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dos Santos GA, Magdaleno GDV, de Magalhães JP. Evidence of a pan-tissue decline in stemness during human aging. Aging (Albany NY) 2024; 16:5796-5810. [PMID: 38604248 PMCID: PMC11042951 DOI: 10.18632/aging.205717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 02/02/2024] [Indexed: 04/13/2024]
Abstract
Despite their biological importance, the role of stem cells in human aging remains to be elucidated. In this work, we applied a machine learning methodology to GTEx transcriptome data and assigned stemness scores to 17,382 healthy samples from 30 human tissues aged between 20 and 79 years. We found that ~60% of the studied tissues exhibit a significant negative correlation between the subject's age and stemness score. The only significant exception was the uterus, where we observed an increased stemness with age. Moreover, we observed that stemness is positively correlated with cell proliferation and negatively correlated with cellular senescence. Finally, we also observed a trend that hematopoietic stem cells derived from older individuals might have higher stemness scores. In conclusion, we assigned stemness scores to human samples and show evidence of a pan-tissue loss of stemness during human aging, which adds weight to the idea that stem cell deterioration may contribute to human aging.
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Affiliation(s)
- Gabriel Arantes dos Santos
- Laboratory of Medical Investigation (LIM55), Urology Department, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo 01246 903, Brazil
- Genomics of Ageing and Rejuvenation Lab, Institute of Inflammation and Ageing, University of Birmingham, Birmingham B15 2WB, United Kingdom
| | | | - João Pedro de Magalhães
- Genomics of Ageing and Rejuvenation Lab, Institute of Inflammation and Ageing, University of Birmingham, Birmingham B15 2WB, United Kingdom
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Mikaeloff F, Svensson Akusjärvi S, Ikomey GM, Krishnan S, Sperk M, Gupta S, Magdaleno GDV, Escós A, Lyonga E, Okomo MC, Tagne CT, Babu H, Lorson CL, Végvári Á, Banerjea AC, Kele J, Hanna LE, Singh K, de Magalhães JP, Benfeitas R, Neogi U. Trans cohort metabolic reprogramming towards glutaminolysis in long-term successfully treated HIV-infection. Commun Biol 2022; 5:27. [PMID: 35017663 PMCID: PMC8752762 DOI: 10.1038/s42003-021-02985-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 12/16/2021] [Indexed: 12/20/2022] Open
Abstract
Despite successful combination antiretroviral therapy (cART), persistent low-grade immune activation together with inflammation and toxic antiretroviral drugs can lead to long-lasting metabolic flexibility and adaptation in people living with HIV (PLWH). Our study investigated alterations in the plasma metabolic profiles by comparing PLWH on long-term cART(>5 years) and matched HIV-negative controls (HC) in two cohorts from low- and middle-income countries (LMIC), Cameroon, and India, respectively, to understand the system-level dysregulation in HIV-infection. Using untargeted and targeted LC-MS/MS-based metabolic profiling and applying advanced system biology methods, an altered amino acid metabolism, more specifically to glutaminolysis in PLWH than HC were reported. A significantly lower level of neurosteroids was observed in both cohorts and could potentiate neurological impairments in PLWH. Further, modulation of cellular glutaminolysis promoted increased cell death and latency reversal in pre-monocytic HIV-1 latent cell model U1, which may be essential for the clearance of the inducible reservoir in HIV-integrated cells.
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Affiliation(s)
- Flora Mikaeloff
- The Systems Virology Lab, Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institute, ANA Futura, Campus Flemingsberg, Stockholm, Sweden
| | - Sara Svensson Akusjärvi
- The Systems Virology Lab, Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institute, ANA Futura, Campus Flemingsberg, Stockholm, Sweden
| | - George Mondinde Ikomey
- Center for the Study and Control of Communicable Diseases (CSCCD), Faculty of Medicine and Biomedical Sciences, University of Yaoundé 1, P.O. Box. 8445, Yaoundé, Cameroon
- Department of Microbiology, Haematology, Parasitology and Infectious Disease, Faculty of Medicine and Biomedical Sciences, University of Yaoundé 1, Yaoundé, Cameroon
| | - Shuba Krishnan
- The Systems Virology Lab, Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institute, ANA Futura, Campus Flemingsberg, Stockholm, Sweden
| | - Maike Sperk
- The Systems Virology Lab, Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institute, ANA Futura, Campus Flemingsberg, Stockholm, Sweden
| | - Soham Gupta
- The Systems Virology Lab, Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institute, ANA Futura, Campus Flemingsberg, Stockholm, Sweden
| | - Gustavo Daniel Vega Magdaleno
- Integrative Genomics of Ageing Group, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Alejandra Escós
- The Systems Virology Lab, Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institute, ANA Futura, Campus Flemingsberg, Stockholm, Sweden
| | - Emilia Lyonga
- Center for the Study and Control of Communicable Diseases (CSCCD), Faculty of Medicine and Biomedical Sciences, University of Yaoundé 1, P.O. Box. 8445, Yaoundé, Cameroon
- Department of Microbiology, Haematology, Parasitology and Infectious Disease, Faculty of Medicine and Biomedical Sciences, University of Yaoundé 1, Yaoundé, Cameroon
| | - Marie Claire Okomo
- Center for the Study and Control of Communicable Diseases (CSCCD), Faculty of Medicine and Biomedical Sciences, University of Yaoundé 1, P.O. Box. 8445, Yaoundé, Cameroon
- Department of Microbiology, Haematology, Parasitology and Infectious Disease, Faculty of Medicine and Biomedical Sciences, University of Yaoundé 1, Yaoundé, Cameroon
| | - Claude Tayou Tagne
- Department of Microbiology, Haematology, Parasitology and Infectious Disease, Faculty of Medicine and Biomedical Sciences, University of Yaoundé 1, Yaoundé, Cameroon
| | - Hemalatha Babu
- Department of HIV/AIDS, National Institute for Research in Tuberculosis, ICMR, Chennai, 600031, India
- Division of Microbiology and Immunology, Yerkes National Primate Research Center, Emory Vaccine Center, Emory University, Atlanta, GA, 30329, USA
| | - Christian L Lorson
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
- Department of Veterinary Pathobiology, University of Missouri, Columbia, MO, 65211, USA
| | - Ákos Végvári
- Division of Chemistry I, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Akhil C Banerjea
- National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi, India
| | - Julianna Kele
- Department of Physiology and Pharmacology, Neurovascular Biology and Health, Karolinska Institutet, 171 77, Stockholm, Sweden
| | - Luke Elizabeth Hanna
- Department of HIV/AIDS, National Institute for Research in Tuberculosis, ICMR, Chennai, 600031, India
| | - Kamal Singh
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
- Department of Veterinary Pathobiology, University of Missouri, Columbia, MO, 65211, USA
| | - João Pedro de Magalhães
- Integrative Genomics of Ageing Group, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Rui Benfeitas
- National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, S-10691, Stockholm, Sweden
| | - Ujjwal Neogi
- The Systems Virology Lab, Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institute, ANA Futura, Campus Flemingsberg, Stockholm, Sweden.
- Manipal Institute of Virology (MIV), Manipal Academy of Higher Education, Manipal, Karnataka, India.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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