Cross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation Process
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Date
2018-07-12
Authors
Farhadian, Mohammad
Rafat, Seyed A
Hasanpur, Karim
Ebrahimi, Mansour
Ebrahimie, Esmaeil
Journal Title
Journal ISSN
Volume Title
Publisher
Frontiers Media
Rights
Copyright © 2018 Farhadian, Rafat, Hasanpur, Ebrahimi and Ebrahimie. 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.
Rights Holder
Copyright © 2018 Farhadian, Rafat, Hasanpur, Ebrahimi and Ebrahimie.
Abstract
Lactation, a physiologically complex process, takes place in mammary gland after
parturition. The expression profile of the effective genes in lactation has not
comprehensively been elucidated. Herein, meta-analysis, using publicly available
microarray data, was conducted identify the differentially expressed genes (DEGs)
between pre- and post-peak milk production. Three microarray datasets of Rat, Bos
Taurus, and Tammar wallaby were used. Samples related to pre-peak (n = 85) and
post-peak (n = 24) milk production were selected. Meta-analysis revealed 31 DEGs
across the studied species. Interestingly, 10 genes, including MRPS18B, SF1, UQCRC1,
NUCB1, RNF126, ADSL, TNNC1, FIS1, HES5 and THTPA, were not detected in
original studies that highlights meta-analysis power in biosignature discovery. Common
target and regulator analysis highlighted the high connectivity of CTNNB1, CDD4 and
LPL as gene network hubs. As data originally came from three different species, to
check the effects of heterogeneous data sources on DEGs, 10 attribute weighting
(machine learning) algorithms were applied. Attribute weighting results showed that
the type of organism had no or little effect on the selected gene list. Systems biology
analysis suggested that these DEGs affect the milk production by improving the immune
system performance and mammary cell growth. This is the first study employing
both meta-analysis and machine learning approaches for comparative analysis of
gene expression pattern of mammary glands in two important time points of lactation
process. The finding may pave the way to use of publically available to elucidate the
underlying molecular mechanisms of physiologically complex traits such as lactation in
mammals.
Description
Note: This article was submitted to
Bioinformatics and Computational
Biology,
a section of the journal
Frontiers in Genetics. 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.
Keywords
milk production, meta-analysis, microarray, gene ontology, gene network, data mining
Citation
Farhadian, M., Rafat, S.A., Hasanpur, K., Ebrahimi, M. and Ebrahimie, E., (2018). Cross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation Process. Front. Genet. 9:235. doi: 10.3389/fgene.2018.00235