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Systems Biology of Ageing. Subcell Biochem 2023; 102:415-424. [PMID: 36600142 DOI: 10.1007/978-3-031-21410-3_16] [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: 01/06/2023]
Abstract
The ageing process is highly complex involving multiple processes operating at different biological levels. Systems Biology presents an approach using integrative computational and laboratory study that allows us to address such complexity. The approach relies on the computational analysis of knowledge and data to generate predictive models that may be validated with further laboratory experimentation. Our understanding of ageing is such that translational opportunities are within reach and systems biology offers a means to ensure that optimal decisions are made. We present an overview of the methods employed from bioinformatics and computational modelling and describe some of the insights into ageing that have been gained.
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Vega Magdaleno GD, Bespalov V, Zheng Y, Freitas AA, de Magalhaes JP. Machine learning-based predictions of dietary restriction associations across ageing-related genes. BMC Bioinformatics 2022; 23:10. [PMID: 34983372 PMCID: PMC8729156 DOI: 10.1186/s12859-021-04523-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 12/08/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Dietary restriction (DR) is the most studied pro-longevity intervention; however, a complete understanding of its underlying mechanisms remains elusive, and new research directions may emerge from the identification of novel DR-related genes and DR-related genetic features. RESULTS This work used a Machine Learning (ML) approach to classify ageing-related genes as DR-related or NotDR-related using 9 different types of predictive features: PathDIP pathways, two types of features based on KEGG pathways, two types of Protein-Protein Interactions (PPI) features, Gene Ontology (GO) terms, Genotype Tissue Expression (GTEx) expression features, GeneFriends co-expression features and protein sequence descriptors. Our findings suggested that features biased towards curated knowledge (i.e. GO terms and biological pathways), had the greatest predictive power, while unbiased features (mainly gene expression and co-expression data) have the least predictive power. Moreover, a combination of all the feature types diminished the predictive power compared to predictions based on curated knowledge. Feature importance analysis on the two most predictive classifiers mostly corroborated existing knowledge and supported recent findings linking DR to the Nuclear Factor Erythroid 2-Related Factor 2 (NRF2) signalling pathway and G protein-coupled receptors (GPCR). We then used the two strongest combinations of feature type and ML algorithm to predict DR-relatedness among ageing-related genes currently lacking DR-related annotations in the data, resulting in a set of promising candidate DR-related genes (GOT2, GOT1, TSC1, CTH, GCLM, IRS2 and SESN2) whose predicted DR-relatedness remain to be validated in future wet-lab experiments. CONCLUSIONS This work demonstrated the strong potential of ML-based techniques to identify DR-associated features as our findings are consistent with literature and recent discoveries. Although the inference of new DR-related mechanistic findings based solely on GO terms and biological pathways was limited due to their knowledge-driven nature, the predictive power of these two features types remained useful as it allowed inferring new promising candidate DR-related genes.
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Affiliation(s)
- Gustavo Daniel Vega Magdaleno
- Integrative Genomics of Ageing Group, Institute of Life Course and Medical Sciences, University of Liverpool, 6 West Derby St, Liverpool, L7 8TX, UK
| | - Vladislav Bespalov
- School of Computer Technologies and Controls, ITMO University, Kronverkskiy Prospekt 49, 197101, St Petersburg, Russia
| | - Yalin Zheng
- Department of Eye and Vision Science, Institute of Life Course and Medical Sciences, University of Liverpool, 6 West Derby St, Liverpool, L7 8TX, UK
| | - Alex A Freitas
- School of Computing, University of Kent, Canterbury, CT2 7NF, UK
| | - Joao Pedro de Magalhaes
- Integrative Genomics of Ageing Group, Institute of Life Course and Medical Sciences, University of Liverpool, 6 West Derby St, Liverpool, L7 8TX, UK.
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3
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da Silva PN, Plastino A, Fabris F, Freitas AA. A Novel Feature Selection Method for Uncertain Features: An Application to the Prediction of Pro-/Anti-Longevity Genes. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2230-2238. [PMID: 32324561 DOI: 10.1109/tcbb.2020.2988450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Understanding the ageing process is a very challenging problem for biologists. To help in this task, there has been a growing use of classification methods (from machine learning) to learn models that predict whether a gene influences the process of ageing or promotes longevity. One type of predictive feature often used for learning such classification models is Protein-Protein Interaction (PPI) features. One important property of PPI features is their uncertainty, i.e., a given feature (PPI annotation) is often associated with a confidence score, which is usually ignored by conventional classification methods. Hence, we propose the Lazy Feature Selection for Uncertain Features (LFSUF) method, which is tailored for coping with the uncertainty in PPI confidence scores. In addition, following the lazy learning paradigm, LFSUF selects features for each instance to be classified, making the feature selection process more flexible. We show that our LFSUF method achieves better predictive accuracy when compared to other feature selection methods that either do not explicitly take PPI confidence scores into account or deal with uncertainty globally rather than using a per-instance approach. Also, we interpret the results of the classification process using the features selected by LFSUF, showing that the number of selected features is significantly reduced, assisting the interpretability of the results. The datasets used in the experiments and the program code of the LFSUF method are freely available on the web at http://github.com/pablonsilva/FSforUncertainFeatureSpaces.
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4
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Fang EF, Xie C, Schenkel JA, Wu C, Long Q, Cui H, Aman Y, Frank J, Liao J, Zou H, Wang NY, Wu J, Liu X, Li T, Fang Y, Niu Z, Yang G, Hong J, Wang Q, Chen G, Li J, Chen HZ, Kang L, Su H, Gilmour BC, Zhu X, Jiang H, He N, Tao J, Leng SX, Tong T, Woo J. A research agenda for ageing in China in the 21st century (2nd edition): Focusing on basic and translational research, long-term care, policy and social networks. Ageing Res Rev 2020; 64:101174. [PMID: 32971255 PMCID: PMC7505078 DOI: 10.1016/j.arr.2020.101174] [Citation(s) in RCA: 231] [Impact Index Per Article: 57.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 08/13/2020] [Accepted: 09/03/2020] [Indexed: 12/18/2022]
Abstract
One of the key issues facing public healthcare is the global trend of an increasingly ageing society which continues to present policy makers and caregivers with formidable healthcare and socio-economic challenges. Ageing is the primary contributor to a broad spectrum of chronic disorders all associated with a lower quality of life in the elderly. In 2019, the Chinese population constituted 18 % of the world population, with 164.5 million Chinese citizens aged 65 and above (65+), and 26 million aged 80 or above (80+). China has become an ageing society, and as it continues to age it will continue to exacerbate the burden borne by current family and public healthcare systems. Major healthcare challenges involved with caring for the elderly in China include the management of chronic non-communicable diseases (CNCDs), physical frailty, neurodegenerative diseases, cardiovascular diseases, with emerging challenges such as providing sufficient dental care, combating the rising prevalence of sexually transmitted diseases among nursing home communities, providing support for increased incidences of immune diseases, and the growing necessity to provide palliative care for the elderly. At the governmental level, it is necessary to make long-term strategic plans to respond to the pressures of an ageing society, especially to establish a nationwide, affordable, annual health check system to facilitate early diagnosis and provide access to affordable treatments. China has begun work on several activities to address these issues including the recent completion of the of the Ten-year Health-Care Reform project, the implementation of the Healthy China 2030 Action Plan, and the opening of the National Clinical Research Center for Geriatric Disorders. There are also societal challenges, namely the shift from an extended family system in which the younger provide home care for their elderly family members, to the current trend in which young people are increasingly migrating towards major cities for work, increasing reliance on nursing homes to compensate, especially following the outcomes of the 'one child policy' and the 'empty-nest elderly' phenomenon. At the individual level, it is important to provide avenues for people to seek and improve their own knowledge of health and disease, to encourage them to seek medical check-ups to prevent/manage illness, and to find ways to promote modifiable health-related behaviors (social activity, exercise, healthy diets, reasonable diet supplements) to enable healthier, happier, longer, and more productive lives in the elderly. Finally, at the technological or treatment level, there is a focus on modern technologies to counteract the negative effects of ageing. Researchers are striving to produce drugs that can mimic the effects of 'exercising more, eating less', while other anti-ageing molecules from molecular gerontologists could help to improve 'healthspan' in the elderly. Machine learning, 'Big Data', and other novel technologies can also be used to monitor disease patterns at the population level and may be used to inform policy design in the future. Collectively, synergies across disciplines on policies, geriatric care, drug development, personal awareness, the use of big data, machine learning and personalized medicine will transform China into a country that enables the most for its elderly, maximizing and celebrating their longevity in the coming decades. This is the 2nd edition of the review paper (Fang EF et al., Ageing Re. Rev. 2015).
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Affiliation(s)
- Evandro F Fang
- Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, 1478 Lørenskog, Norway; The Norwegian Centre on Healthy Ageing (NO-Age), Oslo, Norway; Department of Hypertension and Vascular Disease, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, China; Institute of Geriatric Immunology, School of Medicine, Jinan University, 510632, Guangzhou, China; Department of Geriatrics, The First Affiliated Hospital, Zhengzhou University, 450052, Zhengzhou, China.
| | - Chenglong Xie
- Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, 1478 Lørenskog, Norway; Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
| | - Joseph A Schenkel
- Durham University Department of Sports and Exercise Sciences, Durham, United Kingdom.
| | - Chenkai Wu
- Global Health Research Center, Duke Kunshan University, 215316, Kunshan, China; Duke Global Health Institute, Duke University, Durham, 27710, North Carolina, USA.
| | - Qian Long
- Global Health Research Center, Duke Kunshan University, 215316, Kunshan, China.
| | - Honghua Cui
- Department of Endodontics, Shanghai Stomatological Hospital, Fudan University, China; Oral Biomedical Engineering Laboratory, Shanghai Stomatological Hospital, Fudan University, China.
| | - Yahyah Aman
- Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, 1478 Lørenskog, Norway.
| | - Johannes Frank
- Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, 1478 Lørenskog, Norway.
| | - Jing Liao
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, 510275, Guangzhou, China; Sun Yat-sen Global Health Institute, Institute of State Governance, Sun Yat-sen University, 510275, Guangzhou, China.
| | - Huachun Zou
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China; Kirby Institute, University of New South Wales, Sydney, Australia.
| | - Ninie Y Wang
- Pinetree Care Group, 515 Tower A, Guomen Plaza, Chaoyang District, 100028, Beijing, China.
| | - Jing Wu
- Department of Sociology and Work Science, University of Gothenburg, SE-405 30, Gothenburg, Sweden.
| | - Xiaoting Liu
- School of Public Affairs, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
| | - Tao Li
- BGI-Shenzhen, Beishan Industrial Zone, 518083, Shenzhen, China; China National GeneBank, BGI-Shenzhen, 518120, Shenzhen, China.
| | - Yuan Fang
- Department of Public Health, Erasmus University Medical Centre, Rotterdam, the Netherlands.
| | - Zhangming Niu
- Aladdin Healthcare Technologies Ltd., 25 City Rd, Shoreditch, London EC1Y 1AA, UK.
| | - Guang Yang
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW3 6NP, UK; and National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, United Kingdom.
| | | | - Qian Wang
- Department of Geriatrics, The First Affiliated Hospital, Zhengzhou University, 450052, Zhengzhou, China.
| | - Guobing Chen
- Institute of Geriatric Immunology, School of Medicine, Jinan University, 510632, Guangzhou, China.
| | - Jun Li
- Department of Biochemistry and Molecular Biology, The Institute of Basic Medical Sciences, The Chinese Academy of Medical Sciences (CAMS)& Peking Union Medical University (PUMC), 5 Dondan Santiao Road, Beijing, 100730, China.
| | - Hou-Zao Chen
- Department of Biochemistry and Molecular Biology, The Institute of Basic Medical Sciences, The Chinese Academy of Medical Sciences (CAMS)& Peking Union Medical University (PUMC), 5 Dondan Santiao Road, Beijing, 100730, China.
| | - Lin Kang
- Department of Geriatrics, Peking Union Medical College Hospital, Beijing, 100730, China.
| | - Huanxing Su
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao.
| | - Brian C Gilmour
- The Norwegian Centre on Healthy Ageing (NO-Age), Oslo, Norway.
| | - Xinqiang Zhu
- Department of Toxicology, Zhejiang University School of Public Health, Hangzhou, 310058, Zhejiang, China; The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, 322000, Zhejiang, China.
| | - Hong Jiang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China; Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, Hunan, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
| | - Na He
- School of Public Health, Fudan University, 200032, Shanghai, China; Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, 200032, Shanghai, China; Key Laboratory of Health Technology Assessment of Ministry of Health, Fudan University, 200032, Shanghai, China.
| | - Jun Tao
- Department of Hypertension and Vascular Disease, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, China.
| | - Sean Xiao Leng
- Division of Geriatric Medicine and Gerontology, Johns Hopkins University School of Medicine, 5505 Hopkins Bayview Circle, Baltimore, MD 21224, USA.
| | - Tanjun Tong
- Research Center on Ageing, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing Key Laboratory of Protein Posttranslational Modifications and Cell Function, Beijing, China.
| | - Jean Woo
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.
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Dodig S, Čepelak I, Pavić I. Hallmarks of senescence and aging. Biochem Med (Zagreb) 2019; 29:030501. [PMID: 31379458 PMCID: PMC6610675 DOI: 10.11613/bm.2019.030501] [Citation(s) in RCA: 172] [Impact Index Per Article: 34.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Accepted: 06/10/2019] [Indexed: 12/12/2022] Open
Abstract
The complex process of biological aging, as an intrinsic feature of living beings, is the result of genetic and, to a greater extent, environmental factors and time. For many of the changes taking place in the body during aging, three factors are important: inflammation, immune aging and senescence (cellular aging, biological aging). Senescence is an irreversible form of long-term cell-cycle arrest, caused by excessive intracellular or extracellular stress or damage. The purpose of this cell-cycles arrest is to limit the proliferation of damaged cells, to eliminate accumulated harmful factors and to disable potential malignant cell transformation. As the biological age does not have to be in accordance with the chronological age, it is important to find specific hallmarks and biomarkers that could objectively determine the rate of age of a person. These biomarkers might be a valuable measure of physiological, i.e. biological age. Biomarkers should meet several criteria. For example, they have to predict the rate of aging, monitor a basic process that underlies the aging process, be able to be tested repeatedly without harming the person. In addition, biomarkers have to be indicators of biological processes, pathogenic processes or pharmacological responses to therapeutic intervention. It is considered that the telomere length is the weak biomarker (with poor predictive accuracy), and there is currently no reliable biomarker that meets all the necessary criteria.
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Affiliation(s)
- Slavica Dodig
- Department of Medical Biochemistry and Hematology, Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
| | - Ivana Čepelak
- Department of Medical Biochemistry and Hematology, Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
| | - Ivan Pavić
- Department of Pulmonology, Allergology and Immunology, Children’s Hospital Zagreb; School of Medicine, University of Zagreb, Zagreb, Croatia
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Tacutu R, Thornton D, Johnson E, Budovsky A, Barardo D, Craig T, Diana E, Lehmann G, Toren D, Wang J, Fraifeld VE, de Magalhães JP. Human Ageing Genomic Resources: new and updated databases. Nucleic Acids Res 2019; 46:D1083-D1090. [PMID: 29121237 PMCID: PMC5753192 DOI: 10.1093/nar/gkx1042] [Citation(s) in RCA: 402] [Impact Index Per Article: 80.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2017] [Accepted: 10/18/2017] [Indexed: 12/17/2022] Open
Abstract
In spite of a growing body of research and data, human ageing remains a poorly understood process. Over 10 years ago we developed the Human Ageing Genomic Resources (HAGR), a collection of databases and tools for studying the biology and genetics of ageing. Here, we present HAGR’s main functionalities, highlighting new additions and improvements. HAGR consists of six core databases: (i) the GenAge database of ageing-related genes, in turn composed of a dataset of >300 human ageing-related genes and a dataset with >2000 genes associated with ageing or longevity in model organisms; (ii) the AnAge database of animal ageing and longevity, featuring >4000 species; (iii) the GenDR database with >200 genes associated with the life-extending effects of dietary restriction; (iv) the LongevityMap database of human genetic association studies of longevity with >500 entries; (v) the DrugAge database with >400 ageing or longevity-associated drugs or compounds; (vi) the CellAge database with >200 genes associated with cell senescence. All our databases are manually curated by experts and regularly updated to ensure a high quality data. Cross-links across our databases and to external resources help researchers locate and integrate relevant information. HAGR is freely available online (http://genomics.senescence.info/).
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Affiliation(s)
- Robi Tacutu
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool L7 8TX, UK.,Computational Biology of Aging Group, Institute of Biochemistry, Romanian Academy, Bucharest 060031, Romania
| | - Daniel Thornton
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool L7 8TX, UK
| | - Emily Johnson
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool L7 8TX, UK
| | - Arie Budovsky
- The Shraga Segal Department of Microbiology, Immunology and Genetics, Center for Multidisciplinary Research on Aging, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel.,Judea Regional Research & Development Center, Carmel 90404, Israel
| | - Diogo Barardo
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore City 117597, Singapore.,Science Division, Yale-NUS College, Singapore City 138527, Singapore
| | - Thomas Craig
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool L7 8TX, UK
| | - Eugene Diana
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool L7 8TX, UK
| | - Gilad Lehmann
- The Shraga Segal Department of Microbiology, Immunology and Genetics, Center for Multidisciplinary Research on Aging, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Dmitri Toren
- The Shraga Segal Department of Microbiology, Immunology and Genetics, Center for Multidisciplinary Research on Aging, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Jingwei Wang
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool L7 8TX, UK
| | - Vadim E Fraifeld
- The Shraga Segal Department of Microbiology, Immunology and Genetics, Center for Multidisciplinary Research on Aging, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - João P de Magalhães
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool L7 8TX, UK
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Jin M, Li Y, O'Laughlin R, Bittihn P, Pillus L, Tsimring LS, Hasty J, Hao N. Divergent Aging of Isogenic Yeast Cells Revealed through Single-Cell Phenotypic Dynamics. Cell Syst 2019; 8:242-253.e3. [PMID: 30852250 DOI: 10.1016/j.cels.2019.02.002] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 11/28/2018] [Accepted: 02/07/2019] [Indexed: 12/27/2022]
Abstract
Although genetic mutations that alter organisms' average lifespans have been identified in aging research, our understanding of the dynamic changes during aging remains limited. Here, we integrate single-cell imaging, microfluidics, and computational modeling to investigate phenotypic divergence and cellular heterogeneity during replicative aging of single S. cerevisiae cells. Specifically, we find that isogenic cells diverge early in life toward one of two aging paths, which are characterized by distinct age-associated phenotypes. We captured the dynamics of single cells along the paths with a stochastic discrete-state model, which accurately predicts both the measured heterogeneity and the lifespan of cells on each path within a cell population. Our analysis suggests that genetic and environmental factors influence both a cell's choice of paths and the kinetics of paths themselves. Given that these factors are highly conserved throughout eukaryotes, divergent aging might represent a general scheme in cellular aging of other organisms.
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Affiliation(s)
- Meng Jin
- BioCircuits Institute, University of California, San Diego, La Jolla, San Diego, CA 92093, USA
| | - Yang Li
- Section of Molecular Biology, Division of Biological Sciences, University of California, San Diego, La Jolla, San Diego, CA 92093, USA
| | - Richard O'Laughlin
- Department of Bioengineering, University of California, San Diego, La Jolla, San Diego, CA 92093, USA
| | - Philip Bittihn
- BioCircuits Institute, University of California, San Diego, La Jolla, San Diego, CA 92093, USA
| | - Lorraine Pillus
- Section of Molecular Biology, Division of Biological Sciences, University of California, San Diego, La Jolla, San Diego, CA 92093, USA; UCSD Moores Cancer Center, University of California, San Diego, La Jolla, San Diego, CA 92093, USA
| | - Lev S Tsimring
- BioCircuits Institute, University of California, San Diego, La Jolla, San Diego, CA 92093, USA.
| | - Jeff Hasty
- BioCircuits Institute, University of California, San Diego, La Jolla, San Diego, CA 92093, USA; Section of Molecular Biology, Division of Biological Sciences, University of California, San Diego, La Jolla, San Diego, CA 92093, USA; Department of Bioengineering, University of California, San Diego, La Jolla, San Diego, CA 92093, USA.
| | - Nan Hao
- BioCircuits Institute, University of California, San Diego, La Jolla, San Diego, CA 92093, USA; Section of Molecular Biology, Division of Biological Sciences, University of California, San Diego, La Jolla, San Diego, CA 92093, USA.
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8
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Liu D, Xu L, Zhang X, Shi C, Qiao S, Ma Z, Yuan J. Snapshot: Implications for mTOR in Aging-related Ischemia/Reperfusion Injury. Aging Dis 2019; 10:116-133. [PMID: 30705773 PMCID: PMC6345330 DOI: 10.14336/ad.2018.0501] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2017] [Accepted: 05/01/2018] [Indexed: 12/15/2022] Open
Abstract
Aging may aggravate the damage and dysfunction of different components of multiorgan and thus increasing multiorgan ischemia/reperfusion (IR) injury. IR injury occurs in many organs and tissues, which is a major cause of morbidity and mortality worldwide. The kinase mammalian target of rapamycin (mTOR), an atypical serine/threonine protein kinase, involves in the pathophysiological process of IR injury. In this review, we first briefly introduce the molecular features of mTOR, the association between mTOR and aging, and especially its role on autophagy. Special focus is placed on the roles of mTOR during ischemic and IR injury. We then clarify the association between mTOR and conditioning phenomena. Following this background, we expand our discussion to potential future directions of research in this area. Collectively, information reviewed herein will serve as a comprehensive reference for the actions of mTOR in IR injury and may be significant for the design of future research and increase the potential of mTOR as a therapeutic target.
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Affiliation(s)
- Dong Liu
- 1State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China
| | - Liqun Xu
- 1State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China.,2Department of Thoracic Surgery, Tangdu Hospital, The Fourth Military Medical University, 1 Xinsi Road, Xi'an 710038, China.,3Cadet group 3, School of Basic Medical Sciences, The Fourth Military Medical University, Xi'an 710032, China.,4Laboratory Animal Center, The Fourth Military Medical University, Xi'an 710032, China
| | - Xiaoyan Zhang
- 2Department of Thoracic Surgery, Tangdu Hospital, The Fourth Military Medical University, 1 Xinsi Road, Xi'an 710038, China.,3Cadet group 3, School of Basic Medical Sciences, The Fourth Military Medical University, Xi'an 710032, China
| | - Changhong Shi
- 4Laboratory Animal Center, The Fourth Military Medical University, Xi'an 710032, China
| | - Shubin Qiao
- 1State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China
| | - Zhiqiang Ma
- 1State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China.,2Department of Thoracic Surgery, Tangdu Hospital, The Fourth Military Medical University, 1 Xinsi Road, Xi'an 710038, China
| | - Jiansong Yuan
- 1State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China
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9
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Yoo B, Faisal FE, Chen H, Milenkovic T. Improving Identification of Key Players in Aging via Network De-Noising and Core Inference. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:1056-1069. [PMID: 26529776 DOI: 10.1109/tcbb.2015.2495170] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Current "ground truth" knowledge about human aging has been obtained by transferring aging-related knowledge from well-studied model species via sequence homology or by studying human gene expression data. Since proteins function by interacting with each other, analyzing protein-protein interaction (PPI) networks in the context of aging is promising. Unlike existing static network research of aging, since cellular functioning is dynamic, we recently integrated the static human PPI network with aging-related gene expression data to form dynamic, age-specific networks. Then, we predicted as key players in aging those proteins whose network topologies significantly changed with age. Since current networks are noisy , here, we use link prediction to de-noise the human network and predict improved key players in aging from the de-noised data. Indeed, de-noising gives more significant overlap between the predicted data and the "ground truth" aging-related data. Yet, we obtain novel predictions, which we validate in the literature. Also, we improve the predictions by an alternative strategy: removing "redundant" edges from the age-specific networks and using the resulting age-specific network "cores" to study aging. We produce new knowledge from dynamic networks encompassing multiple data types, via network de-noising or core inference, complementing the existing knowledge obtained from sequence or expression data.
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10
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Song Y, Li Q, Zhang F, Huang H, Feng D, Wang Y, Chen M, Cai W. Dual discriminative local coding for tissue aging analysis. Med Image Anal 2017; 38:65-76. [PMID: 28282641 DOI: 10.1016/j.media.2016.10.001] [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: 03/07/2016] [Revised: 07/12/2016] [Accepted: 10/05/2016] [Indexed: 11/26/2022]
Abstract
In aging research, morphological age of tissue helps to characterize the effects of aging on different individuals. While currently manual evaluations are used to estimate morphological ages under microscopy, such operation is difficult and subjective due to the complex visual characteristics of tissue images. In this paper, we propose an automated method to quantify morphological ages of tissues from microscopy images. We design a new sparse representation method, namely dual discriminative local coding (DDLC), that classifies the tissue images into different chronological ages. DDLC in- corporates discriminative distance learning and dual-level local coding into the basis model of locality-constrained linear coding thus achieves higher discriminative capability. The morphological age is then computed based on the classification scores. We conducted our study using the publicly avail- able terminal bulb aging database that has been commonly used in existing microscopy imaging research. To represent these images, we also design a highly descriptive descriptor that combines several complementary texture features extracted at two scales. Experimental results show that our method achieves significant improvement in age classification when compared to the existing approaches and other popular classifiers. We also present promising results in quantification of morphological ages.
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Affiliation(s)
- Yang Song
- School of Information Technologies, University of Sydney, Australia.
| | - Qing Li
- School of Information Technologies, University of Sydney, Australia
| | - Fan Zhang
- School of Information Technologies, University of Sydney, Australia
| | - Heng Huang
- Department of Computer Science and Engineering, University of Texas at Arlington, USA
| | - Dagan Feng
- School of Information Technologies, University of Sydney, Australia; Med-X Research Institute, Shanghai Jiaotong University, China
| | - Yue Wang
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, USA
| | - Mei Chen
- Computer Engineering Department, University of Albany State University of New York, USA; Robotics Institute, Carnegie Mellon University, USA
| | - Weidong Cai
- School of Information Technologies, University of Sydney, Australia
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11
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Georgousopoulou EN, Panagiotakos DB, Mellor DD, Naumovski N. Tocotrienols, health and ageing: A systematic review. Maturitas 2016; 95:55-60. [PMID: 27889054 DOI: 10.1016/j.maturitas.2016.11.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2016] [Accepted: 11/04/2016] [Indexed: 01/04/2023]
Abstract
OBJECTIVES A systematic review of studies was undertaken to evaluate the potential effect of intake of tocotrienols or circulating levels of tocotrienols on parameters associated with successful ageing, specifically in relation to cognitive function, osteoporosis and DNA damage. METHODS Following PRISMA guidelines a systematic review of epidemiological observational studies and clinical trials was undertaken. Inclusion criteria included all English language publications in the databases PubMed and Scopus, through to the end of July 2016. RESULTS Evidence from prospective and case-control studies suggested that increased blood levels of tocotrienols were associated with favorable cognitive function outcomes. A clinical trial of tocotrienol supplementation for 6 months suggested a beneficial effect of intake on DNA damage rates, but only in elderly people. Regarding osteoporosis, only in vitro studies with cultures of human bone cells were identified, and these demonstrated significant inhibition of osteoclast activity and promotion of osteoblast activity. CONCLUSIONS Research in middle-aged and elderly humans suggests that tocotrienols have a potential beneficial anti-ageing action with respect to cognitive impairment and DNA damage. Clinical trials are required to elucidate these effects.
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Affiliation(s)
- Ekavi N Georgousopoulou
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece; University of Canberra, Faculty of Health, Canberra, Australia
| | - Demosthenes B Panagiotakos
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece.
| | - Duane D Mellor
- University of Canberra, Faculty of Health, Canberra, Australia
| | - Nenad Naumovski
- University of Canberra, Faculty of Health, Canberra, Australia
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12
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Smita S, Lange F, Wolkenhauer O, Köhling R. Deciphering hallmark processes of aging from interaction networks. Biochim Biophys Acta Gen Subj 2016; 1860:2706-15. [PMID: 27456767 DOI: 10.1016/j.bbagen.2016.07.017] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Revised: 07/18/2016] [Accepted: 07/20/2016] [Indexed: 12/18/2022]
Abstract
BACKGROUND Aging is broadly considered to be a dynamic process that accumulates unfavourable structural and functional changes in a time dependent fashion, leading to a progressive loss of physiological integrity of an organism, which eventually leads to age-related diseases and finally to death. SCOPE OF REVIEW The majority of aging-related studies are based on reductionist approaches, focusing on single genes/proteins or on individual pathways without considering possible interactions between them. Over the last few decades, several such genes/proteins were independently analysed and linked to a role that is affecting the longevity of an organism. However, an isolated analysis on genes and proteins largely fails to explain the mechanistic insight of a complex phenotype due to the involvement and integration of multiple factors. MAJOR CONCLUSIONS Technological advance makes it possible to generate high-throughput temporal and spatial data that provide an opportunity to use computer-based methods. These techniques allow us to go beyond reductionist approaches to analyse large-scale networks that provide deeper understanding of the processes that drive aging. GENERAL SIGNIFICANCE In this review, we focus on systems biology approaches, based on network inference methods to understand the dynamics of hallmark processes leading to aging phenotypes. We also describe computational methods for the interpretation and identification of important molecular hubs involved in the mechanistic linkage between aging related processes. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.
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Affiliation(s)
- Suchi Smita
- Department of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany; Oscar-Langendorff-Institute of Physiology, Rostock University Medical Center, Rostock, Germany.
| | - Falko Lange
- Oscar-Langendorff-Institute of Physiology, Rostock University Medical Center, Rostock, Germany.
| | - Olaf Wolkenhauer
- Department of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany; Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, Stellenbosch, South Africa.
| | - Rüdiger Köhling
- Oscar-Langendorff-Institute of Physiology, Rostock University Medical Center, Rostock, Germany.
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13
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Nydegger U, Lung T, Risch L, Risch M, Medina Escobar P, Bodmer T. Inflammation Thread Runs across Medical Laboratory Specialities. Mediators Inflamm 2016; 2016:4121837. [PMID: 27493451 PMCID: PMC4963559 DOI: 10.1155/2016/4121837] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2016] [Accepted: 05/31/2016] [Indexed: 12/16/2022] Open
Abstract
We work on the assumption that four major specialities or sectors of medical laboratory assays, comprising clinical chemistry, haematology, immunology, and microbiology, embraced by genome sequencing techniques, are routinely in use. Medical laboratory markers for inflammation serve as model: they are allotted to most fields of medical lab assays including genomics. Incessant coding of assays aligns each of them in the long lists of big data. As exemplified with the complement gene family, containing C2, C3, C8A, C8B, CFH, CFI, and ITGB2, heritability patterns/risk factors associated with diseases with genetic glitch of complement components are unfolding. The C4 component serum levels depend on sufficient vitamin D whilst low vitamin D is inversely related to IgG1, IgA, and C3 linking vitamin sufficiency to innate immunity. Whole genome sequencing of microbial organisms may distinguish virulent from nonvirulent and antibiotic resistant from nonresistant varieties of the same species and thus can be listed in personal big data banks including microbiological pathology; the big data warehouse continues to grow.
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Affiliation(s)
- Urs Nydegger
- Labormedizinisches Zentrum Dr. Risch and Kantonsspital Graubünden, 7000 Chur, Switzerland
| | - Thomas Lung
- Labormedizinisches Zentrum Dr. Risch and Kantonsspital Graubünden, 7000 Chur, Switzerland
| | - Lorenz Risch
- Labormedizinisches Zentrum Dr. Risch and Kantonsspital Graubünden, 7000 Chur, Switzerland
| | - Martin Risch
- Labormedizinisches Zentrum Dr. Risch and Kantonsspital Graubünden, 7000 Chur, Switzerland
| | - Pedro Medina Escobar
- Labormedizinisches Zentrum Dr. Risch and Kantonsspital Graubünden, 7000 Chur, Switzerland
| | - Thomas Bodmer
- Labormedizinisches Zentrum Dr. Risch and Kantonsspital Graubünden, 7000 Chur, Switzerland
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14
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Zierer J, Menni C, Kastenmüller G, Spector TD. Integration of 'omics' data in aging research: from biomarkers to systems biology. Aging Cell 2015; 14:933-44. [PMID: 26331998 PMCID: PMC4693464 DOI: 10.1111/acel.12386] [Citation(s) in RCA: 81] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/20/2015] [Indexed: 12/16/2022] Open
Abstract
Age is the strongest risk factor for many diseases including neurodegenerative disorders, coronary heart disease, type 2 diabetes and cancer. Due to increasing life expectancy and low birth rates, the incidence of age-related diseases is increasing in industrialized countries. Therefore, understanding the relationship between diseases and aging and facilitating healthy aging are major goals in medical research. In the last decades, the dimension of biological data has drastically increased with high-throughput technologies now measuring thousands of (epi) genetic, expression and metabolic variables. The most common and so far successful approach to the analysis of these data is the so-called reductionist approach. It consists of separately testing each variable for association with the phenotype of interest such as age or age-related disease. However, a large portion of the observed phenotypic variance remains unexplained and a comprehensive understanding of most complex phenotypes is lacking. Systems biology aims to integrate data from different experiments to gain an understanding of the system as a whole rather than focusing on individual factors. It thus allows deeper insights into the mechanisms of complex traits, which are caused by the joint influence of several, interacting changes in the biological system. In this review, we look at the current progress of applying omics technologies to identify biomarkers of aging. We then survey existing systems biology approaches that allow for an integration of different types of data and highlight the need for further developments in this area to improve epidemiologic investigations.
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Affiliation(s)
- Jonas Zierer
- Department of Twins Research and Genetic EpidemiologyKings College LondonLondonUnited Kingdom
- Institute of Bioinformatics and Systems BiologyHelmholtz Zentrum MünchenNeuherbergGermany
| | - Cristina Menni
- Department of Twins Research and Genetic EpidemiologyKings College LondonLondonUnited Kingdom
| | - Gabi Kastenmüller
- Department of Twins Research and Genetic EpidemiologyKings College LondonLondonUnited Kingdom
- Institute of Bioinformatics and Systems BiologyHelmholtz Zentrum MünchenNeuherbergGermany
| | - Tim D. Spector
- Department of Twins Research and Genetic EpidemiologyKings College LondonLondonUnited Kingdom
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15
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Papatheodorou I, Oellrich A, Smedley D. Linking gene expression to phenotypes via pathway information. J Biomed Semantics 2015; 6:17. [PMID: 25901272 PMCID: PMC4404592 DOI: 10.1186/s13326-015-0013-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2014] [Accepted: 03/19/2015] [Indexed: 11/10/2022] Open
Abstract
Establishing robust links among gene expression, pathways and phenotypes is critical for understanding diseases and developing treatments. In recent years there have been many efforts to develop the computational means to traverse from genes to gene expression, model pathways and classify phenotypes. Numerous ontologies and other controlled vocabularies have been developed, as well as computational methods to combine and mine these data sets and establish connections. Here we discuss these efforts and identify areas of future work that could lead to a better integration of genes, pathways and phenotypes to provide insights into the mechanisms under which gene mutations affect expression and pathways and how these effects are manifested onto the phenotype.
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Affiliation(s)
- Irene Papatheodorou
- Mouse Developmental Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, CB1 10SA, Hinxton, UK
| | - Anika Oellrich
- Mouse Developmental Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, CB1 10SA, Hinxton, UK
| | - Damian Smedley
- Mouse Developmental Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, CB1 10SA, Hinxton, UK
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16
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Faisal FE, Zhao H, Milenkovic T. Global Network Alignment in the Context of Aging. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015; 12:40-52. [PMID: 26357077 DOI: 10.1109/tcbb.2014.2326862] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Analogous to sequence alignment, network alignment (NA) can be used to transfer biological knowledge across species between conserved network regions. NA faces two algorithmic challenges: 1) Which cost function to use to capture "similarities" between nodes in different networks? 2) Which alignment strategy to use to rapidly identify "high-scoring" alignments from all possible alignments? We "break down" existing state-of-the-art methods that use both different cost functions and different alignment strategies to evaluate each combination of their cost functions and alignment strategies. We find that a combination of the cost function of one method and the alignment strategy of another method beats the existing methods. Hence, we propose this combination as a novel superior NA method. Then, since human aging is hard to study experimentally due to long lifespan, we use NA to transfer aging-related knowledge from well annotated model species to poorly annotated human. By doing so, we produce novel human aging-related knowledge, which complements currently available knowledge about aging that has been obtained mainly by sequence alignment. We demonstrate significant similarity between topological and functional properties of our novel predictions and those of known aging-related genes. We are the first to use NA to learn more about aging.
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17
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O’Connor JE, Herrera G, Martínez-Romero A, de Oyanguren FS, Díaz L, Gomes A, Balaguer S, Callaghan RC. Systems Biology and immune aging. Immunol Lett 2014; 162:334-45. [DOI: 10.1016/j.imlet.2014.09.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2014] [Accepted: 09/12/2014] [Indexed: 10/24/2022]
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18
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O'Connor JE, Herrera G, Martínez-Romero A, Oyanguren FSD, Díaz L, Gomes A, Balaguer S, Callaghan RC. WITHDRAWN: Systems Biology and Immune Aging. Immunol Lett 2014:S0165-2478(14)00197-7. [PMID: 25251659 DOI: 10.1016/j.imlet.2014.09.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2014] [Accepted: 09/12/2014] [Indexed: 10/24/2022]
Abstract
The Publisher regrets that this article is an accidental duplication of anarticle that has already been published, http://dx.doi.org/10.1016/j.imlet.2014.09.009. The duplicate article has therefore been withdrawn.
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Affiliation(s)
- José-Enrique O'Connor
- Laboratory of Translational Cytomics, Joint Research Unit, The University of Valencia and Principe Felipe Research Center, Valencia, Spain; Cytometry Laboratory, Incliva Foundation, Clinical University Hospital, The University of Valencia, Valencia, Spain.
| | - Guadalupe Herrera
- Laboratory of Translational Cytomics, Joint Research Unit, The University of Valencia and Principe Felipe Research Center, Valencia, Spain; Cytometry Laboratory, Incliva Foundation, Clinical University Hospital, The University of Valencia, Valencia, Spain
| | - Alicia Martínez-Romero
- Cytometry Technological Service, Principe Felipe Research Center, Valencia, Spain; Cytometry Laboratory, Incliva Foundation, Clinical University Hospital, The University of Valencia, Valencia, Spain
| | - Francisco Sala-de Oyanguren
- Laboratory of Translational Cytomics, Joint Research Unit, The University of Valencia and Principe Felipe Research Center, Valencia, Spain; Cytometry Laboratory, Incliva Foundation, Clinical University Hospital, The University of Valencia, Valencia, Spain
| | - Laura Díaz
- Laboratory of Translational Cytomics, Joint Research Unit, The University of Valencia and Principe Felipe Research Center, Valencia, Spain; Cytometry Laboratory, Incliva Foundation, Clinical University Hospital, The University of Valencia, Valencia, Spain
| | - Angela Gomes
- Laboratory of Translational Cytomics, Joint Research Unit, The University of Valencia and Principe Felipe Research Center, Valencia, Spain; Cytometry Laboratory, Incliva Foundation, Clinical University Hospital, The University of Valencia, Valencia, Spain
| | - Susana Balaguer
- Laboratory of Translational Cytomics, Joint Research Unit, The University of Valencia and Principe Felipe Research Center, Valencia, Spain; Cytometry Laboratory, Incliva Foundation, Clinical University Hospital, The University of Valencia, Valencia, Spain
| | - Robert C Callaghan
- Department of Pathology, Faculty of Medicine, The University of Valencia, Valencia, Spain; Cytometry Laboratory, Incliva Foundation, Clinical University Hospital, The University of Valencia, Valencia, Spain
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Abstract
MOTIVATION Because susceptibility to diseases increases with age, studying aging gains importance. Analyses of gene expression or sequence data, which have been indispensable for investigating aging, have been limited to studying genes and their protein products in isolation, ignoring their connectivities. However, proteins function by interacting with other proteins, and this is exactly what biological networks (BNs) model. Thus, analyzing the proteins' BN topologies could contribute to the understanding of aging. Current methods for analyzing systems-level BNs deal with their static representations, even though cells are dynamic. For this reason, and because different data types can give complementary biological insights, we integrate current static BNs with aging-related gene expression data to construct dynamic age-specific BNs. Then, we apply sensitive measures of topology to the dynamic BNs to study cellular changes with age. RESULTS While global BN topologies do not significantly change with age, local topologies of a number of genes do. We predict such genes to be aging-related. We demonstrate credibility of our predictions by (i) observing significant overlap between our predicted aging-related genes and 'ground truth' aging-related genes; (ii) observing significant overlap between functions and diseases that are enriched in our aging-related predictions and those that are enriched in 'ground truth' aging-related data; (iii) providing evidence that diseases which are enriched in our aging-related predictions are linked to human aging; and (iv) validating our high-scoring novel predictions in the literature. AVAILABILITY AND IMPLEMENTATION Software executables are available upon request.
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Affiliation(s)
- Fazle E Faisal
- Department of Computer Science and Engineering, ECK Institute for Global Health and Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Tijana Milenković
- Department of Computer Science and Engineering, ECK Institute for Global Health and Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, IN 46556, USA
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20
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Papatheodorou I, Ziehm M, Wieser D, Alic N, Partridge L, Thornton JM. Using answer set programming to integrate RNA expression with signalling pathway information to infer how mutations affect ageing. PLoS One 2012; 7:e50881. [PMID: 23251396 PMCID: PMC3519537 DOI: 10.1371/journal.pone.0050881] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2012] [Accepted: 10/25/2012] [Indexed: 01/03/2023] Open
Abstract
A challenge of systems biology is to integrate incomplete knowledge on pathways with existing experimental data sets and relate these to measured phenotypes. Research on ageing often generates such incomplete data, creating difficulties in integrating RNA expression with information about biological processes and the phenotypes of ageing, including longevity. Here, we develop a logic-based method that employs Answer Set Programming, and use it to infer signalling effects of genetic perturbations, based on a model of the insulin signalling pathway. We apply our method to RNA expression data from Drosophila mutants in the insulin pathway that alter lifespan, in a foxo dependent fashion. We use this information to deduce how the pathway influences lifespan in the mutant animals. We also develop a method for inferring the largest common sub-paths within each of our signalling predictions. Our comparisons reveal consistent homeostatic mechanisms across both long- and short-lived mutants. The transcriptional changes observed in each mutation usually provide negative feedback to signalling predicted for that mutation. We also identify an S6K-mediated feedback in two long-lived mutants that suggests a crosstalk between these pathways in mutants of the insulin pathway, in vivo. By formulating the problem as a logic-based theory in a qualitative fashion, we are able to use the efficient search facilities of Answer Set Programming, allowing us to explore larger pathways, combine molecular changes with pathways and phenotype and infer effects on signalling in in vivo, whole-organism, mutants, where direct signalling stimulation assays are difficult to perform. Our methods are available in the web-service NetEffects: http://www.ebi.ac.uk/thornton-srv/software/NetEffects.
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Affiliation(s)
- Irene Papatheodorou
- European Molecular Biology Laboratory-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom.
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21
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Harries LW, Fellows AD, Pilling LC, Hernandez D, Singleton A, Bandinelli S, Guralnik J, Powell J, Ferrucci L, Melzer D. Advancing age is associated with gene expression changes resembling mTOR inhibition: evidence from two human populations. Mech Ageing Dev 2012; 133:556-62. [PMID: 22813852 DOI: 10.1016/j.mad.2012.07.003] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2012] [Revised: 06/25/2012] [Accepted: 07/07/2012] [Indexed: 12/21/2022]
Abstract
Interventions which inhibit TOR activity (including rapamycin and caloric restriction) lead to downstream gene expression changes and increased lifespan in laboratory models. However, the role of mTOR signaling in human aging is unclear. We tested the expression of mTOR-related transcripts in two independent study cohorts; the InCHIANTI population study of aging and the San Antonio Family Heart Study (SAFHS). Expression of 27/56 (InCHIANTI) and 19/44 (SAFHS) genes were associated with age after correction for multiple testing. 8 genes were robustly associated with age in both cohorts. Genes involved in insulin signaling (PTEN, PI3K, PDK1), ribosomal biogenesis (S6K), lipid metabolism (SREBF1), cellular apoptosis (SGK1), angiogenesis (VEGFB), insulin production and sensitivity (FOXO), cellular stress response (HIF1A) and cytoskeletal remodeling (PKC) were inversely correlated with age, whereas genes relating to inhibition of ribosomal components (4EBP1) and inflammatory mediators (STAT3) were positively associated with age in one or both datasets. We conclude that the expression of mTOR-related transcripts is associated with advancing age in humans. Changes seen are broadly similar to mTOR inhibition interventions associated with increased lifespan in animals. Work is needed to establish whether these changes are predictive of human longevity and whether further mTOR inhibition would be beneficial in older people.
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Affiliation(s)
- Lorna W Harries
- Institute of Biomedical and Clinical Sciences, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter EX2 5DW, UK
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22
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Teplyuk NM. Near-to-perfect homeostasis: examples of universal aging rule which germline evades. J Cell Biochem 2012; 113:388-96. [PMID: 21928349 DOI: 10.1002/jcb.23366] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Aging is considered to be a progressive decline in an organism's functioning over time and is almost universal throughout the living world. Currently, many different aging mechanisms have been reported at all levels of biological organization, with a variety of biochemical, metabolic, and genetic pathways involved. Some of these mechanisms are common across species, and others work different, but each of them is constitutive. This review describes the common characteristics of the aging processes, which are consistent changes over time that involve either the accumulation or depletion of particular system components. These accumulations and depletions may result from imperfect homeostasis, which is the incomplete compensation of a particular biological process with another process evolved to compensate it. In accordance with disposable-soma theory, this imperfection in homeostasis may originate as a function of cell differentiation as early as in yeasts. It may result either from antagonistic pleiotropy mechanisms, or be simply negligible as a subject of natural selection if an adverse effect of the accumulation phenotypically manifests in organism's post-reproductive age. If this phenomenon holds true for many different functions it would lead to the occurrence of a wide variety of aging mechanisms, some of which are common among species, while others unique, because aging is the inherent property of most biological processes that have not yet evolved to be perfectly in balance. Examples of imperfect homeostasis mechanisms of aging, the ways in which germ line escapes from them, and the possibilities of anti-aging treatment are discussed in this review.
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Affiliation(s)
- Nadiya M Teplyuk
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
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23
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Partridge L, Thornton J, Bates G. The new science of ageing. Philos Trans R Soc Lond B Biol Sci 2011; 366:6-8. [PMID: 21115524 DOI: 10.1098/rstb.2010.0298] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Affiliation(s)
- Linda Partridge
- Department of Genetics, Evolution and Environment, Darwin Building, University College London, Gower Street, London WC1E 6BT, UK.
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