1
|
Roth‐Walter F, Adcock IM, Benito‐Villalvilla C, Bianchini R, Bjermer L, Caramori G, Cari L, Chung KF, Diamant Z, Eguiluz‐Gracia I, Knol EF, Jesenak M, Levi‐Schaffer F, Nocentini G, O'Mahony L, Palomares O, Redegeld F, Sokolowska M, Van Esch BCAM, Stellato C. Metabolic pathways in immune senescence and inflammaging: Novel therapeutic strategy for chronic inflammatory lung diseases. An EAACI position paper from the Task Force for Immunopharmacology. Allergy 2024; 79:1089-1122. [PMID: 38108546 PMCID: PMC11497319 DOI: 10.1111/all.15977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/24/2023] [Accepted: 11/27/2023] [Indexed: 12/19/2023]
Abstract
The accumulation of senescent cells drives inflammaging and increases morbidity of chronic inflammatory lung diseases. Immune responses are built upon dynamic changes in cell metabolism that supply energy and substrates for cell proliferation, differentiation, and activation. Metabolic changes imposed by environmental stress and inflammation on immune cells and tissue microenvironment are thus chiefly involved in the pathophysiology of allergic and other immune-driven diseases. Altered cell metabolism is also a hallmark of cell senescence, a condition characterized by loss of proliferative activity in cells that remain metabolically active. Accelerated senescence can be triggered by acute or chronic stress and inflammatory responses. In contrast, replicative senescence occurs as part of the physiological aging process and has protective roles in cancer surveillance and wound healing. Importantly, cell senescence can also change or hamper response to diverse therapeutic treatments. Understanding the metabolic pathways of senescence in immune and structural cells is therefore critical to detect, prevent, or revert detrimental aspects of senescence-related immunopathology, by developing specific diagnostics and targeted therapies. In this paper, we review the main changes and metabolic alterations occurring in senescent immune cells (macrophages, B cells, T cells). Subsequently, we present the metabolic footprints described in translational studies in patients with chronic asthma and chronic obstructive pulmonary disease (COPD), and review the ongoing preclinical studies and clinical trials of therapeutic approaches aiming at targeting metabolic pathways to antagonize pathological senescence. Because this is a recently emerging field in allergy and clinical immunology, a better understanding of the metabolic profile of the complex landscape of cell senescence is needed. The progress achieved so far is already providing opportunities for new therapies, as well as for strategies aimed at disease prevention and supporting healthy aging.
Collapse
Affiliation(s)
- F. Roth‐Walter
- Comparative Medicine, The Interuniversity Messerli Research Institute of the University of Veterinary Medicine ViennaMedical University Vienna and University ViennaViennaAustria
- Institute of Pathophysiology and Allergy Research, Center of Pathophysiology, Infectiology and ImmunologyMedical University of ViennaViennaAustria
| | - I. M. Adcock
- Molecular Cell Biology Group, National Heart & Lung InstituteImperial College LondonLondonUK
| | - C. Benito‐Villalvilla
- Department of Biochemistry and Molecular Biology, School of ChemistryComplutense University of MadridMadridSpain
| | - R. Bianchini
- Comparative Medicine, The Interuniversity Messerli Research Institute of the University of Veterinary Medicine ViennaMedical University Vienna and University ViennaViennaAustria
| | - L. Bjermer
- Department of Respiratory Medicine and Allergology, Lung and Allergy research, Allergy, Asthma and COPD Competence CenterLund UniversityLundSweden
| | - G. Caramori
- Department of Medicine and SurgeryUniversity of ParmaPneumologiaItaly
| | - L. Cari
- Department of Medicine, Section of PharmacologyUniversity of PerugiaPerugiaItaly
| | - K. F. Chung
- Experimental Studies Medicine at National Heart & Lung InstituteImperial College London & Royal Brompton & Harefield HospitalLondonUK
| | - Z. Diamant
- Department of Respiratory Medicine and Allergology, Institute for Clinical ScienceSkane University HospitalLundSweden
- Department of Respiratory Medicine, First Faculty of MedicineCharles University and Thomayer HospitalPragueCzech Republic
- Department of Clinical Pharmacy & PharmacologyUniversity Groningen, University Medical Center Groningen and QPS‐NLGroningenThe Netherlands
| | - I. Eguiluz‐Gracia
- Allergy UnitHospital Regional Universitario de Málaga‐Instituto de Investigación Biomédica de Málaga (IBIMA)‐ARADyALMálagaSpain
| | - E. F. Knol
- Departments of Center of Translational Immunology and Dermatology/AllergologyUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - M. Jesenak
- Department of Paediatrics, Department of Pulmonology and Phthisiology, Comenius University in Bratislava, Jessenius Faculty of Medicine in MartinUniversity Teaching HospitalMartinSlovakia
| | - F. Levi‐Schaffer
- Institute for Drug Research, Pharmacology Unit, Faculty of MedicineThe Hebrew University of JerusalemJerusalemIsrael
| | - G. Nocentini
- Department of Medicine, Section of PharmacologyUniversity of PerugiaPerugiaItaly
| | - L. O'Mahony
- APC Microbiome IrelandUniversity College CorkCorkIreland
- Department of MedicineUniversity College CorkCorkIreland
- School of MicrobiologyUniversity College CorkCorkIreland
| | - O. Palomares
- Department of Biochemistry and Molecular Biology, School of ChemistryComplutense University of MadridMadridSpain
| | - F. Redegeld
- Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of ScienceUtrecht UniversityUtrechtThe Netherlands
| | - M. Sokolowska
- Swiss Institute of Allergy and Asthma Research (SIAF)University of ZürichDavosSwitzerland
- Christine Kühne – Center for Allergy Research and Education (CK‐CARE)DavosSwitzerland
| | - B. C. A. M. Van Esch
- Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of ScienceUtrecht UniversityUtrechtThe Netherlands
| | - C. Stellato
- Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”University of SalernoSalernoItaly
| |
Collapse
|
2
|
Fernandes M, Wan C, Tacutu R, Barardo D, Rajput A, Wang J, Thoppil H, Thornton D, Yang C, Freitas A, de Magalhães JP. Systematic analysis of the gerontome reveals links between aging and age-related diseases. Hum Mol Genet 2018; 25:4804-4818. [PMID: 28175300 PMCID: PMC5418736 DOI: 10.1093/hmg/ddw307] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Revised: 08/05/2016] [Accepted: 08/26/2016] [Indexed: 12/11/2022] Open
Abstract
In model organisms, over 2,000 genes have been shown to modulate aging, the collection of which we call the ‘gerontome’. Although some individual aging-related genes have been the subject of intense scrutiny, their analysis as a whole has been limited. In particular, the genetic interaction of aging and age-related pathologies remain a subject of debate. In this work, we perform a systematic analysis of the gerontome across species, including human aging-related genes. First, by classifying aging-related genes as pro- or anti-longevity, we define distinct pathways and genes that modulate aging in different ways. Our subsequent comparison of aging-related genes with age-related disease genes reveals species-specific effects with strong overlaps between aging and age-related diseases in mice, yet surprisingly few overlaps in lower model organisms. We discover that genetic links between aging and age-related diseases are due to a small fraction of aging-related genes which also tend to have a high network connectivity. Other insights from our systematic analysis include assessing how using datasets with genes more or less studied than average may result in biases, showing that age-related disease genes have faster molecular evolution rates and predicting new aging-related drugs based on drug-gene interaction data. Overall, this is the largest systems-level analysis of the genetics of aging to date and the first to discriminate anti- and pro-longevity genes, revealing new insights on aging-related genes as a whole and their interactions with age-related diseases.
Collapse
Affiliation(s)
- Maria Fernandes
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.,LaSIGE - Large-Scale Informatics Systems Laboratory, Faculty of Sciences, University of Lisbon, Portugal
| | - Cen Wan
- School of Computing, University of Kent, Canterbury, UK.,Department of Computer Science, University College London, London, UK
| | - Robi Tacutu
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK
| | - Diogo Barardo
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK
| | - Ashish Rajput
- Research Group for Computational Systems Biology, German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
| | - Jingwei Wang
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK
| | - Harikrishnan Thoppil
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK
| | - Daniel Thornton
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK
| | - Chenhao Yang
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK
| | - Alex Freitas
- School of Computing, University of Kent, Canterbury, UK
| | - João Pedro de Magalhães
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK
| |
Collapse
|
3
|
The aging kidney revisited: a systematic review. Ageing Res Rev 2014; 14:65-80. [PMID: 24548926 DOI: 10.1016/j.arr.2014.02.003] [Citation(s) in RCA: 164] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2013] [Revised: 02/05/2014] [Accepted: 02/06/2014] [Indexed: 01/10/2023]
Abstract
As for the whole human body, the kidney undergoes age-related changes which translate in an inexorable and progressive decline in renal function. Renal aging is a multifactorial process where gender, race and genetic background and several key-mediators such as chronic inflammation, oxidative stress, the renin-angiotensin-aldosterone (RAAS) system, impairment in kidney repair capacities and background cardiovascular disease play a significant role. Features of the aging kidney include macroscopic and microscopic changes and important functional adaptations, none of which is pathognomonic of aging. The assessment of renal function in the framework of aging is problematic and the question whether renal aging should be considered as a physiological or pathological process remains a much debated issue. Although promising dietary and pharmacological approaches have been tested to retard aging processes or renal function decline in the elderly, proper lifestyle modifications, as those applicable to the general population, currently represent the most plausible approach to maintain kidney health.
Collapse
|
4
|
Hühne R, Thalheim T, Sühnel J. AgeFactDB--the JenAge Ageing Factor Database--towards data integration in ageing research. Nucleic Acids Res 2013; 42:D892-6. [PMID: 24217911 PMCID: PMC3964983 DOI: 10.1093/nar/gkt1073] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
AgeFactDB (http://agefactdb.jenage.de) is a database aimed at the collection and integration of ageing phenotype data including lifespan information. Ageing factors are considered to be genes, chemical compounds or other factors such as dietary restriction, whose action results in a changed lifespan or another ageing phenotype. Any information related to the effects of ageing factors is called an observation and is presented on observation pages. To provide concise access to the complete information for a particular ageing factor, corresponding observations are also summarized on ageing factor pages. In a first step, ageing-related data were primarily taken from existing databases such as the Ageing Gene Database--GenAge, the Lifespan Observations Database and the Dietary Restriction Gene Database--GenDR. In addition, we have started to include new ageing-related information. Based on homology data taken from the HomoloGene Database, AgeFactDB also provides observation and ageing factor pages of genes that are homologous to known ageing-related genes. These homologues are considered as candidate or putative ageing-related genes. AgeFactDB offers a variety of search and browse options, and also allows the download of ageing factor or observation lists in TSV, CSV and XML formats.
Collapse
Affiliation(s)
- Rolf Hühne
- Biocomputing Group, Leibniz Institute for Age Research - Fritz Lipmann Institute, Jena Centre for Systems Biology of Ageing - JenAge, Beutenbergstrasse 11, Jena, Germany
| | | | | |
Collapse
|
5
|
Alexeyenko A, Schmitt T, Tjärnberg A, Guala D, Frings O, Sonnhammer ELL. Comparative interactomics with Funcoup 2.0. Nucleic Acids Res 2011; 40:D821-8. [PMID: 22110034 PMCID: PMC3245127 DOI: 10.1093/nar/gkr1062] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
FunCoup (http://FunCoup.sbc.su.se) is a database that maintains and visualizes global gene/protein networks of functional coupling that have been constructed by Bayesian integration of diverse high-throughput data. FunCoup achieves high coverage by orthology-based integration of data sources from different model organisms and from different platforms. We here present release 2.0 in which the data sources have been updated and the methodology has been refined. It contains a new data type Genetic Interaction, and three new species: chicken, dog and zebra fish. As FunCoup extensively transfers functional coupling information between species, the new input datasets have considerably improved both coverage and quality of the networks. The number of high-confidence network links has increased dramatically. For instance, the human network has more than eight times as many links above confidence 0.5 as the previous release. FunCoup provides facilities for analysing the conservation of subnetworks in multiple species. We here explain how to do comparative interactomics on the FunCoup website.
Collapse
Affiliation(s)
- Andrey Alexeyenko
- School of Biotechnology, Royal Institute of Technology, Science for Life Laboratory, Box 1031, SE-17121 Solna, Sweden
| | | | | | | | | | | |
Collapse
|
6
|
Understanding the biology of aging with interaction networks. Maturitas 2011; 69:126-30. [DOI: 10.1016/j.maturitas.2011.03.013] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2011] [Accepted: 03/10/2011] [Indexed: 11/22/2022]
|
7
|
Ranganathan S, Schönbach C, Nakai K, Tan TW. Challenges of the next decade for the Asia Pacific region: 2010 International Conference in Bioinformatics (InCoB 2010). BMC Genomics 2010; 11 Suppl 4:S1. [PMID: 21143792 PMCID: PMC3005919 DOI: 10.1186/1471-2164-11-s4-s1] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The 2010 annual conference of the Asia Pacific Bioinformatics Network (APBioNet), Asia’s oldest bioinformatics organisation formed in 1998, was organized as the 9th International Conference on Bioinformatics (InCoB), Sept. 26-28, 2010 in Tokyo, Japan. Initially, APBioNet created InCoB as forum to foster bioinformatics in the Asia Pacific region. Given the growing importance of interdisciplinary research, InCoB2010 included topics targeting scientists in the fields of genomic medicine, immunology and chemoinformatics, supporting translational research. Peer-reviewed manuscripts that were accepted for publication in this supplement, represent key areas of research interests that have emerged in our region. We also highlight some of the current challenges bioinformatics is facing in the Asia Pacific region and conclude our report with the announcement of APBioNet’s 100 BioDatabases (BioDB100) initiative. BioDB100 will comply with the database criteria set out earlier in our proposal for Minimum Information about a Bioinformatics and Investigation (MIABi), setting the standards for biocuration and bioinformatics research, on which we will report at the next InCoB, Nov. 27 – Dec. 2, 2011 at Kuala Lumpur, Malaysia.
Collapse
Affiliation(s)
- Shoba Ranganathan
- Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW, Australia.
| | | | | | | |
Collapse
|