Cross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation Process
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-07-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fgene.2018.00235/full |
| _version_ | 1818039242726572032 |
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| author | Mohammad Farhadian Seyed A. Rafat Karim Hasanpur Mansour Ebrahimi Esmaeil Ebrahimie Esmaeil Ebrahimie Esmaeil Ebrahimie Esmaeil Ebrahimie |
| author_facet | Mohammad Farhadian Seyed A. Rafat Karim Hasanpur Mansour Ebrahimi Esmaeil Ebrahimie Esmaeil Ebrahimie Esmaeil Ebrahimie Esmaeil Ebrahimie |
| author_sort | Mohammad Farhadian |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T07:55:31Z |
| format | Article |
| id | doaj.art-c2a43bfa53dc46c2aabdbd9198771c72 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-10T07:55:31Z |
| publishDate | 2018-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-c2a43bfa53dc46c2aabdbd9198771c722022-12-22T01:56:56ZengFrontiers Media S.A.Frontiers in Genetics1664-80212018-07-01910.3389/fgene.2018.00235333395Cross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation ProcessMohammad Farhadian0Seyed A. Rafat1Karim Hasanpur2Mansour Ebrahimi3Esmaeil Ebrahimie4Esmaeil Ebrahimie5Esmaeil Ebrahimie6Esmaeil Ebrahimie7Department of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz, IranDepartment of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz, IranDepartment of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz, IranDepartment of Biology, University of Qom, Qom, IranAdelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, AustraliaInstitute of Biotechnology, Shiraz University, Shiraz, IranDivision of Information Technology, Engineering and the Environment, School of Information Technology & Mathematical Sciences, University of South Australia, Adelaide, SA, AustraliaSchool of Biological Sciences, Faculty of Science and Engineering, Flinders University, Adelaide, SA, AustraliaLactation, 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.https://www.frontiersin.org/article/10.3389/fgene.2018.00235/fullmilk productionmeta-analysismicroarraygene ontologygene networkdata mining |
| spellingShingle | Mohammad Farhadian Seyed A. Rafat Karim Hasanpur Mansour Ebrahimi Esmaeil Ebrahimie Esmaeil Ebrahimie Esmaeil Ebrahimie Esmaeil Ebrahimie Cross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation Process Frontiers in Genetics milk production meta-analysis microarray gene ontology gene network data mining |
| title | Cross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation Process |
| title_full | Cross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation Process |
| title_fullStr | Cross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation Process |
| title_full_unstemmed | Cross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation Process |
| title_short | Cross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation Process |
| title_sort | cross species meta analysis of transcriptomic data in combination with supervised machine learning models identifies the common gene signature of lactation process |
| topic | milk production meta-analysis microarray gene ontology gene network data mining |
| url | https://www.frontiersin.org/article/10.3389/fgene.2018.00235/full |
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