Biology subjects

Isaac, C.

Publications and source records attributed to Isaac, C..

3 recordsLinked to original sources

bioRxiv2025

Fibrillarin-mediated ribosomal RNA maturation is a novel therapeutic vulnerability in triple-negative breast cancer

Triple-negative breast cancer (TNBC) remains one of the most challenging breast cancer subtypes to treat due to the lack of effective therapeutic options. Ribosome biogenesis has recently emerged as a promising therapeutic target across various cancers. Despite the current targeting of ribosome biogenesis through RNA polymerase I (RNA Pol I) inhibition, we speculated that other factors essential for ribosome assembly, such as rRNA maturation factors, may also represent therapeutic targets in TNBC. Here, we demonstrate that ribosome biogenesis-related genes are notably overexpressed in TNBC compared to other breast cancer subtypes, highlighting its critical role in TNBC progression. Accordingly, we show that RNA Pol I inhibition exerts potent anti-proliferative effects in pre-clinical models of TNBC, both in vitro and in vivo. However, the DNA-damaging activity of RNA Pol I inhibitors raises safety concerns, highlighting the need for alternative strategies to inhibit ribosome biogenesis. To this end, we show that targeting a downstream rRNA maturation step, specifically pre-rRNA cleavage, by inhibiting the maturation factor Fibrillarin, also inhibits tumor growth in TNBC models. Notably, ribosome biogenesis inhibition, through either RNA Pol I or Fibrillarin targeting, induces cell cycle arrest without triggering significant cell death. These findings establish ribosome biogenesis as a therapeutic vulnerability in TNBC and identify rRNA maturation, and Fibrillarin in particular, as novel targets for potential therapeutic intervention. SignificanceTargeting ribosome biogenesis, through inhibition of either rRNA synthesis or maturation, induces anti-tumoral effects in TNBC, representing a novel therapeutic vulnerability with potential to improve patient outcomes.

cancer biology↗

bioRxiv2022

Machine learning-based detection of label-free cancer stem-like cell fate

Most imaging methods rely on labelling biological samples in order to provide specific and easily detectable features. However, label-free imaging is a non-invasive and non-toxic alternative that requires accurate image analysis algorithm based on cell morphology. Such analysis has to deal with a high image variability while fewer features are extractable, so far, fast analysis of label-free brightfield microscopy (LFBM) images remains a challenging task. With the development of microfabricated devices during the last decades, high throughput image generation makes it possible to use machine learning-based algorithms in order to analyse LFBM images. Fast algorithms are also crucially needed to analyze high throughput experiments. In this paper, we provide a data-driven study in order to assess the complexity of LFBM time-lapses monitoring isolated cancer stem-like cells (CSCs) fate in non-adherent conditions. We combined for the first time individual cell fate and cell state temporality analysis in a unique algorithm. Several image analysis algorithms of increasing processing capacities were tested: a classical computer vision algorithm (CCVA), a shallow learning-based algorithm (SLBA) and a deep learning-based algorithm (DLBA). We show that our optimized DLBA has by far the best accuracy compared to CCVA and SLBA, is at least as accurate as other state-of-the-art DLBAs while being faster. With this study, we demonstrate that optimizing our DLBA accordingly to the image analysis problem can overall provide better results than pretrained models. Such a fast and accurate DLBA is therefore compatible with the generation of high throughput data and opens the route for on-the-fly analysis of CSC fate from LFBM time-lapses.

bioinformatics↗

bioRxiv2020

Increased processing of SINE B2 non coding RNAs unveils a novel type of transcriptome de-regulation underlying amyloid beta neuro-pathology

More than 97% of the mammalian genome is non-protein coding, and repetitive elements account for more than 50% of noncoding space. However, the functional importance of many non-coding RNAs generated by these elements and their connection with pathologic processes remains elusive. We have previously shown that B2 RNAs, a class of non-coding RNAs that belong to the B2 family of SINE repeats, mediate the transcriptional activation of stress response genes (SRGs) upon application of a stimulus. Notably, B2 RNAs bind RNA Polymerase II (RNA Pol II) and suppress SRG transcription during pro-stimulation state. Upon application of a stimulus, B2 RNAs are processed into fragments and degraded, which in turn releases RNA Pol II from suppression and upregulates SRGs. Here, we demonstrate a novel role for B2 RNAs in transcriptome response to amyloid beta toxicity and pathology in the mouse hippocampus. In healthy hippocampi, activation of SRGs is followed by a transient upregulation of pro-apoptotic factors, such as p53 and miRNA-34c, which target SRGs creating a negative feedback loop that facilitates transition to the pro-stimulation state. Using an integrative RNA genomics approach, we show that in mouse hippocampi of an amyloid precursor protein knock-in mouse model and in an in vitro cell culture model of amyloid beta toxicity, this regulatory loop is dysfunctional due to increased levels of B2 RNA processing, constitutively elevated SRG expression and high p53 levels. Evidence indicates that Hsf1, a master regulator of stress response, mediates B2 RNA processing in cells, and is upregulated during amyloid toxicity accelerating the processing of SINE RNAs and SRG hyper-activation. Our study reveals that in mouse, SINE RNAs constitute a novel pathway deregulated in amyloid beta pathology, with potential implications for similar cases in the human brain, such as Alzheimers disease (AD). This data attributes a role to SINE RNA processing in a pathological process as well as a new function to Hsf1 that is independent of its transcription factor activity.

genomics↗