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Predictive Metagenomic Profiling, Urine Metabolomics, and Human Marker Gene Expression as an Integrated Approach to Study Alopecia Areata

Involvement of the microbiome in many different scalp conditions has been investigated over the years. Studies on the role of the scalp microbiome in specific diseases, such as those involving hair growth alterations like non-cicatricial [androgenetic alopecia (AGA), alopecia areata (AA)] and cicatr...

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Autores principales: Pinto, Daniela, Calabrese, Francesco Maria, De Angelis, Maria, Celano, Giuseppe, Giuliani, Giammaria, Gobbetti, Marco, Rinaldi, Fabio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7201066/
https://www.ncbi.nlm.nih.gov/pubmed/32411613
http://dx.doi.org/10.3389/fcimb.2020.00146
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author Pinto, Daniela
Calabrese, Francesco Maria
De Angelis, Maria
Celano, Giuseppe
Giuliani, Giammaria
Gobbetti, Marco
Rinaldi, Fabio
author_facet Pinto, Daniela
Calabrese, Francesco Maria
De Angelis, Maria
Celano, Giuseppe
Giuliani, Giammaria
Gobbetti, Marco
Rinaldi, Fabio
author_sort Pinto, Daniela
collection PubMed
description Involvement of the microbiome in many different scalp conditions has been investigated over the years. Studies on the role of the scalp microbiome in specific diseases, such as those involving hair growth alterations like non-cicatricial [androgenetic alopecia (AGA), alopecia areata (AA)] and cicatricial alopecia lichen planopilaris, are of major importance. In the present work, we highlighted the differences in microbial populations inhabiting the scalp of AA subjects and a healthy sample cohort by using an integrated approach relying on metagenomic targeted 16S sequencing analysis, urine metabolomics, and human marker gene expression. Significant differences in genera abundances (p < 0.05) were found in the hypodermis and especially the dermis layer. Based on 16S sequencing data, we explored the differences in predicted KEGG pathways and identified some significant differences in predicted pathways related to the AA pathologic condition such as flagellar, assembly, bacterial chemotaxis, mineral absorption, ABC transporters, cellular antigens, glycosaminoglycan degradation, lysosome, sphingolipid metabolism, cell division, protein digestion and absorption, and energy metabolism. All predicted pathways were significantly enhanced in AA samples compared to expression in healthy samples, with the exceptions of mineral absorption, and ABC transporters. We also determined the expression of TNF-α, FAS, KCNA3, NOD-2, and SOD-2 genes and explored the relationships between human gene expression levels and microbiome composition by Pearson's correlation analysis; here, significant correlations both positive (SOD vs. Staphylococcus, Candidatus Aquiluna) and negative (FAS and SOD2 vs. Anaerococcus, Neisseria, and Acinetobacter) were highlighted. Finally, we inspected volatile organic metabolite profiles in urinary samples and detected statistically significant differences (menthol, methanethiol, dihydrodehydro-beta-ionone, 2,5-dimethylfuran, 1,2,3,4, tetrahydro-1,5,7-trimethylnapthalene) when comparing AA and healthy subject groups. This multiple comparison approach highlighted potential traits associated with AA and their relationship with the microbiota inhabiting the scalp, opening up novel therapeutic interventions in such kind of hair growth disorders mainly by means of prebiotics, probiotics, and postbiotics.
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spelling pubmed-72010662020-05-14 Predictive Metagenomic Profiling, Urine Metabolomics, and Human Marker Gene Expression as an Integrated Approach to Study Alopecia Areata Pinto, Daniela Calabrese, Francesco Maria De Angelis, Maria Celano, Giuseppe Giuliani, Giammaria Gobbetti, Marco Rinaldi, Fabio Front Cell Infect Microbiol Cellular and Infection Microbiology Involvement of the microbiome in many different scalp conditions has been investigated over the years. Studies on the role of the scalp microbiome in specific diseases, such as those involving hair growth alterations like non-cicatricial [androgenetic alopecia (AGA), alopecia areata (AA)] and cicatricial alopecia lichen planopilaris, are of major importance. In the present work, we highlighted the differences in microbial populations inhabiting the scalp of AA subjects and a healthy sample cohort by using an integrated approach relying on metagenomic targeted 16S sequencing analysis, urine metabolomics, and human marker gene expression. Significant differences in genera abundances (p < 0.05) were found in the hypodermis and especially the dermis layer. Based on 16S sequencing data, we explored the differences in predicted KEGG pathways and identified some significant differences in predicted pathways related to the AA pathologic condition such as flagellar, assembly, bacterial chemotaxis, mineral absorption, ABC transporters, cellular antigens, glycosaminoglycan degradation, lysosome, sphingolipid metabolism, cell division, protein digestion and absorption, and energy metabolism. All predicted pathways were significantly enhanced in AA samples compared to expression in healthy samples, with the exceptions of mineral absorption, and ABC transporters. We also determined the expression of TNF-α, FAS, KCNA3, NOD-2, and SOD-2 genes and explored the relationships between human gene expression levels and microbiome composition by Pearson's correlation analysis; here, significant correlations both positive (SOD vs. Staphylococcus, Candidatus Aquiluna) and negative (FAS and SOD2 vs. Anaerococcus, Neisseria, and Acinetobacter) were highlighted. Finally, we inspected volatile organic metabolite profiles in urinary samples and detected statistically significant differences (menthol, methanethiol, dihydrodehydro-beta-ionone, 2,5-dimethylfuran, 1,2,3,4, tetrahydro-1,5,7-trimethylnapthalene) when comparing AA and healthy subject groups. This multiple comparison approach highlighted potential traits associated with AA and their relationship with the microbiota inhabiting the scalp, opening up novel therapeutic interventions in such kind of hair growth disorders mainly by means of prebiotics, probiotics, and postbiotics. Frontiers Media S.A. 2020-04-29 /pmc/articles/PMC7201066/ /pubmed/32411613 http://dx.doi.org/10.3389/fcimb.2020.00146 Text en Copyright © 2020 Pinto, Calabrese, De Angelis, Celano, Giuliani, Gobbetti and Rinaldi. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cellular and Infection Microbiology
Pinto, Daniela
Calabrese, Francesco Maria
De Angelis, Maria
Celano, Giuseppe
Giuliani, Giammaria
Gobbetti, Marco
Rinaldi, Fabio
Predictive Metagenomic Profiling, Urine Metabolomics, and Human Marker Gene Expression as an Integrated Approach to Study Alopecia Areata
title Predictive Metagenomic Profiling, Urine Metabolomics, and Human Marker Gene Expression as an Integrated Approach to Study Alopecia Areata
title_full Predictive Metagenomic Profiling, Urine Metabolomics, and Human Marker Gene Expression as an Integrated Approach to Study Alopecia Areata
title_fullStr Predictive Metagenomic Profiling, Urine Metabolomics, and Human Marker Gene Expression as an Integrated Approach to Study Alopecia Areata
title_full_unstemmed Predictive Metagenomic Profiling, Urine Metabolomics, and Human Marker Gene Expression as an Integrated Approach to Study Alopecia Areata
title_short Predictive Metagenomic Profiling, Urine Metabolomics, and Human Marker Gene Expression as an Integrated Approach to Study Alopecia Areata
title_sort predictive metagenomic profiling, urine metabolomics, and human marker gene expression as an integrated approach to study alopecia areata
topic Cellular and Infection Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7201066/
https://www.ncbi.nlm.nih.gov/pubmed/32411613
http://dx.doi.org/10.3389/fcimb.2020.00146
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