Biology subjects

Froussios, K.

Publications and source records attributed to Froussios, K..

6 recordsLinked to original sources

bioRxiv2022

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. We identified four S/T-rich degrons in IRF1 required for its SPOP MATH domain-dependent turnover. In the absence of SPOP, increased IRF1 protein levels functionally drive increased transcription of IRF1-response-genes, underpinning the biological significance of SPOP in curtailing IRF1 protein abundance.

cell biology↗

bioRxiv2022

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.

cell biology↗

bioRxiv2022

Somatic hypermutation spectra are independent of the local transcriptional and epigenetic landscape

Somatic hypermutation (SHM) of immunoglobulin variable (V) regions modulates antibody-antigen affinity is initiated by activation-induced cytidine deaminase (AID) on single-stranded DNA (ssDNA). Transcription is essential for SHM and AID target genes harbor activating chromatin marks and RNA polymerase II (Pol II) stalling, leading to the model that these features favor higher rates of mutagenesis. However, whether such relationships exist within V regions is undetermined. Here, we directly compared SHM and nascent transcription across four V regions and 275 non-immunoglobulin SHM targets at single-nucleotide resolution using precision run-on sequencing (PRO-seq). Although locales of Pol II enrichment and zones of Pol II stalling were detected within V regions, their correlation with SHM was not statistically significant. Moreover, SHM was robust against major reductions of activating epigenetic marks and transcription. This data suggests that SHM patterns and spectra are established independently of specific local nascent transcriptional features.

immunology↗

bioRxiv2018

Detection and Mitigation of Spurious Antisense RNA-seq Reads with RoSA

MotivationAntisense 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.\n\nResultsOur 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 spike-ins (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.\n\nAvailabilityRoSA is open source software available under the GPL licence via the Barton Group GitHub page https://github.com/bartongroup.\n\nContactg.j.barton@dundee.ac.uk, g.g.simpson@dundee.ac.uk

bioinformatics↗

bioRxiv2017

Identifying differential isoform abundance with RATs: a universal tool and a warning

MotivationThe biological importance of changes in gene and transcript expression is well recognised and is reflected by the wide variety of tools available to characterise these changes. Regulation via Differential Transcript Usage (DTU) is emerging as an important phenomenon. Several tools exist for the detection of DTU from read alignment or assembly data, but options for detection of DTU from alignment-free quantifications are limited.\n\nResultsWe present an R package named RATs - (Relative Abundance of Transcripts) - that identifies DTU transcriptome-wide directly from transcript abundance estimations. RATs is agnostic to quantification methods and exploits bootstrapped quantifications, if available, to inform the significance of detected DTU events. RATs contextualises the DTU results and shows good False Discovery performance (median FDR [≤]0.05) at all replication levels. We applied RATs to a human RNA-seq dataset associated with idiopathic pulmonary fibrosis with three DTU events validated by qRT-PCR. RATs found all three genes exhibited statistically significant changes in isoform proportions based on Ensembl v60 annotations, but the DTU for two were not reliably reproduced across bootstrapped quantifications. RATs also identified 500 novel DTU events that are enriched for eleven GO terms related to regulation of the response to stimulus, regulation of immune system processes, and symbiosis/parasitism. Repeating this analysis with the Ensembl v87 annotation showed the isoform abundance profiles of two of the three validated DTU genes changed radically. RATs identified 414 novel DTU events that are enriched for five GO terms, none of which are in common with those previously identified. Only 141 of the DTU evens are common between the two analyses, and only 8 are among the 248 reported by the original study. Furthermore, the original qRT-PCR probes no longer match uniquely to their original transcripts, calling into question the interpretation of these data. We suggest parallel full-length isoform sequencing, annotation pre-filtering and sequencing of the transcripts captured by qRT-PCR primers as possible ways to improve the validation of RNA-seq results in future experiments.\n\nAvailabilityThe package is available through Github at https://github.com/bartongroup/Rats.

bioinformatics↗

bioRxiv2016

How well do RNA-Seq differential gene expression tools perform in higher eukaryotes?

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 distribution of gene expression. A recent highly replicated study revealed that RNA-seq gene expression measurements in yeast are best represented as being drawn from an underlying negative binomial distribution. In this paper, the statistical properties of gene expression in the higher eukaryote Arabidopsis thaliana are shown to be essentially identical to those from yeast despite the large increase in the size and complexity of the transcriptome: Gene expression measurements from this model plant species are consistent with being drawn from an underlying negative binomial or log-normal distribution and the false positive rate performance of nine widely used DGE tools is not strongly affected by the additional size and complexity of the A. thaliana transcriptome. For RNA-seq data, we therefore recommend the use of DGE tools that are based on the negative binomial distribution.

genomics↗