RNAi screening of RING/U-box domain ubiquitin ligases identifies critical regulators of stem cells and tissue regeneration in planarians

Regenerative processes depend on the interpretation of signals to coordinate cell behaviors. The role of ubiquitin-mediated signaling in regeneration is not well understood. To investigate how ubiquitylation might specifically impact tissue regeneration, we are studying planarians that are capable of regenerating from nearly any injury using a population of stem cells. Here we used RNAi to screen RING/U-box E3 ubiquitin ligases that are highly expressed in planarian stem cells and stem cell progeny. RNAi screening identified nine genes with functions in regeneration, including the spliceosomal factor prpf19 and epigenetic regulator rnf2; based on roles in developmental processes, we further explored these two genes. We found that prpf19 was required for survival but not for stem cell maintenance, suggesting a role in promoting cell differentiation. Because RNF2 is the catalytic subunit of the Polycomb Repressive Complex 1 (PRC1), we also examined other cofactors of rnf2 and observed a striking phenotype of regional tissue misspecification in cbx and phc RNAi planarians. To identify genes regulated by PRC1, we performed RNA-seq after knocking down rnf2 or phc and found that the set of genes differentially expressed were largely non-overlapping despite being predicted to function in the same complex. Using in situ hybridization, we showed that rnf2 regulates gene expression levels within a tissue type, whereas phc is necessary for the spatial restriction of gene expression. This work uncovered roles for RING/U-box E3 ligases in stem cell regulation and regeneration and identified differential gene targets for PRC1 factors required for maintaining cell-type-specific gene expression in planarians.

Learning epistatic gene interactions from perturbation screens

The treatment of complex diseases often relies on combinatorial therapy, a strategy where drugs are used to target multiple genes simultaneously. Promising candidate genes for combinatorial perturbation often constitute epistatic genes, i.e., genes which contribute to a phenotype in a non-linear fashion. Experimental identification of the full landscape of genetic interactions by perturbing all gene combinations is prohibitive due to the exponential growth of testable hypotheses. Here we present a model for the inference of pairwise epistatic, including synthetic lethal, gene interactions from siRNA-based perturbation screens. The model exploits the combinatorial nature of siRNA-based screens resulting from the high numbers of sequence-dependent off-target effects, where each siRNA apart from its intended target knocks down hundreds of additional genes. We show that conditional and marginal epistasis can be estimated as interaction coefficients of regression models on perturbation data. We compare two methods, namely glinternet and xyz, for selecting non-zero effects in high dimensions as components of the model, and make recommendations for the appropriate use of each. For data simulated from real RNAi screening libraries, we show that glinternet successfully identifies epistatic gene pairs with high accuracy across a wide range of relevant parameters for the signal-to-noise ratio of observed phenotypes, the effect size of epistasis and the number of observations per double knockdown. xyz is also able to identify interactions from lower dimensional data sets (fewer genes), but is less accurate for many dimensions. Higher accuracy of glinternet, however, comes at the cost of longer running time compared to xyz. The general model is widely applicable and allows mining the wealth of publicly available RNAi screening data for the estimation of epistatic interactions between genes. As a proof of concept, we apply the model to search for interactions, and potential targets for treatment, among previously published sets of siRNA perturbation screens on various pathogens. The identified interactions include both known epistatic interactions as well as novel findings.

An RNAi screening of clinically relevant transcription factors regulating human adipogenesis and adipocyte metabolism

Objective Healthy hyperplasic (many but smaller fat cells) white adipose tissue (WAT) expansion is mediated by recruitment, proliferation and/or differentiation of new fat cells. This process (adipogenesis) is controlled by transcriptional programs mostly identified in rodents. A systemic investigation of adipogenic human transcription factors (TFs) that are relevant for metabolic conditions has not been revealed previously. Methods TFs regulated in WAT by obesity, adipose morphology, cancer cachexia and insulin resistance were selected from microarrays. Their role in differentiation of human adipose tissue-derived stem cells (hASC) was investigated by RNA interference (RNAi) screen. Lipid accumulation, cell number and lipolysis were measured for all screened factors (148 TFs). RNA (RNAseq), protein (western blot) expression, insulin and catecholamine responsiveness were examined in hASC following siRNA treatment of selected target TFs. Results Analysis of TFs regulated by metabolic conditions in human WAT revealed that many of them belong to adipogenesis-regulating pathways. The RNAi screen identified 39 genes that affected fat cell differentiation in vitro, where 11 genes were novel. Of the latter JARID2 stood out as being necessary for formation of healthy fat cell metabolic phenotype by regulating expression of multiple fat-cell phenotype-specific genes. Conclusions This comprehensive RNAi screening in hASC suggests that a large proportion of WAT TFs that are impacted by metabolic conditions might be important for hyperplastic adipose tissue expansion. The screen also identified JARID2 as a novel TF essential for the development of functional adipocytes.

RNAi Screening in Primary Human Breast Biopsies Identifies Mitochondrial Glutamate Flux as A Metabolic Vulnerability in Locally Advanced Breast Cancer

Background: Breast cancer (BC) is a leading cause of death in women[1]. Women with Locally Advanced Breast Cancer (LABC) have high risk disease with either large primary breast tumours and/or lymph node involvement. While neoadjuvant chemotherapy eradicates breast cancer in approximately one-third of cases prior to surgery, almost 70% of patients have residual disease and many will require additional chemotherapy post-operatively. Improving pre-operative efficacy of neoadjuvant systemic treatments while reducing their iatrogenicities are critical unmet needs. Methods: Here, we develop an RNA interference (RNAi) screening approach using conditionally reprogrammed primary LABC biopsies to identify genes of the mitochondrial Solute Ligand Carrier 25 (SLC25) family that support LABC cell viability. Results: We report that silencing SLC25A12, -A15, and -A18 genes, involved in glutamate and ornithine flux, augment 5-fluorouracil (5FU) cytotoxic effectiveness in LABC cells. Conclusions: Our data suggest glutamate metabolism may be a tumour-specific metabolic vulnerability in LABC. Furthermore, we demonstrate that RNAi screening in conditionally reprogrammed primary human breast cells can identify novel targets for the development of non-genotoxic BC treatments.