An Integrative Transcriptome Subtraction Strategy to Identify Human lncRNAs That Specifically Play a Role in Activation of Human Hepatic Stellate Cells.
Yonghe MaJamie HarrisPing LiChengfei JiangHang SunHaiming CaoPublished in: Non-coding RNA (2024)
Fibrotic liver features excessive deposition of extracellular matrix (ECM), primarily produced from "activated" hepatic stellate cells (HSCs). While targeting human HSCs (hHSCs) in fibrosis therapeutics shows promise, the overall understanding of hHSC activation remains limited, in part because it is very challenging to define the role of human long non-coding RNAs (lncRNAs) in hHSC activation. To address this challenge, we identified another cell type that acts via a diverse gene network to promote fibrogenesis. Then, we identified the lncRNAs that were differentially regulated in activated hHSCs and the other profibrotic cell. Next, we conducted concurrent analysis to identify those lncRNAs that were specifically involved in fibrogenesis. We tested and confirmed that transdifferentiation of vascular smooth muscle cells (VSMCs) represents such a process. By overlapping TGFβ-regulated lncRNAs in multiple sets of hHSCs and VSMCs, we identified a highly selected list of lncRNA candidates that could specifically play a role in hHSC activation. We experimentally characterized one human lncRNA, named CARMN, which was significantly regulated by TGFβ in all conditions above. CARMN knockdown significantly reduced the expression levels of a panel of marker genes for hHSC activation, as well as the levels of ECM deposition and hHSC migration. Conversely, gain of function of CARMN using CRISPR activation (CRISPR-a) yielded the completely opposite effects. Taken together, our work addresses a bottleneck in identifying human lncRNAs that specifically play a role in hHSC activation and provides a framework to effectively select human lncRNAs with significant pathophysiological role.
Keyphrases
- endothelial cells
- induced pluripotent stem cells
- long non coding rna
- extracellular matrix
- genome wide
- vascular smooth muscle cells
- pluripotent stem cells
- network analysis
- gene expression
- poor prognosis
- body mass index
- squamous cell carcinoma
- single cell
- cell death
- oxidative stress
- bone marrow
- magnetic resonance imaging
- genome wide analysis
- machine learning
- artificial intelligence
- deep learning
- computed tomography
- big data
- locally advanced
- cell proliferation