Proteasome inhibitors are used to treat blood cancers such as multiple myeloma (MM) and mantle cell lymphoma. The efficacy of these drugs is frequently undermined by acquired resistance. One mechanism of proteasome inhibitor resistance may involve the transcription factor Nuclear Factor, Erythroid 2 Like 1 (NFE2L1, also referred to as Nrf1), which responds to proteasome insufficiency or pharmacological inhibition by upregulating proteasome subunit gene expression. This “bounce-back” response is achieved through a unique mechanism. Nrf1 is constitutively translocated into the ER lumen, N-glycosylated, and then targeted for proteasomal degradation via the ER-associated degradation (ERAD) pathway. Proteasome inhibition leads to accumulation of cytosolic Nrf1, which is then processed to form the active transcription factor. Here we show that the cytosolic enzyme N-glycanase 1 (NGLY1, the human PNGase) is essential for Nrf1 activation in response to proteasome inhibition. Chemical or genetic disruption of NGLY1 activity results in the accumulation of misprocessed Nrf1 that is largely excluded from the nucleus. Under these conditions, Nrf1 is inactive in regulating proteasome subunit gene expression in response to proteasome inhibition. Through a small molecule screen, we identified a cell-active NGLY1 inhibitor that disrupts the processing and function of Nrf1. The compound potentiates the cytotoxicity of carfilzomib, a clinically used proteasome inhibitor, against MM and T cell-derived acute lymphoblastic leukemia (T-ALL) cell lines. Thus, NGLY1 inhibition prevents Nrf1 activation and represents a new therapeutic approach for cancers that depend on proteasome homeostasis.

The transcriptome contains rich information on molecular, cellular, and organismal phenotypes. However, experimental and statistical limitations constrain sensitivity and throughput of genetic screening with single-cell transcriptomics readout. To overcome these limitations, we introduce targeted Perturb-seq (TAP-seq), a sensitive, inexpensive, and platform-independent method focusing single-cell RNA-seq coverage on genes of interest, thereby increasing the sensitivity and scale of genetic screens by orders of magnitude. TAP-seq permits routine analysis of 1,000s of CRISPR-mediated perturbations within a single experiment, detects weak effects and lowly expressed genes, and decreases sequencing requirements up to 50-fold. We apply TAP-seq to generate perturbation-based enhancer-target gene maps for 1,778 enhancers within 2.5% of the human genome. Thereby, we show that enhancer-target association is jointly determined by 3D contact frequency and epigenetic states, allowing accurate prediction of enhancer targets throughout the genome. In addition, we demonstrate that TAP-seq can identify cell subtypes with only 100 sequencing reads per cell.

No abstract

Unraveling the molecular processes that lead from genotype to phenotype is crucial for the understanding and effective treatment of genetic diseases. Knowledge of the causative genetic defect most often does not enable treatment; therefore, causal intermediates between genotype and phenotype constitute valuable candidates for molecular intervention points that can be therapeutically targeted. Mapping genetic determinants of gene expression levels (also known as expression quantitative trait loci or eQTL studies) is frequently used for this purpose, yet distinguishing causation from correlation remains a significant challenge. Here, we address this challenge using extensive, multi-environment gene expression and fitness profiling of hundreds of genetically diverse yeast strains, in order to identify truly causal intermediate genes that condition fitness in a given environment. Using functional genomics assays, we show that the predictive power of eQTL studies for inferring causal intermediate genes is poor unless performed across multiple environments. Surprisingly, although the effects of genotype on fitness depended strongly on environment, causal intermediates could be most reliably predicted from genetic effects on expression present in all environments. Our results indicate a mechanism explaining this apparent paradox, whereby immediate molecular consequences of genetic variation are shared across environments, and environment-dependent phenotypic effects result from downstream integration of environmental signals. We developed a statistical model to predict causal intermediates that leverages this insight, yielding over 400 transcripts, for the majority of which we experimentally validated their role in conditioning fitness. Our findings have implications for the design and analysis of clinical omics studies aimed at discovering personalized targets for molecular intervention, suggesting that inferring causation in a single cellular context can benefit from molecular profiling in multiple contexts.

Hundreds of transcript isoforms with varying boundaries and alternative regulatory signals are transcribed from the genome, even in a genetically homogeneous population of cells. To study this transcriptional heterogeneity, we developed Transcript Isoform Sequencing (TIF-Seq), a method that allows the genome-wide profiling of full-length transcript isoforms defined by their exact 5′ and 3′ boundaries. TIF-Seq entails generating full-length cDNA libraries, followed by their circularization and the sequencing of the junction fragments spanning the 5′ and 3′ transcript ends. By determining the respective co-occurrence of start and end sites of individual transcript molecules, TIF-Seq can distinguish variations that conventional approaches for mapping single ends cannot, such as short abortive transcripts, bicistronic messages, and overlapping transcripts that differ in lengths. The TIF-Seq protocol we describe here can be applied to any eukaryotic organism (e.g., yeast, human) and requires 6-10 days to generate TIF-Seq libraries, 10 days for sequencing and 2-3 days for analysis.