1
|
Tarkhov AE, Lindstrom-Vautrin T, Zhang S, Ying K, Moqri M, Zhang B, Tyshkovskiy A, Levy O, Gladyshev VN. Nature of epigenetic aging from a single-cell perspective. NATURE AGING 2024; 4:854-870. [PMID: 38724733 DOI: 10.1038/s43587-024-00616-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 03/26/2024] [Indexed: 05/15/2024]
Abstract
Age-related changes in DNA methylation (DNAm) form the basis of the most robust predictors of age-epigenetic clocks-but a clear mechanistic understanding of exactly which aspects of aging are quantified by these clocks is lacking. Here, to clarify the nature of epigenetic aging, we juxtapose the dynamics of tissue and single-cell DNAm in mice. We compare these changes during early development with those observed during adult aging in mice, and corroborate our analyses with a single-cell RNA sequencing analysis within the same multiomics dataset. We show that epigenetic aging involves co-regulated changes as well as a major stochastic component, and this is consistent with transcriptional patterns. We further support the finding of stochastic epigenetic aging by direct tissue and single-cell DNAm analyses and modeling of aging DNAm trajectories with a stochastic process akin to radiocarbon decay. Finally, we describe a single-cell algorithm for the identification of co-regulated and stochastic CpG clusters showing consistent transcriptomic coordination patterns. Together, our analyses increase our understanding of the basis of epigenetic clocks and highlight potential opportunities for targeting aging and evaluating longevity interventions.
Collapse
Affiliation(s)
- Andrei E Tarkhov
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Retro Biosciences Inc., Redwood City, CA, USA.
| | - Thomas Lindstrom-Vautrin
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Sirui Zhang
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Kejun Ying
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Mahdi Moqri
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Obstetrics & Gynecology, Stanford School of Medicine, Stanford University, Stanford, CA, USA
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Bohan Zhang
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alexander Tyshkovskiy
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Orr Levy
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Vadim N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
2
|
Porukala M, Vinod PK. Network-level analysis of ageing and its relationship with diseases and tissue regeneration in the mouse liver. Sci Rep 2023; 13:4632. [PMID: 36944690 PMCID: PMC10030664 DOI: 10.1038/s41598-023-31315-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 03/09/2023] [Indexed: 03/23/2023] Open
Abstract
The liver plays a vital role in maintaining whole-body metabolic homeostasis, compound detoxification and has the unique ability to regenerate itself post-injury. Ageing leads to functional impairment of the liver and predisposes the liver to non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC). Mapping the molecular changes of the liver with ageing may help to understand the crosstalk of ageing with different liver diseases. A systems-level analysis of the ageing-induced liver changes and its crosstalk with liver-associated conditions is lacking. In the present study, we performed network-level analyses of the ageing liver using mouse transcriptomic data and a protein-protein interaction (PPI) network. A sample-wise analysis using network entropy measure was performed, which showed an increasing trend with ageing and helped to identify ageing genes based on local entropy changes. To gain further insights, we also integrated the differentially expressed genes (DEGs) between young and different age groups with the PPI network and identified core modules and nodes associated with ageing. Finally, we computed the network proximity of the ageing network with different networks of liver diseases and regeneration to quantify the effect of ageing. Our analysis revealed the complex interplay of immune, cancer signalling, and metabolic genes in the ageing liver. We found significant network proximities between ageing and NAFLD, HCC, liver damage conditions, and the early phase of liver regeneration with common nodes including NLRP12, TRP53, GSK3B, CTNNB1, MAT1 and FASN. Overall, our study maps the network-level changes of ageing and their interconnections with the physiology and pathology of the liver.
Collapse
Affiliation(s)
- Manisri Porukala
- Centre for Computational Natural Sciences and Bioinformatics, IIIT, Hyderabad, 500032, India
| | - P K Vinod
- Centre for Computational Natural Sciences and Bioinformatics, IIIT, Hyderabad, 500032, India.
| |
Collapse
|
3
|
Dong J, Peng Q, Deng L, Liu J, Huang W, Zhou X, Zhao C, Cai Z. iMS2Net: A multiscale networking methodology to decipher metabolic synergy of organism. iScience 2022; 25:104896. [PMID: 36039290 PMCID: PMC9418851 DOI: 10.1016/j.isci.2022.104896] [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: 05/14/2022] [Revised: 07/04/2022] [Accepted: 08/03/2022] [Indexed: 01/14/2023] Open
Abstract
The metabolic responses of organism to external stimuli are characterized by the multicellular- and multiorgan-based synergistic regulation. Network analysis is a powerful tool to investigate this multiscale interaction. The imaging mass spectrometry (iMS)-based spatial omics provides multidimensional and multiscale information, thus offering the possibility of network analysis to investigate metabolic response of organism to environmental stimuli. We present iMS dataset-sourced multiscale network (iMS2Net) strategy to uncover prenatal environmental pollutant (PM2.5)-induced metabolic responses in the scales of cell and organ from metabolite abundances and metabolite-metabolite interaction using mouse fetal model, including metabotypic similarity, metabolic vulnerability, metabolic co-variability and metabolic diversity within and between organs. Furthermore, network-based analysis results confirm close associations between lipid metabolites and inflammatory cytokine release. This networking methodology elicits particular advantages for modeling the dynamic and adaptive processes of organism under environmental stresses or pathophysiology and provides molecular mechanism to guide the occurrence and development of systemic diseases.
Collapse
Affiliation(s)
- Jiyang Dong
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Qianwen Peng
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Lingli Deng
- Department of Information Engineering, East China University of Technology, China
| | - Jianjun Liu
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Wei Huang
- School of Environment, Guangdong Key Laboratory of Environmental Pollution and Health, Jinan University, China
| | - Xin Zhou
- Bionic Sensing and Intelligence Center, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Chao Zhao
- Bionic Sensing and Intelligence Center, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong SAR, China
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong SAR, China
| |
Collapse
|
4
|
Das P, Shen T, McCord RP. Characterizing the variation in chromosome structure ensembles in the context of the nuclear microenvironment. PLoS Comput Biol 2022; 18:e1010392. [PMID: 35969616 PMCID: PMC9410561 DOI: 10.1371/journal.pcbi.1010392] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 08/25/2022] [Accepted: 07/15/2022] [Indexed: 11/23/2022] Open
Abstract
Inside the nucleus, chromosomes are subjected to direct physical interaction between different components, active forces, and thermal noise, leading to the formation of an ensemble of three-dimensional structures. However, it is still not well understood to what extent and how the structural ensemble varies from one chromosome region or cell-type to another. We designed a statistical analysis technique and applied it to single-cell chromosome imaging data to reveal the heterogeneity of individual chromosome structures. By analyzing the resulting structural landscape, we find that the largest dynamic variation is the overall radius of gyration of the chromatin region, followed by domain reorganization within the region. By comparing different human cell-lines and experimental perturbation data using this statistical analysis technique and a network-based similarity quantification approach, we identify both cell-type and condition-specific features of the structural landscapes. We identify a relationship between epigenetic state and the properties of chromosome structure fluctuation and validate this relationship through polymer simulations. Overall, our study suggests that the types of variation in a chromosome structure ensemble are cell-type as well as region-specific and can be attributed to constraints placed on the structure by factors such as variation in epigenetic state. Recent work has revealed principles of how chromosomes are folded and structured inside the human nucleus. It is now even possible to microscopically trace the path of chromosomes in 3D in individual cells. With this data, we can start to examine how much variation exists in chromosome structure and what biological factors may restrict or enhance this variation. Are chromosomes stuck in just a few possible positions or do they move around more freely, sampling many configurations? Here, we use a mathematical approach to compare chromosome structure variation in different cell types, at different locations along the genome, and when key structural proteins are removed. Through these comparisons and dynamic simulations of chromosome behavior, we identify factors that may constrain or promote variation in chromosome structure.
Collapse
Affiliation(s)
- Priyojit Das
- UT-ORNL Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, Tennessee, United States of America
| | - Tongye Shen
- UT-ORNL Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, Tennessee, United States of America
- Biochemistry & Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee, United States of America
| | - Rachel Patton McCord
- UT-ORNL Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, Tennessee, United States of America
- Biochemistry & Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee, United States of America
- * E-mail:
| |
Collapse
|
5
|
Clemens Z, Sivakumar S, Pius A, Sahu A, Shinde S, Mamiya H, Luketich N, Cui J, Dixit P, Hoeck JD, Kreuz S, Franti M, Barchowsky A, Ambrosio F. The biphasic and age-dependent impact of klotho on hallmarks of aging and skeletal muscle function. eLife 2021; 10:e61138. [PMID: 33876724 PMCID: PMC8118657 DOI: 10.7554/elife.61138] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 04/06/2021] [Indexed: 12/15/2022] Open
Abstract
Aging is accompanied by disrupted information flow, resulting from accumulation of molecular mistakes. These mistakes ultimately give rise to debilitating disorders including skeletal muscle wasting, or sarcopenia. To derive a global metric of growing 'disorderliness' of aging muscle, we employed a statistical physics approach to estimate the state parameter, entropy, as a function of genes associated with hallmarks of aging. Escalating network entropy reached an inflection point at old age, while structural and functional alterations progressed into oldest-old age. To probe the potential for restoration of molecular 'order' and reversal of the sarcopenic phenotype, we systemically overexpressed the longevity protein, Klotho, via AAV. Klotho overexpression modulated genes representing all hallmarks of aging in old and oldest-old mice, but pathway enrichment revealed directions of changes were, for many genes, age-dependent. Functional improvements were also age-dependent. Klotho improved strength in old mice, but failed to induce benefits beyond the entropic tipping point.
Collapse
Affiliation(s)
- Zachary Clemens
- Department of Physical Medicine & Rehabilitation, University of PittsburghPittsburghUnited States
- Department of Environmental and Occupational Health, University of PittsburghPittsburghUnited States
| | - Sruthi Sivakumar
- Department of Physical Medicine & Rehabilitation, University of PittsburghPittsburghUnited States
- Department of Bioengineering, University of PittsburghPittsburghUnited States
| | - Abish Pius
- Department of Physical Medicine & Rehabilitation, University of PittsburghPittsburghUnited States
- Department of Computational & Systems Biology, School of Medicine, University of PittsburghPittsburghUnited States
| | - Amrita Sahu
- Department of Physical Medicine & Rehabilitation, University of PittsburghPittsburghUnited States
| | - Sunita Shinde
- Department of Physical Medicine & Rehabilitation, University of PittsburghPittsburghUnited States
| | - Hikaru Mamiya
- Department of Bioengineering, University of PittsburghPittsburghUnited States
| | - Nathaniel Luketich
- Department of Bioengineering, University of PittsburghPittsburghUnited States
| | - Jian Cui
- Department of Computational & Systems Biology, School of Medicine, University of PittsburghPittsburghUnited States
| | - Purushottam Dixit
- Department of Physics, University of FloridaGainesvilleUnited States
| | - Joerg D Hoeck
- Department of Research Beyond Borders, Regenerative Medicine, Boehringer Ingelheim Pharmaceuticals, IncRheinGermany
| | - Sebastian Kreuz
- Department of Research Beyond Borders, Regenerative Medicine, Boehringer Ingelheim Pharmaceuticals, IncRheinGermany
| | - Michael Franti
- Department of Research Beyond Borders, Regenerative Medicine, Boehringer Ingelheim Pharmaceuticals, IncRheinGermany
| | - Aaron Barchowsky
- Department of Environmental and Occupational Health, University of PittsburghPittsburghUnited States
| | - Fabrisia Ambrosio
- Department of Physical Medicine & Rehabilitation, University of PittsburghPittsburghUnited States
- Department of Environmental and Occupational Health, University of PittsburghPittsburghUnited States
- Department of Bioengineering, University of PittsburghPittsburghUnited States
- McGowan Institute for Regenerative Medicine, University of PittsburghPittsburghUnited States
| |
Collapse
|
6
|
Rosato A, Tenori L, Cascante M, De Atauri Carulla PR, Martins Dos Santos VAP, Saccenti E. From correlation to causation: analysis of metabolomics data using systems biology approaches. Metabolomics 2018; 14:37. [PMID: 29503602 PMCID: PMC5829120 DOI: 10.1007/s11306-018-1335-y] [Citation(s) in RCA: 123] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Accepted: 01/31/2018] [Indexed: 12/26/2022]
Abstract
INTRODUCTION Metabolomics is a well-established tool in systems biology, especially in the top-down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches. OBJECTIVES This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods. METHODS We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis. RESULTS We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner. CONCLUSIONS Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.
Collapse
Affiliation(s)
- Antonio Rosato
- Magnetic Resonance Center and Department of Chemistry "Ugo Schiff", University of Florence, Florence, Italy.
| | - Leonardo Tenori
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Marta Cascante
- CIBER de Enfermedades hepáticas y digestivas (CIBERHD, Madrid) and Department of Biochemistry and Molecular Biomedicine, Universitat de Barcelona, Barcelona, Spain
| | - Pedro Ramon De Atauri Carulla
- CIBER de Enfermedades hepáticas y digestivas (CIBERHD, Madrid) and Department of Biochemistry and Molecular Biomedicine, Universitat de Barcelona, Barcelona, Spain
| | - Vitor A P Martins Dos Santos
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, The Netherlands
- LifeGlimmer GmbH, Berlin, Germany
| | - Edoardo Saccenti
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, The Netherlands.
| |
Collapse
|
7
|
Sigston EAW, Williams BRG. An Emergence Framework of Carcinogenesis. Front Oncol 2017; 7:198. [PMID: 28959682 PMCID: PMC5603758 DOI: 10.3389/fonc.2017.00198] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2017] [Accepted: 08/17/2017] [Indexed: 11/13/2022] Open
Abstract
Experimental paradigms provide the framework for the understanding of cancer, and drive research and treatment, but are rarely considered by clinicians. The somatic mutation theory (SMT), in which cancer is considered a genetic disease, has been the predominant traditional model of cancer for over 50 years. More recently, alternative theories have been proposed, such as tissue organization field theory (TOFT), evolutionary models, and inflammatory models. Key concepts within the various models have led to them being difficult to reconcile. Progressively, it has been recognized that biological systems cannot be fully explained by the physicochemical properties of their constituent parts. There is an increasing call for a 'systems' approach. Incorporating the concepts of 'emergence', 'systems', 'thermodynamics', and 'chaos', a single integrated framework for carcinogenesis has been developed, enabling existing theories to become compatible as alternative mechanisms, facilitating the integration of bioinformatics and providing a structure in which translational research can flow from both 'benchtop to bedside' and 'bedside to benchtop'. In this review, a basic understanding of the key concepts of 'emergence', 'systems', 'system levels', 'complexity', 'thermodynamics', 'entropy', 'chaos', and 'fractals' is provided. Non-linear mathematical equations are included where possible to demonstrate compatibility with bioinformatics. Twelve principles that define the 'emergence framework of carcinogenesis' are developed, with principles 1-10 encapsulating the key concepts upon which the framework is built and their application to carcinogenesis. Principle 11 relates the framework to cancer progression. Principle 12 relates to the application of the framework to translational research. The 'emergence framework of carcinogenesis' collates current paradigms, concepts, and evidence around carcinogenesis into a single framework that incorporates previously incompatible viewpoints and ideas. Any researcher, scientist, or clinician involved in research, treatment, or prevention of cancer can employ this framework.
Collapse
Affiliation(s)
- Elizabeth A W Sigston
- Department of Otorhinolaryngology, Head & Neck Surgery, Monash Health, Melbourne, VIC, Australia.,Department of Surgery, Monash Medical Centre, Monash University, Melbourne, VIC, Australia.,Hudson Institute of Medical Research, Melbourne, VIC, Australia
| | - Bryan R G Williams
- Hudson Institute of Medical Research, Melbourne, VIC, Australia.,Department of Molecular and Translational Science, Monash University, Melbourne, VIC, Australia
| |
Collapse
|
8
|
Monti D, Ostan R, Borelli V, Castellani G, Franceschi C. Inflammaging and human longevity in the omics era. Mech Ageing Dev 2016; 165:129-138. [PMID: 28038993 DOI: 10.1016/j.mad.2016.12.008] [Citation(s) in RCA: 106] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Accepted: 12/21/2016] [Indexed: 11/24/2022]
Abstract
Inflammaging is a recent theory of aging originally proposed in 2000 where data and conceptualizations regarding the aging of the immune system (immunosenescence) and the evolution of immune responses from invertebrates to mammals converged. This theory has received an increasing number of citations and experimental confirmations. Here we present an updated version of inflammaging focused on omics data - particularly on glycomics - collected on centenarians, semi-supercentenarians and their offspring. Accordingly, we arrived to the following conclusions: i) inflammaging has a structure where specific combinations of pro- and anti-inflammatory mediators are involved; ii) inflammaging is systemic and more complex than we previously thought, as many organs, tissues and cell types participate in producing pro- and anti-inflammatory stimuli defined "molecular garbage"; iii) inflammaging is dynamic, can be propagated locally to neighboring cells and systemically from organ to organ by circulating products and microvesicles, and amplified by chronic age-related diseases constituting a "local fire", which in turn produces additional inflammatory stimuli and molecular garbage; iv) an integrated Systems Medicine approach is urgently needed to let emerge a robust and highly informative set/combination of omics markers able to better grasp the complex molecular core of inflammaging in elderly and centenarians.
Collapse
Affiliation(s)
- Daniela Monti
- Department of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, Viale Morgagni 50, 50134 Florence, Italy
| | - Rita Ostan
- Interdepartmental Centre "L. Galvani" (CIG) and Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Via San Giacomo 12, 40126 Bologna, Italy.
| | - Vincenzo Borelli
- Interdepartmental Centre "L. Galvani" (CIG) and Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Via San Giacomo 12, 40126 Bologna, Italy
| | - Gastone Castellani
- Department of Physics and Astronomy DIFA, University of Bologna, Viale Berti Pichat 6/2, 40127, Bologna, Italy
| | - Claudio Franceschi
- IRCCS, Institute of Neurological Sciences of Bologna, Via Altura 3, 40139 Bologna, Italy
| |
Collapse
|
9
|
Park Y, Lim S, Nam JW, Kim S. Measuring intratumor heterogeneity by network entropy using RNA-seq data. Sci Rep 2016; 6:37767. [PMID: 27883053 PMCID: PMC5121893 DOI: 10.1038/srep37767] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Accepted: 10/31/2016] [Indexed: 12/27/2022] Open
Abstract
Intratumor heterogeneity (ITH) is observed at different stages of tumor progression, metastasis and reouccurence, which can be important for clinical applications. We used RNA-sequencing data from tumor samples, and measured the level of ITH in terms of biological network states. To model complex relationships among genes, we used a protein interaction network to consider gene-gene dependency. ITH was measured by using an entropy-based distance metric between two networks, nJSD, with Jensen-Shannon Divergence (JSD). With nJSD, we defined transcriptome-based ITH (tITH). The effectiveness of tITH was extensively tested for the issues related with ITH using real biological data sets. Human cancer cell line data and single-cell sequencing data were investigated to verify our approach. Then, we analyzed TCGA pan-cancer 6,320 patients. Our result was in agreement with widely used genome-based ITH inference methods, while showed better performance at survival analysis. Analysis of mouse clonal evolution data further confirmed that our transcriptome-based ITH was consistent with genetic heterogeneity at different clonal evolution stages. Additionally, we found that cell cycle related pathways have significant contribution to increasing heterogeneity on the network during clonal evolution. We believe that the proposed transcriptome-based ITH is useful to characterize heterogeneity of a tumor sample at RNA level.
Collapse
Affiliation(s)
- Youngjune Park
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-742, Korea
| | - Sangsoo Lim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-742, Korea
| | - Jin-Wu Nam
- Department of Life Science, College of Natural Sciences, Hanyang University, Seoul, 133-791, Korea
- Research Institute for Natural Sciences, Hanyang University, Seoul, 133-791, Korea
| | - Sun Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-742, Korea
- Department of Computer Science and Engineering, Seoul National University, Seoul, 151-742, Korea
- Bioinformatics Institute, Seoul National University, Seoul, 151-742, Korea
| |
Collapse
|
10
|
Saccenti E, Menichetti G, Ghini V, Remondini D, Tenori L, Luchinat C. Entropy-Based Network Representation of the Individual Metabolic Phenotype. J Proteome Res 2016; 15:3298-307. [DOI: 10.1021/acs.jproteome.6b00454] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Affiliation(s)
- Edoardo Saccenti
- Laboratory
of Systems and Synthetic Biology, Wageningen University, Stippeneng
4, 6708 WE Wageningen, The Netherlands
| | - Giulia Menichetti
- Department
of Physics and Astronomy and INFN Sez. Bologna, University of Bologna, Viale Berti Pichat 6/2, 40127 Bologna, Italy
| | - Veronica Ghini
- Magnetic
Resonance Center, University of Florence, via Luigi Sacconi 6, 59100 Sesto Fiorentino, Italy
| | - Daniel Remondini
- Department
of Physics and Astronomy and INFN Sez. Bologna, University of Bologna, Viale Berti Pichat 6/2, 40127 Bologna, Italy
| | - Leonardo Tenori
- Magnetic
Resonance Center, University of Florence, via Luigi Sacconi 6, 59100 Sesto Fiorentino, Italy
- Department
of Experimental and Clinical Medicine, University of Florence, Largo Brambilla
3, 50134 Florence, Italy
| | - Claudio Luchinat
- Magnetic
Resonance Center, University of Florence, via Luigi Sacconi 6, 59100 Sesto Fiorentino, Italy
- Department
of Chemistry, University of Florence, via della Lastruccia 3, 50019 Sesto Fiorentino, Italy
| |
Collapse
|
11
|
Network measures for protein folding state discrimination. Sci Rep 2016; 6:30367. [PMID: 27464796 PMCID: PMC4964642 DOI: 10.1038/srep30367] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2016] [Accepted: 06/24/2016] [Indexed: 11/09/2022] Open
Abstract
Proteins fold using a two-state or multi-state kinetic mechanisms, but up to now there is not a first-principle model to explain this different behavior. We exploit the network properties of protein structures by introducing novel observables to address the problem of classifying the different types of folding kinetics. These observables display a plain physical meaning, in terms of vibrational modes, possible configurations compatible with the native protein structure, and folding cooperativity. The relevance of these observables is supported by a classification performance up to 90%, even with simple classifiers such as discriminant analysis.
Collapse
|
12
|
Castellani GC, Menichetti G, Garagnani P, Giulia Bacalini M, Pirazzini C, Franceschi C, Collino S, Sala C, Remondini D, Giampieri E, Mosca E, Bersanelli M, Vitali S, Valle IFD, Liò P, Milanesi L. Systems medicine of inflammaging. Brief Bioinform 2016; 17:527-40. [PMID: 26307062 PMCID: PMC4870395 DOI: 10.1093/bib/bbv062] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2015] [Revised: 06/29/2015] [Indexed: 12/30/2022] Open
Abstract
Systems Medicine (SM) can be defined as an extension of Systems Biology (SB) to Clinical-Epidemiological disciplines through a shifting paradigm, starting from a cellular, toward a patient centered framework. According to this vision, the three pillars of SM are Biomedical hypotheses, experimental data, mainly achieved by Omics technologies and tailored computational, statistical and modeling tools. The three SM pillars are highly interconnected, and their balancing is crucial. Despite the great technological progresses producing huge amount of data (Big Data) and impressive computational facilities, the Bio-Medical hypotheses are still of primary importance. A paradigmatic example of unifying Bio-Medical theory is the concept of Inflammaging. This complex phenotype is involved in a large number of pathologies and patho-physiological processes such as aging, age-related diseases and cancer, all sharing a common inflammatory pathogenesis. This Biomedical hypothesis can be mapped into an ecological perspective capable to describe by quantitative and predictive models some experimentally observed features, such as microenvironment, niche partitioning and phenotype propagation. In this article we show how this idea can be supported by computational methods useful to successfully integrate, analyze and model large data sets, combining cross-sectional and longitudinal information on clinical, environmental and omics data of healthy subjects and patients to provide new multidimensional biomarkers capable of distinguishing between different pathological conditions, e.g. healthy versus unhealthy state, physiological versus pathological aging.
Collapse
|
13
|
Giampieri E, Remondini D, Bacalini MG, Garagnani P, Pirazzini C, Yani SL, Giuliani C, Menichetti G, Zironi I, Sala C, Capri M, Franceschi C, Bürkle A, Castellani G. Statistical strategies and stochastic predictive models for the MARK-AGE data. Mech Ageing Dev 2015. [PMID: 26209580 DOI: 10.1016/j.mad.2015.07.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
MARK-AGE aims at the identification of biomarkers of human aging capable of discriminating between the chronological age and the effective functional status of the organism. To achieve this, given the structure of the collected data, a proper statistical analysis has to be performed, as the structure of the data are non trivial and the number of features under study is near to the number of subjects used, requiring special care to avoid overfitting. Here we described some of the possible strategies suitable for this analysis. We also include a description of the main techniques used, to explain and justify the selected strategies. Among other possibilities, we suggest to model and analyze the data with a three step strategy.
Collapse
Affiliation(s)
- Enrico Giampieri
- Interdepartmental Center Galvani "CIG", Via Selmi, 3 University of Bologna, Bologna, Italy; Physics and Astronomy Department, Viale Berti Pichat 6/2, University of Bologna, Bologna, Italy.
| | - Daniel Remondini
- Interdepartmental Center Galvani "CIG", Via Selmi, 3 University of Bologna, Bologna, Italy; Physics and Astronomy Department, Viale Berti Pichat 6/2, University of Bologna, Bologna, Italy
| | - Maria Giulia Bacalini
- Department of Experimental, Diagnostic and Specialty Medicine, Via S. Giacomo, 12 University of Bologna, Bologna, Italy; Interdepartmental Center Galvani "CIG", Via Selmi, 3 University of Bologna, Bologna, Italy
| | - Paolo Garagnani
- Department of Experimental, Diagnostic and Specialty Medicine, Via S. Giacomo, 12 University of Bologna, Bologna, Italy; Interdepartmental Center Galvani "CIG", Via Selmi, 3 University of Bologna, Bologna, Italy
| | - Chiara Pirazzini
- Department of Experimental, Diagnostic and Specialty Medicine, Via S. Giacomo, 12 University of Bologna, Bologna, Italy; Interdepartmental Center Galvani "CIG", Via Selmi, 3 University of Bologna, Bologna, Italy
| | - Stella Lukas Yani
- Department of Experimental, Diagnostic and Specialty Medicine, Via S. Giacomo, 12 University of Bologna, Bologna, Italy; Interdepartmental Center Galvani "CIG", Via Selmi, 3 University of Bologna, Bologna, Italy; Institute for Biomedical Aging Research, University of Innsbruck, Austria
| | - Cristina Giuliani
- Department of Experimental, Diagnostic and Specialty Medicine, Via S. Giacomo, 12 University of Bologna, Bologna, Italy; Interdepartmental Center Galvani "CIG", Via Selmi, 3 University of Bologna, Bologna, Italy
| | - Giulia Menichetti
- Interdepartmental Center Galvani "CIG", Via Selmi, 3 University of Bologna, Bologna, Italy; Physics and Astronomy Department, Viale Berti Pichat 6/2, University of Bologna, Bologna, Italy
| | - Isabella Zironi
- Interdepartmental Center Galvani "CIG", Via Selmi, 3 University of Bologna, Bologna, Italy; Physics and Astronomy Department, Viale Berti Pichat 6/2, University of Bologna, Bologna, Italy
| | - Claudia Sala
- Interdepartmental Center Galvani "CIG", Via Selmi, 3 University of Bologna, Bologna, Italy; Physics and Astronomy Department, Viale Berti Pichat 6/2, University of Bologna, Bologna, Italy
| | - Miriam Capri
- Department of Experimental, Diagnostic and Specialty Medicine, Via S. Giacomo, 12 University of Bologna, Bologna, Italy; Interdepartmental Center Galvani "CIG", Via Selmi, 3 University of Bologna, Bologna, Italy
| | - Claudio Franceschi
- Department of Experimental, Diagnostic and Specialty Medicine, Via S. Giacomo, 12 University of Bologna, Bologna, Italy; Interdepartmental Center Galvani "CIG", Via Selmi, 3 University of Bologna, Bologna, Italy
| | - Alexander Bürkle
- Molecular Toxicology Group, Department of Biology, University of Konstanz, 78457 Konstanz, Germany
| | - Gastone Castellani
- Interdepartmental Center Galvani "CIG", Via Selmi, 3 University of Bologna, Bologna, Italy; Physics and Astronomy Department, Viale Berti Pichat 6/2, University of Bologna, Bologna, Italy
| |
Collapse
|