H, Sha Y, Zhai X, Xing A, Lingampelly SS, Sripathi SR, Wang Y, Li K. Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases. Metabolites 2024; 14:93. [PMID: 38392985 PMCID: PMC10890086 DOI: 10.3390/metabo14020093] [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: 12/15/2023] [Revised: 01/17/2024] [Accepted: 01/25/2024] [Indexed: 02/25/2024] Open
Abstract
The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism's phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells. This review provides an introduction to common metabolic network models and their construction methods. It also explores the diverse applications of metabolic networks in elucidating disease mechanisms, predicting and diagnosing diseases, and facilitating drug development. Additionally, it discusses potential future directions for research in metabolic networks. Ultimately, this review serves as a valuable reference for researchers interested in metabolic network modeling, analysis, and their applications.
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Affiliation(s)
- Weiyu Meng
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Hongxin Pan
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Yuyang Sha
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Xiaobing Zhai
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Abao Xing
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | | | - Srinivasa R Sripathi
- Henderson Ocular Stem Cell Laboratory, Retina Foundation of the Southwest, Dallas, TX 75231, USA
| | - Yuefei Wang
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Kefeng Li
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
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2
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Zanella PB. Letter to the Editor: Comment on "The impact of adiposity indices on lung function in children with respiratory allergic diseases". Obes Res Clin Pract 2022; 16:437. [PMID: 36192352 DOI: 10.1016/j.orcp.2022.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 09/27/2022] [Indexed: 10/07/2022]
Affiliation(s)
- Priscila Berti Zanella
- Postgraduate Program in Pulmonary Sciences, Universidade Federal do Rio Grande do Sul, Brazil.
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3
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Lawton ML, Emili A. Mass Spectrometry-Based Phosphoproteomics and Systems Biology: Approaches to Study T Lymphocyte Activation and Exhaustion. J Mol Biol 2021; 433:167318. [PMID: 34687714 DOI: 10.1016/j.jmb.2021.167318] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 10/04/2021] [Accepted: 10/15/2021] [Indexed: 11/24/2022]
Abstract
T lymphocytes respond to extracellular cues and recognize and clear foreign bodies. These functions are tightly regulated by receptor-mediated intracellular signal transduction pathways and phosphorylation cascades resulting in rewiring of transcription, cell adhesion, and metabolic pathways, which leads to changes in downstream effector functions including cytokine secretion and target-cell killing. Given that these pathways become dysregulated in chronic diseases such as cancer, auto-immunity, diabetes, and persistent infections, mapping T cell signaling dynamics in normal and pathological states is central to understanding and modulating immune system behavior. Despite recent advances, there remains much to be learned from the study of T cell signaling at a systems level. The application of global phospho-proteomic profiling technology has the potential to provide unprecedented insights into the molecular networks that govern T cell function. These include capturing the spatiotemporal dynamics of the T cell responses as an ensemble of interacting components, rather than a static view at a single point in time. In this review, we describe innovative experimental approaches to study signaling mechanisms in the TCR, co-stimulatory receptors, synthetic signaling molecules such as chimeric antigen receptors, inhibitory receptors, and T cell exhaustion. Technical advances in mass spectrometry and systems biology frameworks are emphasized as these are poised to identify currently unknown functional relationships and dependencies to create causal predictive models that expand from the traditional narrow reductionist lens of singular components in isolation.
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Affiliation(s)
- Matthew L Lawton
- Center for Network Systems Biology, Boston University, Boston, MA, USA; Department of Biochemistry, Boston University School of Medicine, Boston, MA, USA
| | - Andrew Emili
- Center for Network Systems Biology, Boston University, Boston, MA, USA; Department of Biochemistry, Boston University School of Medicine, Boston, MA, USA; Department of Biology, Boston University, Boston, MA, USA.
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4
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Scossa F, Fernie AR. Ancestral sequence reconstruction - An underused approach to understand the evolution of gene function in plants? Comput Struct Biotechnol J 2021; 19:1579-1594. [PMID: 33868595 PMCID: PMC8039532 DOI: 10.1016/j.csbj.2021.03.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 03/04/2021] [Accepted: 03/06/2021] [Indexed: 02/06/2023] Open
Abstract
Whilst substantial research effort has been placed on understanding the interactions of plant proteins with their molecular partners, relatively few studies in plants - by contrast to work in other organisms - address how these interactions evolve. It is thought that ancestral proteins were more promiscuous than modern proteins and that specificity often evolved following gene duplication and subsequent functional refining. However, ancestral protein resurrection studies have found that some modern proteins have evolved de novo from ancestors lacking those functions. Intriguingly, the new interactions evolved as a consequence of just a few mutations and, as such, acquisition of new functions appears to be neither difficult nor rare, however, only a few of them are incorporated into biological processes before they are lost to subsequent mutations. Here, we detail the approach of ancestral sequence reconstruction (ASR), providing a primer to reconstruct the sequence of an ancestral gene. We will present case studies from a range of different eukaryotes before discussing the few instances where ancestral reconstructions have been used in plants. As ASR is used to dig into the remote evolutionary past, we will also present some alternative genetic approaches to investigate molecular evolution on shorter timescales. We argue that the study of plant secondary metabolism is particularly well suited for ancestral reconstruction studies. Indeed, its ancient evolutionary roots and highly diverse landscape provide an ideal context in which to address the focal issue around the emergence of evolutionary novelties and how this affects the chemical diversification of plant metabolism.
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Key Words
- APR, ancestral protein resurrection
- ASR, ancestral sequence reconstruction
- Ancestral sequence reconstruction
- CDS, coding sequence
- Evolution
- GR, glucocorticoid receptor
- GWAS, genome wide association study
- Genomics
- InDel, insertion/deletion
- MCMC, Markov Chain Monte Carlo
- ML, maximum likelihood
- MP, maximum parsimony
- MR, mineralcorticoid receptor
- MSA, multiple sequence alignment
- Metabolism
- NJ, neighbor-joining
- Phylogenetics
- Plants
- SFS, site frequency spectrum
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Affiliation(s)
- Federico Scossa
- Max-Planck-Institute of Molecular Plant Physiology (MPI-MP), 14476 Potsdam-Golm, Germany
- Council for Agricultural Research and Economics (CREA), Research Centre for Genomics and Bioinformatics (CREA-GB), Rome, Italy
| | - Alisdair R. Fernie
- Max-Planck-Institute of Molecular Plant Physiology (MPI-MP), 14476 Potsdam-Golm, Germany
- Center of Plant Systems Biology and Biotechnology (CPSBB), Plovdiv, Bulgaria
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5
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Guo L, Hu Z, Zhao C, Xu X, Wang S, Xu J, Dong J, Cai Z. Data Filtering and Its Prioritization in Pipelines for Spatial Segmentation of Mass Spectrometry Imaging. Anal Chem 2021; 93:4788-4793. [PMID: 33683863 DOI: 10.1021/acs.analchem.0c05242] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Mass spectrometry imaging (MSI) could provide vast amounts of data at the temporal-spatial scale in heterogeneous biological specimens, which challenges us to segment accurately suborgans/microregions from complex MSI data. Several pipelines had been proposed for MSI spatial segmentation in the past decade. More importantly, data filtering was found to be an efficient procedure to improve the outcomes of MSI segmentation pipelines. It is not clear, however, how the filtering procedure affects the MSI segmentation. An improved pipeline was established by elaborating the filtering prioritization and filtering algorithm. Lipidomic-characteristic-based MSI data of a whole-body mouse fetus was used to evaluate the established pipeline on localization of the physiological position of suborgans by comparing with three commonly used pipelines and commercial SCiLS Lab software. Two structural measurements were used to quantify the performances of the pipelines including the percentage of abnormal edge pixel (PAEP) and CHAOS. Our results demonstrated that the established pipeline outperformed the other pipelines in visual inspection, spatial consistence, time-cost, and robustness analysis. For example, the dorsal pallium (isocortex) and hippocampal formation (Hpf) regions, midbrain, cerebellum, and brainstem on the mouse brain were annotated and located by the established pipeline. As a generic pipeline, the established pipeline could help with the accurate assessment and screening of drug/chemical-induced targeted organs and exploration of the progression and molecular mechanisms of diseases. The filter-based strategy is expected to become a critical component in the standard operating procedure of MSI data sets.
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Affiliation(s)
- Lei Guo
- National Institute for Data Science in Health and Medicine, Department of Electronic Science, Xiamen University, Xiamen 361005, China
| | - Zhenxing Hu
- National Institute for Data Science in Health and Medicine, Department of Electronic Science, Xiamen University, Xiamen 361005, China
| | - Chao Zhao
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong SAR 999077, China.,Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Xiangnan Xu
- School of Mathematics and Statistics, The University of Sydney, Camperdown Sydney, NSW 2006, Australia
| | - Shujuan Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing 102206, China
| | - Jingjing Xu
- National Institute for Data Science in Health and Medicine, Department of Electronic Science, Xiamen University, Xiamen 361005, China
| | - Jiyang Dong
- National Institute for Data Science in Health and Medicine, Department of Electronic Science, Xiamen University, Xiamen 361005, China
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong SAR 999077, China
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Mehnert M, Ciuffa R, Frommelt F, Uliana F, van Drogen A, Ruminski K, Gstaiger M, Aebersold R. Multi-layered proteomic analyses decode compositional and functional effects of cancer mutations on kinase complexes. Nat Commun 2020; 11:3563. [PMID: 32678104 PMCID: PMC7366679 DOI: 10.1038/s41467-020-17387-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 06/26/2020] [Indexed: 01/09/2023] Open
Abstract
Rapidly increasing availability of genomic data and ensuing identification of disease associated mutations allows for an unbiased insight into genetic drivers of disease development. However, determination of molecular mechanisms by which individual genomic changes affect biochemical processes remains a major challenge. Here, we develop a multilayered proteomic workflow to explore how genetic lesions modulate the proteome and are translated into molecular phenotypes. Using this workflow we determine how expression of a panel of disease-associated mutations in the Dyrk2 protein kinase alter the composition, topology and activity of this kinase complex as well as the phosphoproteomic state of the cell. The data show that altered protein-protein interactions caused by the mutations are associated with topological changes and affected phosphorylation of known cancer driver proteins, thus linking Dyrk2 mutations with cancer-related biochemical processes. Overall, we discover multiple mutation-specific functionally relevant changes, thus highlighting the extensive plasticity of molecular responses to genetic lesions.
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Affiliation(s)
- Martin Mehnert
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Switzerland.
| | - Rodolfo Ciuffa
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Switzerland
| | - Fabian Frommelt
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Switzerland
| | - Federico Uliana
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Switzerland
| | - Audrey van Drogen
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Switzerland
| | - Kilian Ruminski
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Switzerland
- Centre d'Immunologie de Marseille-Luminy, Aix Marseille Université, INSERM, CNRS, Marseille, France
| | - Matthias Gstaiger
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Switzerland.
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Switzerland.
- Faculty of Science, University of Zurich, Zurich, Switzerland.
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7
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Córdoba I, Bielza C, Larrañaga P. A review of Gaussian Markov models for conditional independence. J Stat Plan Inference 2020. [DOI: 10.1016/j.jspi.2019.09.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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8
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Causal Queries from Observational Data in Biological Systems via Bayesian Networks: An Empirical Study in Small Networks. Methods Mol Biol 2018. [PMID: 30547398 DOI: 10.1007/978-1-4939-8882-2_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2023]
Abstract
Biological networks are a very convenient modeling and visualization tool to discover knowledge from modern high-throughput genomics and post-genomics data sets. Indeed, biological entities are not isolated but are components of complex multilevel systems. We go one step further and advocate for the consideration of causal representations of the interactions in living systems. We present the causal formalism and bring it out in the context of biological networks, when the data is observational. We also discuss its ability to decipher the causal information flow as observed in gene expression. We also illustrate our exploration by experiments on small simulated networks as well as on a real biological data set.
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9
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Affiliation(s)
- Hélio Amante Miot
- Universidade Estadual Paulista - UNESP, Faculdade de Medicina de Botucatu, Departamento de Dermatologia e Radioterapia, Botucatu, SP, Brasil
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10
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Ness RO, Sachs K, Mallick P, Vitek O. A Bayesian Active Learning Experimental Design for Inferring Signaling Networks. J Comput Biol 2018; 25:709-725. [PMID: 29927613 DOI: 10.1089/cmb.2017.0247] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Machine learning methods for learning network structure are applied to quantitative proteomics experiments and reverse-engineer intracellular signal transduction networks. They provide insight into the rewiring of signaling within the context of a disease or a phenotype. To learn the causal patterns of influence between proteins in the network, the methods require experiments that include targeted interventions that fix the activity of specific proteins. However, the interventions are costly and add experimental complexity. We describe an active learning strategy for selecting optimal interventions. Our approach takes as inputs pathway databases and historic data sets, expresses them in form of prior probability distributions on network structures, and selects interventions that maximize their expected contribution to structure learning. Evaluations on simulated and real data show that the strategy reduces the detection error of validated edges as compared with an unguided choice of interventions and avoids redundant interventions, thereby increasing the effectiveness of the experiment.
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Affiliation(s)
- Robert O Ness
- 1 Department of Statistics, Purdue University , West Lafayette, Indiana
| | - Karen Sachs
- 2 Department of Immunology, School of Medicine, Stanford University , Palo Alto, California
| | - Parag Mallick
- 3 Canary Center for Cancer Early Detection, School of Medicine, Stanford University , Palo Alto, California
| | - Olga Vitek
- 4 College of Science, College of Computer and Information Science, Northeastern University , Boston, Massachusetts
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11
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Inglese P, McKenzie JS, Mroz A, Kinross J, Veselkov K, Holmes E, Takats Z, Nicholson JK, Glen RC. Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer. Chem Sci 2017; 8:3500-3511. [PMID: 28507724 PMCID: PMC5418631 DOI: 10.1039/c6sc03738k] [Citation(s) in RCA: 100] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Accepted: 02/18/2017] [Indexed: 12/14/2022] Open
Abstract
Visual inspection of tumour tissues does not reveal the complex metabolic changes that differentiate cancer and its sub-types from healthy tissues. Mass spectrometry imaging, which quantifies the underlying chemistry, represents a powerful tool for the molecular exploration of tumour tissues. A 3-dimensional topological description of the chemical properties of the tumour permits the formulation of hypotheses about the biological composition and interactions and the possible causes of its heterogeneous structure. The large amount of information contained in such datasets requires powerful tools for its analysis, visualisation and interpretation. Linear methods for unsupervised dimensionality reduction, such as PCA, are inadequate to capture the complex non-linear relationships present in these data. For this reason, a deep unsupervised neural network based technique, parametric t-SNE, is adopted to map a 3D-DESI-MS dataset from a human colorectal adenocarcinoma biopsy onto a 2-dimensional manifold. This technique allows the identification of clusters not visible with linear methods. The unsupervised clustering of the tumour tissue results in the identification of sub-regions characterised by the abundance of identified metabolites, making possible the formulation of hypotheses to account for their significance and the underlying biological heterogeneity in the tumour.
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Affiliation(s)
- Paolo Inglese
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
| | - James S McKenzie
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
| | - Anna Mroz
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
| | - James Kinross
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
| | - Kirill Veselkov
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
| | - Elaine Holmes
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
| | - Zoltan Takats
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
| | - Jeremy K Nicholson
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
| | - Robert C Glen
- Department of Surgery and Cancer - Division of Computational and Systems Medicine , Imperial College London , London , UK . ; ;
- Centre for Molecular Informatics , Department of Chemistry , University of Cambridge , Cambridge , UK
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12
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Kohlbacher O, Vitek O, Weintraub ST. Challenges in Large-Scale Computational Mass Spectrometry and Multiomics. J Proteome Res 2016; 15:681-2. [DOI: 10.1021/acs.jproteome.6b00067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Oliver Kohlbacher
- Center for Bioinformatics, Quantitative Biology Center,
Department of Computer Science and Faculty of Medicine, University
of Tübingen and Max Planck Institute for Developmental Biology
| | - Olga Vitek
- Sy and Laurie Sternberg Interdisciplinary Associate
Professor, College of Science College of Computer and Information
Science, Northeastern University
| | - Susan T. Weintraub
- Department of Biochemistry, The University of Texas
Health Science Center at San Antonio
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