PLoS ONEGenomicsPeer-reviewed journal article
Limited differential expression of miRNAs and other small RNAs in LPS-stimulated human monocytes
Daniel LuiD, Tracy YamawakiiD, Hong ZhouiD, Wen-Yu Chou, Mark Chhoa, Edwin Lamas, Sydney M. EscobariD, Heather A. ArnettiD, Huanying Ge, Todd Juan, Songli WangiD, Chi-Ming LiiD
Abstract
Monocytes are a distinct subset of myeloid cells with diverse functions in early inflammatory immune modulation. While previous studies have surveyed the role of microRNA regulation on different myeloid cell lines and primary cultures, the time-dependent kinetics of inflammatory stimulation on miRNA expression and the relationship between miRNA-to-target-RNA expression have not been comprehensively profiled in monocytes. In this study, we use next-generation sequencing and RT-PCR assays to analyze the non-coding small RNA transcriptome of unstimulated and lipopolysaccharide (LPS)-stimulated monocytes at 6 and 24 hours. We identified a miRNA signature consisting of five mature miRNAs (hsa-mir-146a, hsa-mir-155, hsa-mir-9, hsa-mir-147b, and hsa-mir-193a) upregulated by LPS-stimulated monocytes after 6 hours and found that most miRNAs were also upregulated after 24 hours of stimulation. Only one miRNA gene was down-regulated and no other small RNAs were found dysregulated in monocytes after LPS treatment. In addition, novel tRNA-derived fragments were also discovered in monocytes, although none showed significant changes upon LPS stimulation. Interrogation of validated miRNA targets by transcriptomic analysis revealed that the majority of differential mRNA expression was maintained in LPS-stimulated monocytes although few of miRNA targets seemed to obtain heterogeneous expression pattern along the treatment. Our findings reveal a potential role by which selective miRNA upregulation and stable expression of other small RNAs enable monocytes to develop finely tuned cellular responses during acute inflammation.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: experimental assay
- Citations
- 13
- FWCI
- 0.79
- Field percentile
- top 39%
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