Premium
Integrative Analysis of Long Non‐Coding RNAs and mRNAs in Lungs of Sickle Cell Mice
Author(s) -
Kang Bum-Yong,
Chang Sarah S.,
Park Changwon,
Archer David R.,
Sutliff Roy L.,
Hart C. Michael
Publication year - 2020
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.2020.34.s1.04319
Subject(s) - kegg , biology , downregulation and upregulation , messenger rna , long non coding rna , fold change , gene , real time polymerase chain reaction , gene expression , cell growth , microbiology and biotechnology , computational biology , bioinformatics , genetics , gene ontology
Rationale Pulmonary hypertension (PH) is a serious complication of sickle cell disease (SCD) and causes significant morbidity and mortality. Emerging evidence indicates that long non‐coding RNAs (lncRNAs) play a pivotal role in biological processes such as, cellular proliferation, differentiation, and apoptosis. However, the role of lncRNA and mRNA interaction in SCD‐PH pathogenesis has not been defined. We sought to evaluate the expression profiles of lncRNAs and mRNAs in lungs of sickle cell (SS) mice that spontaneously develop PH. Methods and Results Total RNAs were isolated from lungs of SS mice and littermate control (AA) mice at age 15–17 weeks and subjected to lncRNA and mRNA expression profiling using the Arrystar™ lncRNA array (Rockville, MD). Raw signal intensities were normalized in quantile method by GeneSpring GX software (Agilent Technologies, Santa Clara, CA). Volcano plot filtering was used to screen for differentially expressed (DE) lncRNAs and mRNAs with statistical significance (Fold Change >2.0, p< 0.05). To validate DE lncRNAs and mRNAs, 5 lncRNAs and 5 mRNAs were selected for quantitative real‐time PCR (qRT‐PCR). 2302 lncRNAs and 1753 mRNAs were upregulated (Up) and 2546 lncRNAs and 1455 mRNAs were downregulated (Dn) in lungs of SS mice compared to AA mice. Database for Annotation, Visualization and Integrated Discovery (DAVID) bioinformatics analysis was used for Gene Ontology (GO) enrichment including biological process (BP), molecular function (MF), and cellular component (CC), and pathway analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. GO analysis showed that the altered mRNAs were mainly distinct to negative (Up) or positive (Dn) regulation of biological processes in BP, ion binding (Up) or organic cyclic compound binding (Dn) in MF, and membrane‐bounded organelle and vesicle (Up) and non‐membrane‐bounded organelle and intracellular organelle (Dn) in CC. Pathway analysis revealed that malaria and MAPK signaling pathway were significantly associated with upregulated mRNAs, whereas C‐type lectin receptor signaling pathway, focal adhesion, and JAK‐STAT signaling pathway were related with downregulated mRNAs. LncRNA‐mRNA co‐expression network analysis was evaluated by Coding‐Non‐coding gene Co‐expression network (CNC network) analysis and generated by Cytoscape software (The Cytoscape Consortium, San Diego, CA). Conclusions These results identify potential new targets for therapeutic intervention in SCD and provide new insights into SCD pulmonary vascular dysfunction and PH pathogenesis. Support or Funding Information NIH HL133053 (BYK)