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

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Abstract

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.

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Acknowledgments

We wish to thank the mothers who willingly donated breast milk. AW was supported by a Deakin Postgraduate Scholarship.

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Sharp, J.A., Lefèvre, C., Watt, A. et al. Analysis of human breast milk cells: gene expression profiles during pregnancy, lactation, involution, and mastitic infection. Funct Integr Genomics 16, 297–321 (2016). https://doi.org/10.1007/s10142-016-0485-0

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