Gene regulatory networks in lactation: identification of global principles using bioinformatics.
BACKGROUND
The molecular events underlying mammary development during pregnancy, lactation, and involution are incompletely understood.


RESULTS
Mammary gland microarray data, cellular localization data, protein-protein interactions, and literature-mined genes were integrated and analyzed using statistics, principal component analysis, gene ontology analysis, pathway analysis, and network analysis to identify global biological principles that govern molecular events during pregnancy, lactation, and involution.


CONCLUSIONS
Several key principles were derived: (1) nearly a third of the transcriptome fluctuates to build, run, and disassemble the lactation apparatus; (2) genes encoding the secretory machinery are transcribed prior to lactation; (3) the diversity of the endogenous portion of the milk proteome is derived from fewer than 100 transcripts; (4) while some genes are differentially transcribed near the onset of lactation, the lactation switch is primarily post-transcriptionally mediated; (5) the secretion of materials during lactation occurs not by up-regulation of novel genomic functions, but by widespread transcriptional suppression of functions such as protein degradation and cell-environment communication; (6) the involution switch is primarily transcriptionally mediated; and (7) during early involution, the transcriptional state is partially reverted to the pre-lactation state. A new hypothesis for secretory diminution is suggested - milk production gradually declines because the secretory machinery is not transcriptionally replenished. A comprehensive network of protein interactions during lactation is assembled and new regulatory gene targets are identified. Less than one fifth of the transcriptionally regulated nodes in this lactation network have been previously explored in the context of lactation. Implications for future research in mammary and cancer biology are discussed.
Department of Food Science and Technology, University of California, One Shields Ave,, Davis, CA 95616, USA. dglemay@ucdavis.edu
Danielle G
DG
Lemay
Margaret C
MC
Neville
Michael C
MC
Rudolph
Katherine S
KS
Pollard
J Bruce
JB
German
D001940:Breast / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D005260:Female; D005786:Gene Expression Regulation / Q000502:physiology*; D006801:Humans; D007774:Lactation / Q000378:metabolism*; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
D020543:Proteome