RNA sequencing data showing influence of p62 mRNA knockout on gene and miRNA expression
The inflammasome sensor NLRP1 is mainly expressed by epithelial cells including keratinocytes of human skin. Germline gain-of-function mutations in NLRP1 cause inflammatory skin syndromes and predispose patients to the development of cutaneous squamous cell carcinomas (cSCCs), a major type of skin cancer originating from keratinocytes. However, expression of NLRP1 is strongly reduced in cSCCs suggesting a complex role of the NLRP1 inflammasome in the development of this type of skin cancer. Suppression of NLRP1 expression in SCC cells is partially caused by an increase in p62 (SQSTM1), a cargo receptor for autophagy-dependent protein degradation. p62 is upregulated in numerous types of cancer and plays key roles in tumor development by activating different pathways. Here, we characterized the molecular mechanisms underlying suppression of NLRP1 expression by p62 in cSCCs. In SCC cells, NLRP1 activation is rescued by a knockdown or knockout of p62 mRNA and, consequently, protein expression, rather than by a knockout of p62 protein expression only. As these experiments suggest a regulation of NLRP1 by the p62 mRNA, we characterized p62 mRNA-regulated gene expression in SCC cells through RNA sequencing. In addition to mRNAs, we identified several differentially regulated microRNAs (miRs), including miR-34a-5p. These short non-coding RNAs regulate the stability or translation of mRNAs in a dynamic manner and a single miR can target multiple mRNAs. miR-34a-5p is an established tumor suppressor in different types of cancer and its expression is also downregulated in cSCCs. Although miR-34a-5p seems to bind neither p62 nor NLRP1 mRNA directly, it increases NLRP1 expression, most likely through a complex mechanism at the RNA level. In summary, our findings revealed a novel pathway regulating suppression of the inflammasome sensor NLRP1 in SCC cells by p62, which occurs at the mRNA level and is mediated by miRs, including the tumor suppressive miR-34a-5p. p62 mRNA, but not protein, regulates expression of other mRNAs and miRNAs. Differential expression analysis of RNA-seq data from SCC12 cells with a protein (p62.1) or mRNA (p62.2K) knockout, and control cells were performed. CRISPR/Cas9 knockout (p62.1 - protein only) or CRISPR/dCas9-KRAB knockout (p62.2K - mRNA and protein) of p62 in SCC12 cells Overall design: We performed an RNA sequencing experiment in SCC12 cells. Upon lentiviral transduction and selection with antibiotics, we generated stable polyclonal dCas9 control (transduction with non-targeting empty vector), p62 protein (by CRISPR/Cas9) and p62 mRNA/protein (by CRISPR/dCas9-KRAB) knockout SCC12 cells. Total RNA was isolated (quadruplet) and analyzed by RNA sequencing. We identified 156 or 106 genes (mRNAs), which were up- or down-regulated by p62 mRNA, respectively. Furthermore, 30 miRs were regulated by p62 mRNA, from which 18 were up- and 12 downregulated. Interestingly, several miRs were suppressed by a p62 mRNA knockout and, therefore, their expression is positively correlated with NLRP1 expression. This experiment indeed demonstrates that p62 mRNA is able to regulate mRNA and miR expression in a positive as well as in a negative manner. N=4. Total RNA was isolated using the miRNeasy Kit (217004, Qiagen) according to the manufacturer's instruction. For mRNA sequencing, libraries were prepared with TruSeq RNA (Illumina), while miRNA libraries utilized RealSeq®-AC (somagenics), following standard protocols at the Functional Genomics Centre Zurich (FGCZ). High Throughput Sequencing (NGS) was performed on Illumina Novaseq 6000. Raw reads were quality-checked using FastQC. mRNA reads were mapped to the human transcriptome using salmon version 1.9.0. The quantified transcripts were imported into R and collapsed to the gene level using the R package “tximport”. Mature and hairpin microRNA quantification was performed using the nf-core/smrnaseq pipeline v2.3.0 (10.5281/zenodo.3456879). Differential expression analysis of mRNA and miRNA was performed using the R package DESeq2