Biology subjects

Sauvageau, G.

Publications and source records attributed to Sauvageau, G..

13 recordsLinked to original sources

bioRxiv2026

Genome-wide structural variant landscape following HDR-enhanced CRISPR editing in human hematopoietic stem and progenitor cells

DNA-PK inhibitors (DNA-PKi) have emerged as potent enhancers of homology-directed repair (HDR) in genome editing, however, recent studies have reported that such inhibitors can induce large-scale structural variations (SVs) raising safety concerns. To investigate factors influencing SV induction in human hematopoietic stem and progenitor cells (HSPCs), we employed a combination of on-target long-read nanopore sequencing and unbiased genome wide detection of SVs using Strand-seq. Our results show that editing alone results in a high frequency of large on-target deletions which were modestly increased by DNA-PK inhibition and could be mitigated using DNA polQ inhibitors. Genome-wide SVs were also induced at low frequencies by editing but were not substantially increased with DNA-PKi. SVs including translocations were, however, increased with multiple simultaneous editing. Our results highlight that edit-induced SVs are not limited to DNA-PKi enhanced protocols, thus careful assessment will be critical to ensure the safety of any edited therapeutic cell product.

Molecular Biology↗

bioRxiv2026

A Chemical-Genetic Interaction Matrix Reveals Drug Mechanism and Genetic Architecture

To probe drug mechanism of action (MOA) and interrogate the genetic architecture of human cells, we carried out isogenic genome-wide CRISPR/Cas9 knockout screens against 310 diverse drugs, bioactive compounds, and stress conditions. Stringent statistical correction for gene knockout fitness defects yielded a large-scale matrix of >12,000 high confidence chemical-genetic interactions (CGIs). This dataset revealed many previously unappreciated off-target effects for well-characterized compounds and novel MOAs for uncharacterized compounds. The CGI matrix uncovered dense genetic modules that yielded new biological insights into phospholipidosis, mitotic regulation, metabolism, the DNA damage response, and mTOR signaling. The dataset allowed identification of multi-drug sensitization and resistance mechanisms, inference of gene function, elaboration of cross-process connectivity, evaluation of the cell type specificity of CGIs, prediction of chemical synergism, and extensive annotation of understudied genes. This resource provides a map of the genetic landscape in human cells and a framework to help guide drug discovery.

systems biology↗

bioRxiv2025

TP53-mutant AML with ribosomal gene loss exhibits impaired protein translation and sensitivity to HSP90 inhibition

TP53-mutated acute myeloid leukemia (AML) represents a particularly aggressive and therapeutically refractory subtype of the disease. While recurrent chromosomal abnormalities such as -5/del(5q), -7/del(7q), and del(17p) are well studied in this context, additional co-occurring events remain less well defined. Using the multi-dimensional Leucegene dataset ([~]700 primary AML specimens), we identified and comprehensively characterized a distinct subset of TP53-altered AML marked by recurrent deletions on the short arm of chromosome 3 (del(3p), > 20% TP53-mutated cases). These deletions frequently co-occur with del(5q) and encompass several ribosomal protein genes (RPGs), leading to a global downregulation of the ribosomal network and reduced protein synthesis. We show that this ribosomopathy-like phenotype is most pronounced in TP53-mutated cases with combined RPG deletions on chromosomes 3p and 5q, suggesting a cooperative oncogenic mechanism. Importantly, chemical screening identified HSP90 inhibition as a selective vulnerability in AML with low RPG expression. These findings highlight a previously unappreciated TP53-altered AML subset characterized by converging genomic and translational defects, and suggest that ribosomal stress may serve as a therapeutic entry point for targeted intervention of this patient subgroup.

cancer biology↗

bioRxiv2025

Predictive Gene Discovery with EPCY: A Density-Based Alternative to DE analysis

Identifying predictive genes from high-throughput data remains a key challenge in biomedical research. Most current approaches rely on statistical tests to select differentially expressed genes (DEGs), which may not align with the goal of predicting outcomes. We present EPCY, a method that ranks genes based on their predictive power using cross-validated classifiers and density estimation, without relying on null hypothesis testing. Applied to both bulk and single-cell RNA sequencing datasets, EPCY consistently outperforms benchmark DEG-based methods in selecting robust candidate genes. It also demonstrates greater stability across varying cohort sizes, enabling reproducible gene prioritization even in large, heterogeneous datasets. EPCY provides interpretable predictive scores, facilitating candidate selection aligned with downstream validation goals.

bioinformatics↗

bioRxiv2025

CD33 Epitope Editing Unlocks UM171-Expanded Cord Blood Grafts for AML Immunotherapy

Immunotherapies in acute myeloid leukemia (AML) are limited by shared antigen expression between leukemic and healthy hematopoietic cells, leading to on-target toxicity. Here we developed a clinically scalable strategy to engineer cord blood (CB)-derived hematopoietic stem and progenitor cell (HSPC) grafts resistant to CD33-directed therapies. Leveraging UM171-mediated expansion and adenine base editing, we precisely disrupted a critical epitope in CD33 required for gemtuzumab ozogamicin (GO) binding, centered on phenylalanine 21, while preserving CD33 expression and its sialic acid binding function. Ex vivo edited HSPCs maintained robust multilineage engraftment, T-cell output, and conferred protection from GO-induced myelotoxicity in xenograft models, without impairing anti-leukemic efficacy. Editing was efficient across multiple donors, enriched in primitive subsets, and exhibited minimal off-target activity by ultra-deep exome sequencing. Our work establishes base editor-driven epitope engineering as an improved approach to CD33-targeted immunotherapy-compatible HSPC grafts, enabling safe integration of currently available agents into post-transplant care.

immunology↗

bioRxiv2025

DEK::NUP214 acts as an XPO1-dependent transcriptional activator of essential leukemia genes

The t(6;9)(p22.3;q34.1) translocation/DEK::NUP214 fusion protein defines a distinct subgroup of younger AML patients classified as a separate disease entity by the World Health Organization. DEK is a nuclear factor with multifunctional roles, including gene regulation, while its fusion partner, NUP214, plays a pivotal role in nuclear export by interacting with transport receptors such as XPO1. However, the precise mechanism by which DEK::NUP214 drives leukemia remains unclear. A comprehensive multi-omics comparison of 57 AML primary samples (including whole genome sequencing, targeted sequencing, transcriptomics, and drug screening with > 500 compounds) revealed that t(6;9) cases display a selective response to XPO1 inhibitors (Selinexor & Eltanexor) and a distinct transcriptomic signature characterized by the overexpression of FOXC1 and HOX genes that are key leukemia mediators. CUT&RUN experiments demonstrated the direct binding of DEK::NUP214 to the promoters of FOXC1 and HOXA/B clusters. Strikingly, the expression of these genes and the binding of DEK::NUP214 to their regulatory regions were selectively reduced upon XPO1 inhibition in t(6;9) cells. Altogether, these results identified a novel function of DEK::NUP214 as an XPO1-dependent transcriptional activator of key leukemia drivers and provide a rationale to explore the use of XPO1 inhibitors in this patient population.

cancer biology↗

bioRxiv2023

SPAT: Surface Protein Annotation Tool

Given the particular attractivity of antibody-based immunotherapies, in vitro experimental approaches aiming to identify and quantify proteins directly located at the cell surface, such as the surfaceome, have been recently developed and improved. However, the "surface" enriched, yet noisy output obtained from available methods makes it challenging to accurately evaluate which proteins are more likely to be located at the surface of the plasma membrane and which are simple contaminants. To that purpose, we developed the in silico Surface Protein Annotation Tool (SPAT), which unifies established annotations to grade proteins according to the chance they have to be located at the cell surface. SPAT accuracy was tested using in-house acute myeloid leukemia data, as well as public datasets, and despite using publicly available annotations, showed good performances when compared to more complex surfaceome predictors. Given its simple input requirement, SPAT is easily usable for the annotation of any gene/protein lists. Its output, in addition to the "surface" score, provides additional annotations including a "secretion" flag, references to verified antibodies targeting annotated proteins, as well as expression data and protein levels in essential human organs, making it a user-friendly tool for the community.

bioinformatics↗

bioRxiv2023

Near-perfect precise on-target editing of human hematopoietic stem and progenitor cells

Precision gene editing in primary hematopoietic stem and progenitor cells (HSPCs) would facilitate both curative treatments for monogenic disorders as well as disease modelling. Precise efficiencies even with the CRISPR/Cas system, however, remain limited. Through an optimization of guide RNA delivery, donor design, and additives, we have now obtained mean precise editing efficiencies >90% on primary cord blood HSCPs with minimal toxicity and without observed off-target editing. The main protocol modifications needed to achieve such high efficiencies were the addition of the DNA-PK inhibitor AZD7648, and the inclusion of spacer-breaking silent mutations in the donor in addition to mutations disrupting the PAM sequence. Critically, editing was even across the progenitor hierarchy, did not substantially distort the hierarchy or affect lineage outputs in colony-forming cell assays or the frequency of high self-renewal potential long-term culture initiating cells. As modelling of many diseases requires heterozygosity, we also demonstrated that the overall editing and zygosity can be tuned by adding in defined mixtures of mutant and wild-type donor. With these optimizations, editing at near-perfect efficiency can now be accomplished directly in human HSPCs. This will open new avenues in both therapeutic strategies and disease modelling.

bioengineering↗

bioRxiv2022

Baseline Acute Myeloid Leukemia Prognosis Models using Transcriptomic and Clinical Profiles by Studying the Impacts of Dimensionality Reductions and Gene Signatures on Cox-Proportional Hazard

Gene marker extraction to evaluate risk in cancer can refine the diagnosis process and lead to adapted therapies and better survival. These survival analyses can be done through computer systems and Machine Learning (ML) algorithms such as the Cox-Proportional-Hazard model from gene expression (GE) RNA-Seq data. However, optimal tuning of CPH from genome-wide GE data is challenging and poorly assessed so far. In this work we propose to interrogate an Acute Myeloid Leukemia (AML) dataset (Leucegene) to derive key components of the CPH driving down its performance and discovering its sensitivity to various factors in hoping to ameliorate the system. In this study, we compare the projection and selection data reduction techniques, mainly the PCA and LSC17 gene signature in combination with the CPH in a ML framework. Results reveals that CPH performs better with a combination of clinical and genetic expression features. We determine that projections performs better than selections without clinical information. We ascertain that CPH is affected by overfitting and that this overfitting is linked to the number and the content of input covariables. We show that PCA links clinical features via ability to learn from the input data directly and generalizes better than LSC17 on Leucegene. We postulate that projection are preferred than selection on harder task such as assessing risk in the intermediate subset of Leucegene. We extrapolate that these findings apply in the more general context of risk detection via machine learning in cancer. We see that higher capacity models such as CPH-DNNs systems can be improved via survival-derived projections and combat overfitting through heavy regularization. Author summaryThis study aims to investigate the feasibility of using gene expression to evaluate risk in cancer, and to compare the projection and selection data reduction techniques. The study used the Leucegene dataset to compare the PCA method and a previously published 17 genes signature in combination with the Cox-Proportional-Hazard model in a machine learning framework. Results showed that CPH was affected by overfitting and that this overfitting was linked to the number and the content of input covariables. The study found that PCA links clinical features via ability to learn from the input data directly and generalizes better than LSC17 on Leucegene. The study concluded that projections are preferred than selection on harder task such as assessing risk in the intermediate subset of Leucegene and can be tuned to improve their performance. Data availability statementSource code for pipelines and algorithms, as well as gene expression matrices, are available here: https://github.com/lemieux-lab/dimensions_coxph. Access to the Leucegene cohorts survival times can be granted upon request and following ethical review.

bioinformatics↗

bioRxiv2020

HLF Expression Defines the Human Haematopoietic Stem Cell State

Hematopoietic stem cells (HSCs) sustain blood cell homeostasis throughout life and are able to regenerate all blood lineages following transplantation. Despite this clear functional definition, highly enriched isolation of human HSCs can currently only be achieved through combinatorial assessment of multiple surface antigens. While a number of transgenic HSC reporter mouse strains have been described, no analogous approach to prospectively isolate human HSCs has been reported. To identify genes with the most selective expression in human HSCs, we profiled population- and single-cell transcriptomes of fresh and ex vivo cultured cord blood derived HSPCs as well as peripheral blood, adult bone marrow and fetal liver. Based on these analyses, we propose the master transcription factor HLF (Hepatic Leukemia Factor) as one of the most specific HSC marker genes. To directly track its expression in human hematopoietic cells, we developed a genomic HLF reporter strategy, capable of selectively labeling the most immature blood cells on the basis of a single engineered parameter. Most importantly, HLF-expressing cells comprise all of the stem cell activity in culture and in vivo during serial transplantation. Taken together, these results experimentally establish HLF as a defining gene of the human hematopoietic stem cell state and outline a new approach to continuously mark these cells with high fidelity. Key PointsO_LIIn the human blood system, HLF expression is specific to stem cell populations in primary anatomical sites and during ex vivo expansion. C_LIO_LICRISPR/rAAV6-mediated integration of a genomic HLF-reporter allows selective and stable genetic labeling of human HSCs ex vivo and in vivo. C_LI

cell biology↗

bioRxiv2019

Genetic Landscape of Electron Transport Chain Complex I Dependency in Acute Myeloid Leukemia

Inhibition of oxidative phosphorylation (OXPHOS) is a promising therapeutic strategy in Acute Myeloid Leukemia (AML), but patients respond heterogeneously. Through chemically interrogation of 200 sequenced specimens, we identified Mubritinib as a strong in vitro and in vivo anti-leukemic compound, acting through ubiquinone-dependent inhibition of Electron Transport Chain complex I (ETC1). ETC1 targeting showed selective toxicity against a subgroup of chemotherapy-resistant leukemias exhibiting OXPHOS hyperactivity, high expression of mitochondrial activity-related genes, and mutations affecting NPM1, FLT3 and DNMT3A. Altogether, our work thus identifies a novel ETC1 inhibitor with high clinical potential and reveals the landscape of OXPHOS dependency in AML.

cancer biology↗

bioRxiv2018

Target variant detection in leukemia using unaligned RNA-Seq reads

Mutations identified in each Acute Myeloid Leukemia (AML) patients are useful for prognosis and to select targeted therapies. Detection of such mutations by the analysis of Next-Generation Sequencing (NGS) data requires a computationally intensive read mapping step and application of several variant calling methods. Targeted mutation identification drastically shifts the usual tradeoff between accuracy and performance by concentrating all computations over a small portion of sequence space. Here, we present km, an efficient approach leveraging k-mer decomposition of reads to identify targeted mutations. Our approach is versatile, as it can detect single-base mutations, several types of insertions and deletions, as well as fusions. We used two independent AML cohorts (The Cancer Genome Atlas and Leucegene), to show that mutation detection by km is fast, accurate and mainly limited by sequencing depth. Therefore, km allows to establish fast diagnostics from NGS data, and could be suitable for clinical applications.

bioinformatics↗

bioRxiv2017

Committed hemopoietic progenitors, not stem cells, are the principal responders to Hox gene transduction

As hemopoietic stem cells differentiate, their proliferative lifespan shortens by unknown mechanisms. Homeobox cluster (Hox) genes have been implicated by their enhancement of self-renewal when transduced into hemopoietic cells, but gene deletions have been inconclusive because of functional redundancy. Here we enforced HOXB4 expression in purified precursor stages, and compared responses of early stages expressing the endogenous genes with later stages that did not. Contrary to the prevalent view that transduced Hox genes enhance the self-renewal of hemopoietic stem cells, stem cells or their multipotent progeny expressing the endogenous genes showed little response. Instead, immortalization, extensive self-renewal and acquired reconstituting potential occurred in committed erythroid and myeloid progenitors where the endogenous genes were shutting down. The results change our understanding of the stages affected by exogenous HOX proteins and point to shutdown of the endogenous genes as a principal determinant of the shortened clonal lifespans of committed progenitor cells.

cell biology↗