Cross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation Process

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.

Transcriptome signature of the lactation process in a fat-tailed sheep identifies with integrative approach of RNA-Seq and Supervised Machine Learning models

“…Milk fat global is the most important part of the milk and some milk fat globules secreted from the cytoplasm [56]. The enriched binding term in milk refers to binding some mineral and enzyme activity in the milk [57,58]. Results of GO analysis confirmed the functional role of the DEGs on milk production.…”

Genome‐wide identification of runs of homozygosity islands in the Gyr breed (Bos indicus)

“…VAMP8 is one of the most important genes expressed in secretory tissues such as the mammary gland. Its expression increases the production of milk by secretory cells of the mammary gland at the beginning of lactation (Farhadian, Rafat, Hasanpur, Ebrahimi, & Ebrahimie, ). In addition, LZTS2 was found to be involved in milk fat composition in a study on Italian Holstein cows (Palombo et al, ).…”

Non‐coding and coding genomic variants distinguish prostate cancer, castration‐resistant prostate cancer, familial prostate cancer, and metastatic castration‐resistant prostate cancer from each other

“…Compared with analyses of single variant associations, it is probable that the combined coding and non‐coding variants indicate the risk of prostate cancer more accurately. Advances in application of machine learning models such as decision tree algorithms, attribute weighting models, association rule mining, SVM, and neural networks and more importantly, integration of machine learning and meta‐analysis provides a new platform for biomarker discovery. It should be noted that racial and ethnic differences exist in advanced prostate cancer where multiple combinations of variants can lead to castration‐resistant prostate cancer and metastatic castration‐resistant prostate cancer.…”