SPOP targets the immune transcription factor IRF1 for proteasomal degradation

Adaptation of the functional proteome is essential to counter pathogens during infection, yet precisely timed degradation of these response proteins after pathogen clearance is likewise key to preventing autoimmunity. Interferon regulatory factor 1 (IRF1) plays an essential role as a transcription factor in driving the expression of immune response genes during infection. The striking difference in functional output with other IRFs is that IRF1 also drives the expression of various cell cycle inhibiting factors, making it an important tumor suppressor. Thus, it is critical to regulate the abundance of IRF1 to achieve a 'Goldilocks' zone in which there is sufficient IRF1 to prevent tumorigenesis, yet not too much which could drive excessive immune activation. Using genetic screening, we identified the E3 ligase receptor speckle type BTB/POZ protein (SPOP) to mediate IRF1 proteasomal turnover in human and mouse cells. We identified S/T-rich degrons in IRF1 required for its SPOP MATH domain-dependent turnover. In the absence of SPOP, elevated IRF1 protein levels functionally increased IRF1-dependent cellular responses, underpinning the biological significance of SPOP in curtailing IRF1 protein abundance.

HUWE1 controls tristetraprolin proteasomal degradation by regulating its phosphorylation

Tristetraprolin (TTP) is a critical negative immune regulator. It binds AU-rich elements in the untranslated-regions of many mRNAs encoding pro-inflammatory mediators, thereby accelerating their decay. A key but poorly understood mechanism of TTP regulation is its timely proteolytic removal: TTP is degraded by the proteasome through yet unidentified phosphorylation-controlled drivers. In this study, we set out to identify factors controlling TTP stability. Cellular assays showed that TTP is strongly lysine-ubiquitinated, which is required for its turnover. A genetic screen identified the ubiquitin E3 ligase HUWE1 as a strong regulator of TTP proteasomal degradation, which we found to control TTP stability indirectly by regulating its phosphorylation. Pharmacological assessment of multiple kinases revealed that HUWE1-regulated TTP phosphorylation and stability was independent of the previously characterized effects of MAPK-mediated S52/S178 phosphorylation. HUWE1 function was dependent on phosphatase and E3 ligase binding sites identified in the TTP C-terminus. Our findings indicate that while phosphorylation of S52/S178 is critical for TTP stabilization at earlier times after pro-inflammatory stimulation, phosphorylation of the TTP C-terminus controls its stability at later stages.

Isolating live cell clones from barcoded populations using CRISPRa-inducible reporters

We developed a functional lineage tracing tool termed CaTCH (CRISPRa tracing of clones in heterogeneous cell populations). CaTCH combines precise clonal tracing of millions of cells with the ability to retrospectively isolate founding clones alive before and during selection, allowing functional experiments. Using CaTCH, we captured rare clones representing as little as 0.001% of a population and investigated the emergence of resistance to targeted melanoma therapy in vivo.

Abstract PO-132: CaTCH - A barcode-guided CRISPRa-inducible reporter to isolate clones from heterogeneous populations

The emergence of resistant cell clones to targeted therapies poses a significant issue in the treatment of metastatic melanoma. While these founding clones are often extremely rare in a starting population, their isolation and characterization holds unique potential for understanding disease processes, uncovering novel biomarkers and developing therapeutic concepts. The functional characterization of such founder clones and comprehensive comparisons to their post-selection counterparts requires live cells. To achieve this, we developed a novel lineage tracing tool termed CaTCH (CRISPRa tracing of clones in heterogeneous cell populations). CaTCH combines precise mapping of the lineage history of millions of cells with the ability to isolate any given clone alive from a complex population based on genetic barcodes. CaTCH thereby enables the retrospective isolation and analysis of founding clones from heterogeneous cell populations prior to evolutionary selection. In first applications, we use CaTCH to provide insights into the development of resistance to targeted cancer therapies. We demonstrate that CaTCH can be used to trace and isolate a single pre-existing therapy-resistant clone from a complex cancer cell population in vitro. Furthermore, we validate the utility of CaTCH for applications in vivo by investigating the origins of resistance to clinically relevant RAF/MEK inhibition in an immunocompetent melanoma mouse model. Here we find that most clones have the capacity to acquire resistance to combined RAF/MEK inhibitor therapy, indicating that resistance to this clinically relevant regimen is a universally achievable state in this model. We envision that CaTCH will address fundamental questions in basic and translational research (e.g., how cell identity states and trajectories are determined in therapy resistance, metastasis formation, tissue development and somatic cell re-programming), potentially revealing new vulnerabilities that can serve as targets for therapies.

Citation Format: Christian Umkehrer, Felix Holstein, Laura Formenti, Julian Jude, Kimon Froussios, Tobias Neumann, Shona M. Cronin, Lisa Haas, Jesse Lipp, Thomas R. Burkard, Michaela Fellner, Thomas Wiesner, Johannes Zuber, Anna C. Obenauf. CaTCH - A barcode-guided CRISPRa-inducible reporter to isolate clones from heterogeneous populations [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-132.

How well do RNA-Seq differential gene expression tools perform in a complex eukaryote? A case study in Arabidopsis thaliana

MOTIVATION

RNA-seq experiments are usually carried out in three or fewer replicates. In order to work well with so few samples, differential gene expression (DGE) tools typically assume the form of the underlying gene expression distribution. In this paper, the statistical properties of gene expression from RNA-seq are investigated in the complex eukaryote, Arabidopsis thaliana, extending and generalizing the results of previous work in the simple eukaryote Saccharomyces cerevisiae.

RESULTS

We show that, consistent with the results in S.cerevisiae, more gene expression measurements in A.thaliana are consistent with being drawn from an underlying negative binomial distribution than either a log-normal distribution or a normal distribution, and that the size and complexity of the A.thaliana transcriptome does not influence the false positive rate performance of nine widely used DGE tools tested here. We therefore recommend the use of DGE tools that are based on the negative binomial distribution.

AVAILABILITY AND IMPLEMENTATION

The raw data for the 17 WT Arabidopsis thaliana datasets is available from the European Nucleotide Archive (E-MTAB-5446). The processed and aligned data can be visualized in context using IGB (Freese et al., 2016), or downloaded directly, using our publicly available IGB quickload server at https://compbio.lifesci.dundee.ac.uk/arabidopsisQuickload/public_quickload/ under 'RNAseq>Froussios2019'. All scripts and commands are available from github at https://github.com/bartongroup/KF_arabidopsis-GRNA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Detection and mitigation of spurious antisense expression with RoSA

Antisense transcription is known to have a range of impacts on sense gene expression, including (but not limited to) impeding transcription initiation, disrupting post-transcriptional processes, and enhancing, slowing, or even preventing transcription of the sense gene. Strand-specific RNA-Seq protocols preserve the strand information of the original RNA in the data, and so can be used to identify where antisense transcription may be implicated in regulating gene expression. However, our analysis of 199 strand-specific RNA-Seq experiments reveals that spurious antisense reads are often present in these datasets at levels greater than 1% of sense gene expression levels. Furthermore, these levels can vary substantially even between replicates in the same experiment, potentially disrupting any downstream analysis, if the incorrectly assigned antisense counts dominate the set of genes with high antisense transcription levels. Currently, no tools exist to detect or correct for this spurious antisense signal. Our tool, RoSA (Removal of Spurious Antisense), detects the presence of high levels of spurious antisense read alignments in strand-specific RNA-Seq datasets. It uses incorrectly spliced reads on the antisense strand and/or ERCC spikeins (if present in the data) to calculate both global and gene-specific antisense correction factors. We demonstrate the utility of our tool to filter out spurious antisense transcript counts in an Arabidopsis thaliana RNA-Seq experiment. Availability: RoSA is open source software available under the GPL licence via the Barton Group GitHub page https://github.com/bartongroup.

Relative Abundance of Transcripts ( RATs): Identifying differential isoform abundance from RNA-seq

The biological importance of changes in RNA expression is reflected by the wide variety of tools available to characterise these changes from RNA-seq data. Several tools exist for detecting differential transcript isoform usage (DTU) from aligned or assembled RNA-seq data, but few exist for DTU detection from alignment-free RNA-seq quantifications. We present the RATs, an R package that identifies DTU transcriptome-wide directly from transcript abundance estimates. RATs is unique in applying bootstrapping to estimate the reliability of detected DTU events and shows good performance at all replication levels (median false positive fraction < 0.05). We compare RATs to two existing DTU tools, DRIM-Seq & SUPPA2, using two publicly available simulated RNA-seq datasets and a published human RNA-seq dataset, in which 248 genes have been previously identified as displaying significant DTU. RATs with default threshold values on the simulated Human data has a sensitivity of 0.55, a Matthews correlation coefficient of 0.71 and a false discovery rate (FDR) of 0.04, outperforming both other tools. Applying the same thresholds for SUPPA2 results in a higher sensitivity (0.61) but poorer FDR performance (0.33). RATs and DRIM-seq use different methods for measuring DTU effect-sizes complicating the comparison of results between these tools, however, for a likelihood-ratio threshold of 30, DRIM-Seq has similar FDR performance to RATs (0.06), but worse sensitivity (0.47). These differences persist for the simulated drosophila dataset. On the published human RNA-seq dataset the greatest agreement between the tools tested is 53%, observed between RATs and SUPPA2. The bootstrapping quality filter in RATs is responsible for removing the majority of DTU events called by SUPPA2 that are not reported by RATs. All methods, including the previously published qRT-PCR of three of the 248 detected DTU events, were found to be sensitive to annotation differences between Ensembl v60 and v87.