Cargando…
miR-126-3p and miR-21-5p as Hallmarks of Bio-Positive Ageing; Correlation Analysis and Machine Learning Prediction in Young to Ultra-Centenarian Sicilian Population
Human ageing can be characterized by a profile of circulating microRNAs (miRNAs), which are potentially predictors of biological age. They can be used as a biomarker of risk for age-related inflammatory outcomes, and senescent endothelial cells (ECs) have emerged as a possible source of circulating...
Autores principales: | , , , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9099697/ https://www.ncbi.nlm.nih.gov/pubmed/35563810 http://dx.doi.org/10.3390/cells11091505 |
_version_ | 1784706670486618112 |
---|---|
author | Accardi, Giulia Bono, Filippa Cammarata, Giuseppe Aiello, Anna Herrero, Maria Trinidad Alessandro, Riccardo Augello, Giuseppa Carru, Ciriaco Colomba, Paolo Costa, Maria Assunta De Vivo, Immaculata Ligotti, Mattia Emanuela Lo Curto, Alessia Passantino, Rosa Taverna, Simona Zizzo, Carmela Duro, Giovanni Caruso, Calogero Candore, Giuseppina |
author_facet | Accardi, Giulia Bono, Filippa Cammarata, Giuseppe Aiello, Anna Herrero, Maria Trinidad Alessandro, Riccardo Augello, Giuseppa Carru, Ciriaco Colomba, Paolo Costa, Maria Assunta De Vivo, Immaculata Ligotti, Mattia Emanuela Lo Curto, Alessia Passantino, Rosa Taverna, Simona Zizzo, Carmela Duro, Giovanni Caruso, Calogero Candore, Giuseppina |
author_sort | Accardi, Giulia |
collection | PubMed |
description | Human ageing can be characterized by a profile of circulating microRNAs (miRNAs), which are potentially predictors of biological age. They can be used as a biomarker of risk for age-related inflammatory outcomes, and senescent endothelial cells (ECs) have emerged as a possible source of circulating miRNAs. In this paper, a panel of four circulating miRNAs including miR-146a-5p, miR-126-3p, miR-21-5p, and miR-181a-5p, involved in several pathways related to inflammation, and ECs senescence that seem to be characteristic of the healthy ageing phenotype. The circulating levels of these miRNAs were determined in 78 healthy subjects aged between 22 to 111 years. Contextually, extracellular miR-146a-5p, miR-126-3p, miR-21-5p, and miR-181a-5p levels were measured in human ECs in vitro model, undergoing senescence. We found that the levels of the four miRNAs, using ex vivo and in vitro models, progressively increase with age, apart from ultra-centenarians that showed levels comparable to those measured in young individuals. Our results contribute to the development of knowledge regarding the identification of miRNAs as biomarkers of successful and unsuccessful ageing. Indeed, they might have diagnostic/prognostic relevance for age-related diseases. |
format | Online Article Text |
id | pubmed-9099697 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90996972022-05-14 miR-126-3p and miR-21-5p as Hallmarks of Bio-Positive Ageing; Correlation Analysis and Machine Learning Prediction in Young to Ultra-Centenarian Sicilian Population Accardi, Giulia Bono, Filippa Cammarata, Giuseppe Aiello, Anna Herrero, Maria Trinidad Alessandro, Riccardo Augello, Giuseppa Carru, Ciriaco Colomba, Paolo Costa, Maria Assunta De Vivo, Immaculata Ligotti, Mattia Emanuela Lo Curto, Alessia Passantino, Rosa Taverna, Simona Zizzo, Carmela Duro, Giovanni Caruso, Calogero Candore, Giuseppina Cells Article Human ageing can be characterized by a profile of circulating microRNAs (miRNAs), which are potentially predictors of biological age. They can be used as a biomarker of risk for age-related inflammatory outcomes, and senescent endothelial cells (ECs) have emerged as a possible source of circulating miRNAs. In this paper, a panel of four circulating miRNAs including miR-146a-5p, miR-126-3p, miR-21-5p, and miR-181a-5p, involved in several pathways related to inflammation, and ECs senescence that seem to be characteristic of the healthy ageing phenotype. The circulating levels of these miRNAs were determined in 78 healthy subjects aged between 22 to 111 years. Contextually, extracellular miR-146a-5p, miR-126-3p, miR-21-5p, and miR-181a-5p levels were measured in human ECs in vitro model, undergoing senescence. We found that the levels of the four miRNAs, using ex vivo and in vitro models, progressively increase with age, apart from ultra-centenarians that showed levels comparable to those measured in young individuals. Our results contribute to the development of knowledge regarding the identification of miRNAs as biomarkers of successful and unsuccessful ageing. Indeed, they might have diagnostic/prognostic relevance for age-related diseases. MDPI 2022-04-30 /pmc/articles/PMC9099697/ /pubmed/35563810 http://dx.doi.org/10.3390/cells11091505 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Accardi, Giulia Bono, Filippa Cammarata, Giuseppe Aiello, Anna Herrero, Maria Trinidad Alessandro, Riccardo Augello, Giuseppa Carru, Ciriaco Colomba, Paolo Costa, Maria Assunta De Vivo, Immaculata Ligotti, Mattia Emanuela Lo Curto, Alessia Passantino, Rosa Taverna, Simona Zizzo, Carmela Duro, Giovanni Caruso, Calogero Candore, Giuseppina miR-126-3p and miR-21-5p as Hallmarks of Bio-Positive Ageing; Correlation Analysis and Machine Learning Prediction in Young to Ultra-Centenarian Sicilian Population |
title | miR-126-3p and miR-21-5p as Hallmarks of Bio-Positive Ageing; Correlation Analysis and Machine Learning Prediction in Young to Ultra-Centenarian Sicilian Population |
title_full | miR-126-3p and miR-21-5p as Hallmarks of Bio-Positive Ageing; Correlation Analysis and Machine Learning Prediction in Young to Ultra-Centenarian Sicilian Population |
title_fullStr | miR-126-3p and miR-21-5p as Hallmarks of Bio-Positive Ageing; Correlation Analysis and Machine Learning Prediction in Young to Ultra-Centenarian Sicilian Population |
title_full_unstemmed | miR-126-3p and miR-21-5p as Hallmarks of Bio-Positive Ageing; Correlation Analysis and Machine Learning Prediction in Young to Ultra-Centenarian Sicilian Population |
title_short | miR-126-3p and miR-21-5p as Hallmarks of Bio-Positive Ageing; Correlation Analysis and Machine Learning Prediction in Young to Ultra-Centenarian Sicilian Population |
title_sort | mir-126-3p and mir-21-5p as hallmarks of bio-positive ageing; correlation analysis and machine learning prediction in young to ultra-centenarian sicilian population |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9099697/ https://www.ncbi.nlm.nih.gov/pubmed/35563810 http://dx.doi.org/10.3390/cells11091505 |
work_keys_str_mv | AT accardigiulia mir1263pandmir215pashallmarksofbiopositiveageingcorrelationanalysisandmachinelearningpredictioninyoungtoultracentenariansicilianpopulation AT bonofilippa mir1263pandmir215pashallmarksofbiopositiveageingcorrelationanalysisandmachinelearningpredictioninyoungtoultracentenariansicilianpopulation AT cammaratagiuseppe mir1263pandmir215pashallmarksofbiopositiveageingcorrelationanalysisandmachinelearningpredictioninyoungtoultracentenariansicilianpopulation AT aielloanna mir1263pandmir215pashallmarksofbiopositiveageingcorrelationanalysisandmachinelearningpredictioninyoungtoultracentenariansicilianpopulation AT herreromariatrinidad mir1263pandmir215pashallmarksofbiopositiveageingcorrelationanalysisandmachinelearningpredictioninyoungtoultracentenariansicilianpopulation AT alessandroriccardo mir1263pandmir215pashallmarksofbiopositiveageingcorrelationanalysisandmachinelearningpredictioninyoungtoultracentenariansicilianpopulation AT augellogiuseppa mir1263pandmir215pashallmarksofbiopositiveageingcorrelationanalysisandmachinelearningpredictioninyoungtoultracentenariansicilianpopulation AT carruciriaco mir1263pandmir215pashallmarksofbiopositiveageingcorrelationanalysisandmachinelearningpredictioninyoungtoultracentenariansicilianpopulation AT colombapaolo mir1263pandmir215pashallmarksofbiopositiveageingcorrelationanalysisandmachinelearningpredictioninyoungtoultracentenariansicilianpopulation AT costamariaassunta mir1263pandmir215pashallmarksofbiopositiveageingcorrelationanalysisandmachinelearningpredictioninyoungtoultracentenariansicilianpopulation AT devivoimmaculata mir1263pandmir215pashallmarksofbiopositiveageingcorrelationanalysisandmachinelearningpredictioninyoungtoultracentenariansicilianpopulation AT ligottimattiaemanuela mir1263pandmir215pashallmarksofbiopositiveageingcorrelationanalysisandmachinelearningpredictioninyoungtoultracentenariansicilianpopulation AT locurtoalessia mir1263pandmir215pashallmarksofbiopositiveageingcorrelationanalysisandmachinelearningpredictioninyoungtoultracentenariansicilianpopulation AT passantinorosa mir1263pandmir215pashallmarksofbiopositiveageingcorrelationanalysisandmachinelearningpredictioninyoungtoultracentenariansicilianpopulation AT tavernasimona mir1263pandmir215pashallmarksofbiopositiveageingcorrelationanalysisandmachinelearningpredictioninyoungtoultracentenariansicilianpopulation AT zizzocarmela mir1263pandmir215pashallmarksofbiopositiveageingcorrelationanalysisandmachinelearningpredictioninyoungtoultracentenariansicilianpopulation AT durogiovanni mir1263pandmir215pashallmarksofbiopositiveageingcorrelationanalysisandmachinelearningpredictioninyoungtoultracentenariansicilianpopulation AT carusocalogero mir1263pandmir215pashallmarksofbiopositiveageingcorrelationanalysisandmachinelearningpredictioninyoungtoultracentenariansicilianpopulation AT candoregiuseppina mir1263pandmir215pashallmarksofbiopositiveageingcorrelationanalysisandmachinelearningpredictioninyoungtoultracentenariansicilianpopulation |