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MicroRNA roles in signalling during lactation: an insight from differential expression, time course and pathway analyses of deep sequence data
The study examined microRNA (miRNA) expression and regulatory patterns during an entire bovine lactation cycle. Total RNA from milk fat samples collected at the lactogenesis (LAC, day1 [D1] and D7), galactopoiesis (GAL, D30, D70, D130, D170 and D230) and involution (INV, D290 and when milk productio...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5357959/ https://www.ncbi.nlm.nih.gov/pubmed/28317898 http://dx.doi.org/10.1038/srep44605 |
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author | Do, Duy N. Li, Ran Dudemaine, Pier-Luc Ibeagha-Awemu, Eveline M. |
author_facet | Do, Duy N. Li, Ran Dudemaine, Pier-Luc Ibeagha-Awemu, Eveline M. |
author_sort | Do, Duy N. |
collection | PubMed |
description | The study examined microRNA (miRNA) expression and regulatory patterns during an entire bovine lactation cycle. Total RNA from milk fat samples collected at the lactogenesis (LAC, day1 [D1] and D7), galactopoiesis (GAL, D30, D70, D130, D170 and D230) and involution (INV, D290 and when milk production dropped to 5 kg/day) stages from 9 cows was used for miRNA sequencing. A total of 475 known and 238 novel miRNAs were identified. Fifteen abundantly expressed miRNAs across lactation stages play regulatory roles in basic metabolic, cellular and immunological functions. About 344, 366 and 209 miRNAs were significantly differentially expressed (DE) between GAL and LAC, INV and GAL, and INV and LAC stages, respectively. MiR-29b/miR-363 and miR-874/miR-6254 are important mediators for transition signals from LAC to GAL and from GAL to INV, respectively. Moreover, 58 miRNAs were dynamically DE in all lactation stages and 19 miRNAs were significantly time-dependently DE throughout lactation. Relevant signalling pathways for transition between lactation stages are involved in apoptosis (PTEN and SAPK/JNK), intracellular signalling (protein kinase A, TGF-β and ERK5), cell cycle regulation (STAT3), cytokines, hormones and growth factors (prolactin, growth hormone and glucocorticoid receptor). Overall, our data suggest diverse, temporal and physiological signal-dependent regulatory and mediator functions for miRNAs during lactation. |
format | Online Article Text |
id | pubmed-5357959 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-53579592017-03-22 MicroRNA roles in signalling during lactation: an insight from differential expression, time course and pathway analyses of deep sequence data Do, Duy N. Li, Ran Dudemaine, Pier-Luc Ibeagha-Awemu, Eveline M. Sci Rep Article The study examined microRNA (miRNA) expression and regulatory patterns during an entire bovine lactation cycle. Total RNA from milk fat samples collected at the lactogenesis (LAC, day1 [D1] and D7), galactopoiesis (GAL, D30, D70, D130, D170 and D230) and involution (INV, D290 and when milk production dropped to 5 kg/day) stages from 9 cows was used for miRNA sequencing. A total of 475 known and 238 novel miRNAs were identified. Fifteen abundantly expressed miRNAs across lactation stages play regulatory roles in basic metabolic, cellular and immunological functions. About 344, 366 and 209 miRNAs were significantly differentially expressed (DE) between GAL and LAC, INV and GAL, and INV and LAC stages, respectively. MiR-29b/miR-363 and miR-874/miR-6254 are important mediators for transition signals from LAC to GAL and from GAL to INV, respectively. Moreover, 58 miRNAs were dynamically DE in all lactation stages and 19 miRNAs were significantly time-dependently DE throughout lactation. Relevant signalling pathways for transition between lactation stages are involved in apoptosis (PTEN and SAPK/JNK), intracellular signalling (protein kinase A, TGF-β and ERK5), cell cycle regulation (STAT3), cytokines, hormones and growth factors (prolactin, growth hormone and glucocorticoid receptor). Overall, our data suggest diverse, temporal and physiological signal-dependent regulatory and mediator functions for miRNAs during lactation. Nature Publishing Group 2017-03-20 /pmc/articles/PMC5357959/ /pubmed/28317898 http://dx.doi.org/10.1038/srep44605 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Do, Duy N. Li, Ran Dudemaine, Pier-Luc Ibeagha-Awemu, Eveline M. MicroRNA roles in signalling during lactation: an insight from differential expression, time course and pathway analyses of deep sequence data |
title | MicroRNA roles in signalling during lactation: an insight from differential expression, time course and pathway analyses of deep sequence data |
title_full | MicroRNA roles in signalling during lactation: an insight from differential expression, time course and pathway analyses of deep sequence data |
title_fullStr | MicroRNA roles in signalling during lactation: an insight from differential expression, time course and pathway analyses of deep sequence data |
title_full_unstemmed | MicroRNA roles in signalling during lactation: an insight from differential expression, time course and pathway analyses of deep sequence data |
title_short | MicroRNA roles in signalling during lactation: an insight from differential expression, time course and pathway analyses of deep sequence data |
title_sort | microrna roles in signalling during lactation: an insight from differential expression, time course and pathway analyses of deep sequence data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5357959/ https://www.ncbi.nlm.nih.gov/pubmed/28317898 http://dx.doi.org/10.1038/srep44605 |
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