Analysis of human breast milk cells: gene expression profiles during pregnancy, lactation, involution, and mastitic infection

Sharp, Julie A., Lefèvre, Christophe, Watt, Ashalyn and Nicholas, Kevin R. 2016, Analysis of human breast milk cells: gene expression profiles during pregnancy, lactation, involution, and mastitic infection, Functional and integrative genomics, vol. 16, no. 3, pp. 297-321, doi: 10.1007/s10142-016-0485-0.

Attached Files
Name Description MIMEType Size Downloads

Title Analysis of human breast milk cells: gene expression profiles during pregnancy, lactation, involution, and mastitic infection
Author(s) Sharp, Julie A.ORCID iD for Sharp, Julie A. orcid.org/0000-0002-4481-5223
Lefèvre, Christophe
Watt, Ashalyn
Nicholas, Kevin R.
Journal name Functional and integrative genomics
Volume number 16
Issue number 3
Start page 297
End page 321
Total pages 25
Publisher Springer
Place of publication Berlin, Germany
Publication date 2016-05
ISSN 1438-7948
Keyword(s) Human
Lactation
Mammary gland
Mastitis
Milk
Transcriptome
Science & Technology
Life Sciences & Biomedicine
Genetics & Heredity
MAMMARY EPITHELIAL-CELLS
NF-KAPPA-B
STEM-CELLS
ALPHA-LACTALBUMIN
GLAND INVOLUTION
MESSENGER-RNA
PERIPHERAL-BLOOD
AMNIOTIC-FLUID
GROWTH-HORMONE
BETA-CASEIN
Summary The molecular processes underlying human milk production and the effects of mastitic infection are largely unknown because of limitations in obtaining tissue samples. Determination of gene expression in normal lactating women would be a significant step toward understanding why some women display poor lactation outcomes. Here, we demonstrate the utility of RNA obtained directly from human milk cells to detect mammary epithelial cell (MEC)-specific gene expression. Milk cell RNA was collected from five time points (24 h prepartum during the colostrum period, midlactation, two involutions, and during a bout of mastitis) in addition to an involution series comprising three time points. Gene expression profiles were determined by use of human Affymetrix arrays. Milk cells collected during milk production showed that the most highly expressed genes were involved in milk synthesis (e.g., CEL, OLAH, FOLR1, BTN1A1, and ARG2), while milk cells collected during involution showed a significant downregulation of milk synthesis genes and activation of involution associated genes (e.g., STAT3, NF-kB, IRF5, and IRF7). Milk cells collected during mastitic infection revealed regulation of a unique set of genes specific to this disease state, while maintaining regulation of milk synthesis genes. Use of conventional epithelial cell markers was used to determine the population of MECs within each sample. This paper is the first to describe the milk cell transcriptome across the human lactation cycle and during mastitic infection, providing valuable insight into gene expression of the human mammary gland.
Language eng
DOI 10.1007/s10142-016-0485-0
Field of Research 060405 Gene Expression (incl Microarray and other genome-wide approaches)
060199 Biochemistry and Cell Biology not elsewhere classified
0604 Genetics
Socio Economic Objective 920114 Reproductive System and Disorders
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Springer-Verlag Berlin Heidelberg
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082028

Document type: Journal Article
Collection: Institute for Frontier Materials
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 3 times in TR Web of Science
Scopus Citation Count Cited 3 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 158 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Wed, 09 Mar 2016, 08:54:26 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.