Introduction

The Wnt signal transduction pathways, which are essential for both embryonic development and adult homeostasis, regulate numerous fundamental cell functions, including proliferation, migration, apoptosis, stem cell renewal, and differentiation [1,2,3]. The Wnt pathways can be broadly divided into the canonical-β-catenin dependent, and the more diverse non-canonical-β-catenin independent pathways [4]. Aberrant activation of canonical Wnt signaling is associated with a number of human diseases, including a variety of malignancies such as gastric, breast, liver, and colorectal cancer (CRC) [5].

The canonical Wnt/β-catenin pathway, similarly to other signaling cascades, is initiated at the cell membrane and its primary output involves changes in gene transcriptional programs. These changes occur by regulating the expression levels, post-translational modifications, and subcellular localization, of the Wnt signaling key effector-β-catenin [2, 6, 7]. In unstimulated cells, the Wnt-signalling cascade is silenced due to the activity of a dedicated cytoplasmic destruction complex that phosphorylates β-catenin, marking it for ubiquitination and subsequent degradation. At the core of this complex are the tumour suppressor adenomatous polyposis coli (APC), the scaffold protein axin, two kinases: glycogen synthase kinase-3 (GSK-3) and casein kinase 1 (CK1), and the E3-ubiquitin ligase β-TrCP [8]. Mutations in these components may lead to uncontrolled activation of the pathway and the development of cancer [5].

The Wnt-signalling cascade initiates with the binding of secreted Wnt glycoproteins to a receptor complex composed of frizzled (Fz) and low-density lipoprotein receptor-related protein 5 or 6 (LRP5/6). The binding of a Wnt ligand to the FZD-LRP5/6 complex results in recruitment of the cytoplasmic protein dishevelled, and the subsequent formation of large “signalosomes”. This process leads to disassembly of the destruction complex and stabilization of β-catenin, which translocates into the nucleus, where it associates with T-cell factor/lymphoid enhancer-binding factor (TCF/LEF) transcription factors and other components. The resultant nuclear complex upregulates Wnt target genes and is predicted to be a preferred target for novel Wnt-signalling specific therapeutic approaches [1, 2, 9, 10].

The Wnt cascade is extremely complex and is tightly regulated at different epistatic levels depending on the cellular and environmental context [11]. However, despite decades of research, crucial mechanistic gaps throughout the pathway remain to be filled, and new pathway components are still being identified [12].

To reveal unknown regulators of Wnt/β-catenin signaling, we designed and performed a genetic screen using a Genome-wide Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9 pooled Knock-out (KO) Library, based on Wnt signaling induced cell survival. Using next-generation sequencing (NGS), we identified several new Wnt signaling inhibitors, of which, we focused on the initiation factor DExH-box protein 29 (DHX29).

Results

Establishment of a GeCKO screening system based on Wnt-induced cell survival under antibiotic selection

The aim of the study was to use a CRISPR library in order to identify novel regulators of the canonical Wnt signaling pathway. Knocking out potential pathway repressors leads to pathway activation and subsequent hygromycin resistance of the cells mediated by a reporter plasmid TCF/HSV-TK, which we have previously used as a screening tool [13] (Fig. 1A). The reporter was stably transfected into HEK293 cells, in which the β-catenin destruction complex is active and the level of Wnt signaling activity is therefore minimal. We speculated that cells in which a Wnt inhibitor is silenced would become resistant to the hygromycin B antibiotic since the hygromycin resistance gene is regulated by the TCF-binding sites. Preliminary assays were conducted to determine hygromycin concentration and screening duration by comparing a library transduced sample with a nontransduced control sample. Following 10 days of selection with 150µgr/ml hygromycin no live cells were found in the control sample (Fig. S4). Two independent screens were conducted, followed by genomic DNA and deep sequencing analysis. Examining the sgRNA frequency distribution following hygromycin selection revealed that a subset of guide-RNAs was enriched in two independent screen repeats (Fig. 1B). Although a greater enrichment of gRNAs occurring in the cell population treated with hygromycin was expected, similar results were obtained in other screens [14, 15]. As shown in the publication of Shalem et al. longer screening periods further enrich the number of positive guides selected. Our results are comparable to the early timepoints described in these studies. Furthermore, while longer timepoints would increase the magnitude of the fold enrichment, it would probably not change the identity of the selected guides. The pooled library used contains multiple sgRNAs for each gene, and for most enriched genes, more than one sgRNA targeting the same transcript was enriched in the selected cells (Fig. 1C). Ranking the enriched genes using the MAGeCK algorithm [FAP patient Biopsy samples

Adenoma and healthy surrounding tissue samples were collected in liquid nitrogen. RNA was extracted using the AllPrep DNA/RNA/protein kit (QIAGEN) following manufacture’s protocol. The samples were obtained for a previous study [19], which was approved by the local IRB committee and registered at the NIH website (NCT02175914). All patients or their legal guardians signed informed consent forms prior to study enrollment.

Mass spectrometry analysis

Equal amounts of HEK293-DHX29 or NT1 knockout cells were pelleted and sent for Mass spectrometry analysis at the Smoler proteomics center, Israel Institute of Technology. The samples were digested by trypsin, analyzed by LC-MS/MS on Q-Exactive (Thermo), and identified by Discoverer software against a Human database. The results were semi-quantified by calculating the peak area of each peptide as the average of the three most intense peptides from each protein. The area scores of the protein profile from the HEK293-DHX29 KO sample were compared to that in the NT1 control to identify up and downregulated proteins.

Statistical methods

Data were analyzed using Graphpad Prism software (version 9.0, GraphPad, La Jolla, CA) and the results are presented as the mean with standard deviation of 3–5 repeats. An unpaired t-test or analysis of variance (ANOVA) to assess the significance of variations; multiple comparisons were conducted according to software recommendations.