Data from: Digital gene expression analyses of mammary glands from meat ewes naturally infected with clinical mastitis

Clinical mastitis in sheep has gravely restrained production performance for a long time. Knowledge of mechanisms of its pathogenesis and resistance in meat sheep mammary gland with clinical mastitis are not yet understood, especially for clinical mastitis caused by natural infection. In this work, RNA-seq was firstly used to screen differentially expressed genes (DEGs) in clinical mastitic mammary tissues (CMMTs) as compared to healthy mammary tissues (HMTs) from meat sheep flocks. We identified 420 DEGs including 316 up-regulated and 104 down-regulated genes in CMMTs. Gene Ontology annotation revealed these DEGs were mainly engaged in immune response, inflammation response, etc. Pathway enrichment showed they were primarily enriched in pathways relevant to inflammation, immune response and metabolism. Alternative splicing analysis showed most common differential splicing genes (DSGs) in CMMTs and HMTs were implicated in immune response. Immunostaining for two immune response-related proteins encoded by DEGs were mainly observed in mammary epithelium between CMMTs and HMTs, and their positive signals were more intensive in CMMTs. These findings provide experimental basis and reference for further researching the molecular genetic mechanisms particularly immune defense mechanisms of mammary gland in clinical ovine mastitis.

Identifier
DOI https://doi.org/10.5061/dryad.7mg3d80
PID https://nbn-resolving.org/urn:nbn:nl:ui:13-af-18h9
Metadata Access https://easy.dans.knaw.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:easy.dans.knaw.nl:easy-dataset:128673
Provenance
Creator Li, Tao T.; Gao, Jian F.; Zhao, Xing X.; Ma, You J.
Publisher Data Archiving and Networked Services (DANS)
Publication Year 2019
Rights info:eu-repo/semantics/openAccess; License: http://creativecommons.org/publicdomain/zero/1.0; http://creativecommons.org/publicdomain/zero/1.0
OpenAccess true
Representation
Resource Type Dataset
Discipline Life Sciences; Medicine