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      Short Time-Series Expression Transcriptome Data Reveal the Gene Expression Patterns of Dairy Cow Mammary Gland as Milk Yield Decreased Process

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          Abstract

          The existing research on dairy cow mammary gland genes is extensive, but there have been few reports about dynamic changes in dairy cow mammary gland genes as milk yield decrease. For the first time, transcriptome analysis based on short time-series expression miner (STEM) and histological observations were performed using the Holstein dairy cow mammary gland to explore gene expression patterns in this process of decrease (at peak, mid-, and late lactation). Histological observations suggested that the number of mammary acinous cells at peak/mid-lactation was significantly higher than that at mid-/late lactation, and the lipid droplets area secreted by dairy cows was almost unaltered across the three stages of lactation ( p > 0.05). Totals of 882 and 1439 genes were differentially expressed at mid- and late lactation, respectively, compared to peak lactation. Function analysis showed that differentially expressed genes (DEGs) were mainly related to apoptosis and energy metabolism (fold change ≥ 2 or fold change ≤ 0.5, p-value ≤ 0.05). Transcriptome analysis based on STEM identified 16 profiles of differential gene expression patterns, including 5 significant profiles (false discovery rate, FDR ≤ 0.05). Function analysis revealed DEGs involved in milk fat synthesis were downregulated in Profile 0 and DEGs in Profile 12 associated with protein synthesis. These findings provide a foundation for future studies on the molecular mechanisms underlying mammary gland development in dairy cows.

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          Most cited references 78

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          Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.

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            STEM: a tool for the analysis of short time series gene expression data

            Background Time series microarray experiments are widely used to study dynamical biological processes. Due to the cost of microarray experiments, and also in some cases the limited availability of biological material, about 80% of microarray time series experiments are short (3–8 time points). Previously short time series gene expression data has been mainly analyzed using more general gene expression analysis tools not designed for the unique challenges and opportunities inherent in short time series gene expression data. Results We introduce the Short Time-series Expression Miner (STEM) the first software program specifically designed for the analysis of short time series microarray gene expression data. STEM implements unique methods to cluster, compare, and visualize such data. STEM also supports efficient and statistically rigorous biological interpretations of short time series data through its integration with the Gene Ontology. Conclusion The unique algorithms STEM implements to cluster and compare short time series gene expression data combined with its visualization capabilities and integration with the Gene Ontology should make STEM useful in the analysis of data from a significant portion of all microarray studies. STEM is available for download for free to academic and non-profit users at .
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              Identification of reference genes for quantitative real-time PCR in the bovine mammary gland during the lactation cycle.

              Achieving greater understanding of the genomic influence on milk synthesis in dairy cows represents a daunting challenge. Bovine-specific microarrays have allowed for high-throughput gene expression analysis of the mammary transcriptome. However, real-time PCR (qPCR) still represents the method of choice for accurate expression profiling of small numbers of genes and verification of key microarray relationships. This method is extremely sensitive but requires data normalization to account for analytical errors. Ideally, expression of genes used as internal controls should not be affected by specific treatments or physiological state. Mammary biopsies were collected from five cows each at -15, 1, 15, 30, 60, 120, and 240 days relative to parturition for gene expression profiling. We evaluated expression of nine genes (RPS9, ACTB, GAPD, GTP, ITGB4BP, MRPL39, RPS23, RPS15, and UXT) that could serve as internal controls in mammary tissue using qPCR. Due to gradual increases in mammary RNA concentration (mug/mg tissue) over lactation, all genes investigated experienced a dilution effect. We used pairwise comparison of expression ratios to analyze the reliability of these genes as internal controls. UXT, RPS9, and RPS15 had the most stable expression ratios across cow and time. We also assessed co-regulation among genes through network analysis. Network analysis suggested co-regulation among most of the genes examined, with MYC playing a central role. Pairwise comparison was suitable for finding appropriate internal controls in mammary gland tissue. Results showed that the geometrical average of UXT, RPS9, and RPS15 expression could be used as internal control for longitudinal mammary gene expression profiling.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Genes (Basel)
                Genes (Basel)
                genes
                Genes
                MDPI
                2073-4425
                20 June 2021
                June 2021
                : 12
                : 6
                Affiliations
                [1 ]College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China; dx120170088@ 123456yzu.edu.cn (Y.F.); ZiyinHan@ 123456126.com (Z.H.); dx120180094@ 123456yzu.edu.cn (X.L.); arbabtor@ 123456yahoo.com (A.A.I.A.); drmudasirnazar457@ 123456gmail.com (M.N.)
                [2 ]Joint International Research Laboratory of Agriculture & Agri-Product Safety, Ministry of Education, Yangzhou University, Yangzhou 225009, China
                [3 ]Jiangsu Co-Innovation Center for the Prevention and Control of Important Animal Infectious Diseases and Zoonoses, Yangzhou University College of Veterinary Medicine, Yangzhou 225009, China; yangyi@ 123456yzu.edu.cn
                Author notes
                [* ]Correspondence: yzp@ 123456yzu.edu.cn ; Tel.: +86-0514-87979269
                Article
                genes-12-00942
                10.3390/genes12060942
                8235497
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

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