Investigating the Role of Non-Coding RNA in Non-Alcoholic Fatty Liver Disease.

Non-coding RNAs (ncRNAs) are RNA molecules that do not code for protein but play key roles in regulating cellular processes. NcRNAs globally affect gene expression in diverse physiological and pathological contexts. Functionally important ncRNAs act in chromatin modifications, in mRNA stabilization and translation, and in regulation of various signaling pathways. Non-alcoholic fatty liver disease (NAFLD) is a set of conditions caused by the accumulation of triacylglycerol in the liver. Studies of ncRNA in NAFLD are limited but have demonstrated that ncRNAs play a critical role in the pathogenesis of NAFLD. In this review, we summarize NAFLD's pathogenesis and clinical features, discuss current treatment options, and review the involvement of ncRNAs as regulatory molecules in NAFLD and its progression to non-alcoholic steatohepatitis (NASH). In addition, we highlight signaling pathways dysregulated in NAFLD and review their crosstalk with ncRNAs. Having a thorough understanding of the disease process's molecular mechanisms will facilitate development of highly effective diagnostic and therapeutic treatments. Such insights can also inform preventive strategies to minimize the disease's future development.

∆Np63α inhibits Rac1 activation and cancer cell invasion through suppression of PREX1.

ΔNp63α, a member of the p53 family of transcription factors, plays a critical role in maintaining the proliferative potential of stem cells in the stratified epithelium. Although ΔNp63α is considered an oncogene and is frequently overexpressed in squamous cell carcinoma, loss of ΔNp63α expression is associated with increased tumor cell invasion and metastasis. We recently identified a ΔNp63α/miR-320a/PKCγ signaling axis that regulates cancer cell invasion by inhibiting phosphorylation of the small GTPase Rac1, a master switch of cell motility that positively regulates cell invasion in multiple human cancers. In this study, we identified a novel mechanism by which ΔNp63α negatively regulates Rac1 activity, by inhibiting the expression of the Rac-specific Guanine Exchange Factor PREX1. ΔNp63α knockdown in multiple squamous cell carcinoma cell lines leads to increased Rac1 activation, which is abrogated by treatment with the Rac1 inhibitor NSC23766. Furthermore, ΔNp63α negatively regulates PREX1 transcript and protein levels. Using a Rac-GEF activation assay, we also showed that ΔNp63α reduces the levels of active PREX1. The inhibition of the PREX1-Rac1 signaling axis by ΔNp63α leads to impaired cell invasion, thus establishing the functional relevance of this link. Our results elucidated a novel molecular mechanism by which ΔNp63α negatively affects cancer cell invasion and identifies the ΔNp63α/Rac1 axis as a potential target for metastasis.

ERK3 is transcriptionally upregulated by ∆Np63α and mediates the role of ∆Np63α in suppressing cell migration in non-melanoma skin cancers.

BACKGROUND: p63, a member of the p53 gene family, is an important regulator for epithelial tissue growth and development. ∆Np63α is the main isoform of p63 and highly expressed in Non-melanoma skin cancer (NMSC). Extracellular signal-regulated kinase 3 (ERK3) is an atypical mitogen-activated protein kinase (MAPK) whose biochemical features and cellular regulation are distinct from those of conventional MAPKs such as ERK1/2. While ERK3 has been shown to be upregulated in lung cancers and head and neck cancers, in which it promotes cancer cell migration and invasion, little is known about the implication of ERK3 in NMSCs. METHODS: Fluorescent immunohistochemistry was performed to evaluate the expression levels of ΔNp63α and ERK3 in normal and NMSC specimens. Dunnett's test was performed to compare mean fluorescence intensity (MFI, indicator of expression levels) of p63 or ERK3 between normal cutaneous samples and NMSC samples. A mixed effects (ANOVA) test was used to determine the correlation between ΔNp63α and ERK3 expression levels (MFI). The regulation of ERK3 by ΔNp63α was studied by qRT-PCR, Western blot and luciferase assay. The effect of ERK3 regulation by ΔNp63α on cell migration was measured by performing trans-well migration assay. RESULTS: The expression level of ∆Np63α is upregulated in NMSCs compared to normal tissue. ERK3 level is significantly upregulated in AK and SCC in comparison to normal tissue and there is a strong positive correlation between ∆Np63α and ERK3 expression in normal skin and skin specimens of patients with AK, SCC or BCC. Further, we found that ∆Np63α positively regulates ERK3 transcript and protein levels in A431 and HaCaT skin cells, underlying the upregulation of ERK3 expression and its positive correlation with ∆Np63α in NMSCs. Moreover, similar to the effect of ∆Np63α depletion, silencing ERK3 greatly enhanced A431 cell migration. Restoration of ERK3 expression under the condition of silencing ∆Np63α counteracted the increase in cell migration induced by the depletion of ∆Np63α. Mechanistically, ERK3 inhibits the phosphorylation of Rac1 G-protein and the formation of filopodia of A431 skin SCC cells. CONCLUSIONS: ERK3 is positively regulated by ∆Np63α and mediates the role of ∆Np63α in suppressing cell migration in NMSC.

ΔNp63α suppresses cells invasion by downregulating PKCγ/Rac1 signaling through miR-320a.

ΔNp63α, a member of the p53 family of transcription factors, is overexpressed in a number of cancers and plays a role in proliferation, differentiation, migration, and invasion. ΔNp63α has been shown to regulate several microRNAs that are involved in development and cancer. We identified miRNA miR-320a as a positively regulated target of ΔNp63α. Previous studies have shown that miR-320a is downregulated in colorectal cancer and targets the small GTPase Rac1, leading to a reduction in noncanonical WNT signaling and EMT, thereby inhibiting tumor metastasis and invasion. We showed that miR-320a is a direct target of ΔNp63α. Knockdown of ΔNp63α in HaCaT and A431 cells downregulates miR-320a levels and leads to a corresponding elevation in PKCγ transcript and protein levels. Rac1 phosphorylation at Ser71 was increased in the absence of ΔNp63α, whereas overexpression of ΔNp63α reversed S71 phosphorylation of Rac1. Moreover, increased PKCγ levels, Rac1 phosphorylation and cell invasion observed upon knockdown of ΔNp63α was reversed by either overexpressing miR-320a mimic or Rac1 silencing. Finally, silencing PKCγ or treatment with the PKC inhibitor Gö6976 reversed increased Rac1 phosphorylation and cell invasion observed upon silencing ΔNp63α. Taken together, our data suggest that ΔNp63α positively regulates miR-320a, thereby inhibiting PKCγ expression, Rac1 phosphorylation, and cancer invasion.

Identification of novel ΔNp63α-regulated miRNAs using an optimized small RNA-Seq analysis pipeline.

Advances in high-throughput sequencing have enabled profiling of microRNAs (miRNAs), however, a consensus pipeline for sequencing of small RNAs has not been established. We built and optimized an analysis pipeline using Partek Flow, circumventing the need for analyzing data via scripting languages. Our analysis assessed the effect of alignment reference, normalization method, and statistical model choice on biological data. The pipeline was evaluated using sequencing data from HaCaT cells transfected with either a non-silencing control or siRNA against ΔNp63α, a p53 family member protein which is highly expressed in non-melanoma skin cancer and shown to regulate a number of miRNAs. We posit that 1) alignment and quantification to the miRBase reference provides the most robust quantitation of miRNAs, 2) normalizing sample reads via Trimmed Mean of M-values is the most robust method for accurate downstream analyses, and 3) use of the lognormal with shrinkage statistical model effectively identifies differentially expressed miRNAs. Using our pipeline, we identified previously unrecognized regulation of miRs-149-5p, 18a-5p, 19b-1-5p, 20a-5p, 590-5p, 744-5p and 93-5p by ΔNp63α. Regulation of these miRNAs was validated by RT-qPCR, substantiating our small RNA-Seq pipeline. Further analysis of these miRNAs may provide insight into ΔNp63α's role in cancer progression. By defining the optimal alignment reference, normalization method, and statistical model for analysis of miRNA sequencing data, we have established an analysis pipeline that may be carried out in Partek Flow or at the command line. In this manner, our pipeline circumvents some of the major hurdles encountered during small RNA-Seq analysis.