Transcriptome signature of the lactation process, identified by meta-analysis of microarray and RNA-Seq data

Abstract

Lactation plays the crucial role in mammals' life. Uncovering the transcriptome signature of lactation process helps to understand the molecular basis of milk production. To identify the genes that express differentially between early and late lactation, publicly available microarray transcriptomic datasets of dairy cattle were investigated and the array results were validated by a next-generation sequencing dataset (RNA-Seq data from sheep). Limma and edgeR packages were used for the analysis of the microarray and RNA-Seq datasets, respectively. Five common differentially expressed genes (DEGs), namely glutathione s-transferase mu 3 (GSTM3 ), EGF containing fibulin-like extracellular matrix protein 1(EFEMP1 ), fibulin 1(FBLN1 ), gelsolin (GSN ), and fibrinogen-like 2 (FGL2 ), were identified. The involvement of EFEMP1 in the lactation process has been reported for the first time. The identified DEGs are involved in the development of the immune system and cell differentiation of the mammary gland. A gene ontology network analysis revealed the key role of the GSN gene in the regulation of two important functions of actin nucleation and barbed-end actin filament capping. The gene ontology enrichment analysis showed that the function of calcium ion binding is statistically (P < 0.05) enriched by the identified transcriptomic signature. The approach presented in this study provides an integrative framework for finding the signature of the lactation process by utilizing global transcriptome data, gene ontology (GO) regulatory network, and enrichment analysis.

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

“…These animals transfer some immunity-related elements to their milk in order to develop their youths‘ immune system and to protect themselves from infectious disease such as mastitis ( Hasselbalch et al, 1996 ; Thompson et al, 2000 ). The developed gene signature is involved in activation of immune system and propagation of mammary gland cells as observed in other mammals ( Farhadian et al, 2018 ).…”

“…So, elucidating the genes influencing each lactation time point can assist the animal breeders to accelerate the genetic improvement of dairy animals in breeding programs. Gene expression profiling of milk at different stages of lactation may reflect the molecular events of mammary glands ( Farhadian et al, 2018 ). To provide a better understanding of milk production, unraveling molecular events in mammary glands is necessary.…”

“…Important biological processes are often precisely conserved across related species ( McCarroll et al, 2004 ; Wang and Rekaya, 2009 ). Meta-analysis and machine learning have the potential to uncover the common biosignature among mammals ( Shekoofa et al, 2014 ; Ebrahimie et al, 2018 ; Farhadian et al, 2018 ; Sharifi et al, 2018 ). Recently, with availability of cross-species data, meta-analysis has been performed on multiple species ( Lu et al, 2009 ).…”

A Transcriptomic Meta-Analysis Identifies Differentially Expressed Genes across Different Types of Cancer and Their Association with Checkpoint Inhibitor Immunotherapy

“…The late-stage (meta-analysis) data integration approach utilized in this study (12), where the preprocessing and normalization of each experiment is implemented before, then merging the results of the analyses at the end step (12). This approach would minimize potential background discrepancies as a result of variation of the platforms and processing method of each study experiment (preparation and quality of the mRNA used, cDNA quality and the methodological variation in the hybridization) (13).…”

Unified Transcriptomic Signature of Arbuscular Mycorrhiza Colonization in Roots of Medicago truncatula by Integration of Machine Learning, Promoter Analysis, and Direct Merging Meta-Analysis

“…The emerging field of meta-analysis may solve the issue of merging different experiments to identify a unique biosignature of Medicago root response to AM inoculation. Cross-species meta-analysis of transcriptomic data has received increased attention in recent years due to the advances in pattern discovery and meta-analysis models (Tromas et al, 2012 ; Farhadian et al, 2018b ). Meta-analysis enables the combination of expression datasets and is highly advantageous in increasing statistical power to detect biological phenomena from studies with a restricted sample size (Johnson et al, 2007 ).…”