Biology subjects

Mounier, N.

Publications and source records attributed to Mounier, N..

6 recordsLinked to original sources

bioRxiv2025

L-asparaginase treatment induces a tumor metabolic plasticity and reveals a vulnerability to PARP1/2 inhibitor Olaparib, in B-cell lymphomas.

Intrinsic mechanisms driving secondary resistance to L-asparaginase (ASNase) treatment remain poorly understood. Using a preclinical model of B-cell lymphomas (BCL), sensitive to ASNase, we observed tumor relapse during ASNase treatment despite an initial antineoplastic response. Through in vivo metabolic profiling of BCL and stable isotope resolved metabolomics in vitro and in vivo, we show that ASNase induces a flexible metabolic reprogramming marked by increased de novo serine biosynthesis. This adaptive response is driven in part by phosphoglycerate dehydrogenase (PHGDH), which mitigates ASNase-induced reactive oxygen species (ROS) and ROS-mediated DNA damage, enabling tumor cell survival under therapeutic pressure. ASNase-treated malignant B cells exhibit features of replication stress, with activation of the ATR-dependent DNA damage response and increased poly(ADP-ribose) polymerase 1/2 (PARP) activity. Targeting initiation of DNA repair mechanism using the PARP inhibitor Olaparib, enhances the antineoplastic effect of each individual mono-chemotherapy, both in vitro and in vivo. Importantly, we showed that this therapeutic combination was also effective in homologous recombination (HR)-proficient colorectal cancer cells, supporting its broader therapeutic relevance beyond lymphoid malignancies. Altogether, our findings reveal redox- and DNA repair-dependent metabolic vulnerabilities in ASNase-treated B-cell lymphomas and provide proof-of-concept for a rational combination therapy using clinically approved agents, ASNase and Olaparib.

cancer biology↗

bioRxiv2023

Incorporating discovery and replication GWAS into summary data Mendelian randomization studies: A review of current methods and a simple, general and powerful alternative

Mendelian Randomization (MR) is a popular method for using genetics to estimate the causal effect of a modifiable exposure on a health outcome. Single Nucleotide Polymorphisms (SNPs) are typically selected for inclusion if they pass a genome-wide significance threshold in order to guarantee that they are strong genetic instruments, but this also induces Winners curse, as SNP-exposure associations tend to be overestimated. In this paper, we consider how to combine SNP-exposure data from discovery and replication samples using two-sample and three-sample approaches to best account for Winners curse, weak instrument bias, and pleiotropy within a summary data MR framework, using only GWAS summary statistics. After reviewing several existing methods, that often correct for Winners curse at the individual SNP level, we propose a simple alternative based on the technique of regression calibration that enacts a global correction to the causal effect estimate directly. This approach does not only correct for Winners curse, but also simultaneously accounts for weak instruments bias. Regression calibration can be used with a wide range of existing MR methods, including pleiotropy-robust methods such as median-based and mode-based estimators. Extensive simulations and real data examples are used to illustrate the utility of the new approach. Software is provided for users to implement the method in practice. Author SummaryMendelian randomization is a method to explore causation in health research which exploits the random inheritance of genes from parents to offspring as a natural experiment. It attempts to quantify the effect of intervening and modifying a health exposure, such as a persons body mass, on a downstream outcome such as blood pressure. Causal estimates obtained using this method can be strongly influenced by the set of genes used, or more specifically, the rationale used to select them. For example, selecting only genes that are strongly associated with the health exposure can induce bias due to the Winners curse. Unfortunately, using genes with a small association can lead to so-called weak instrument bias leading to a no-win paradox. In this paper, we present a novel approach based on the technique of regression calibration to de-bias causal estimates in an MR study. Our approach relies on the use of two independent samples for the exposure (discovery and replication) to estimate the amount of bias that is expected for a specific set of genes, so that causal estimates can be re-calibrated accordingly. We use extensive simulations and applied examples to compare our approach to current methods and provide software for researchers to implement our approach in future studies.

genetics↗

bioRxiv2021

Correction for sample overlap, winner's curse and weak instrument bias in two-sample Mendelian Randomization

Inverse-variance weighted two-sample Mendelian randomization (IVW-MR) is the most widely used approach that utilizes genome-wide association studies (GWAS) summary statistics to infer the existence and the strength of the causal effect between an exposure and an outcome. Estimates from this approach can be subject to different biases due to the use of weak instruments and winners curse, which can change as a function of the overlap between the exposure and outcome samples. We developed a method (MRlap) that simultaneously considers weak instrument bias and winners curse, while accounting for potential sample overlap. Assuming spike-and-slab genomic architecture and leveraging LD-score regression and other techniques, we could analytically derive, reliably estimate, and hence correct for the bias of IVW-MR using association summary statistics only. We tested our approach using simulated data for a wide range of realistic settings. In all the explored scenarios, our correction reduced the bias, in some situations by as much as 30 folds. Additionally, our results are consistent with the fact that the strength of the biases will decrease as the sample size increases and we also showed that the overall bias is also dependent on the genetic architecture of the exposure, and traits with low heritability and/or high polygenicity are more strongly affected. Applying MRlap to obesity-related exposures revealed significant differences between IVW-based and corrected effects, both for non-overlapping and fully overlapping samples. Our method not only reduces bias in causal effect estimation but also enables the use of much larger GWAS sample sizes, by allowing for potentially overlapping samples.

genetics↗

bioRxiv2019

Using genetics to disentangle the complex relationship between food choices and health status

Despite food choices being one of the most important factors influencing health, efforts to identify individual food groups and dietary patterns that cause disease have been challenging, with traditional nutritional epidemiological approaches plagued by biases and confounding. After identifying 302 individual genetic determinants of dietary intake in 445,779 individuals in the UK Biobank study, we develop a statistical genetics framework that enables us, to directly assess the impact of food choices on health outcomes. We show that the biases which affect observational studies extend also to GWAS, genetic correlations and causal inference through genetics, which can be corrected by applying our methods. Finally, by applying Mendelian Randomization approaches to the corrected results we identify some of the first robust causal associations between eating patterns and cancer, heart disease, obesity, and several other health related risk factors, distinguishing between the effects of specific foods or dietary patterns.

genetics↗

bioRxiv2019

Maximum likelihood method quantifies the overall contribution of gene-environment interaction to complex traits: an application to obesity traits

As genome-wide association studies (GWAS) increased in size, numerous gene-environment interactions (GxE) have been discovered, many of which however explore only one environment at a time and may suffer from statistical artefacts leading to biased interaction estimates. Here we propose a maximum likelihood method to estimate the contribution of GxE to complex traits taking into account all interacting environmental variables at the same time, without the need to measure any. This is possible because GxE induces fluctuations in the conditional trait variance, the extent of which depends on the strength of GxE. The approach can be applied to continuous outcomes and for single SNPs or genetic risk scores (GRS). Extensive simulations demonstrated that our method yields unbiased interaction estimates and excellent confidence interval coverage. We also offer a strategy to distinguish specific GxE from general heteroscedasticity (scale effects). Applying our method to 32 complex traits in the UK Biobank reveals that for body mass index (BMI) the GRSxE explains an additional 1.9% variance on top of the 5.2% GRS contribution. However, this interaction is not specific to the GRS and holds for any variable similarly correlated with BMI. On the contrary, the GRSxE interaction effect for leg impedance [Formula] is significantly (P < 10-56) larger than it would be expected for a similarly correlated variable [Formula]. We showed that our method could robustly detect the global contribution of GxE to complex traits, which turned out to be substantial for certain obesity measures.

epidemiology↗

bioRxiv2018

Genomic underpinnings of lifespan allow prediction and reveal basis in modern risks

We use a multi-stage genome-wide association of 1 million parental lifespans of genotyped subjects and data on mortality risk factors to validate previously unreplicated findings near CDKN2B-AS1, ATXN2/BRAP, FURIN/FES, ZW10, PSORS1C3, and 13q21.31, and identify and replicate novel findings near GADD45G, KCNK3, LDLR, POM121C, ZC3HC1, and ABO. We also validate previous findings near 5q33.3/EBF1 and FOXO3, whilst finding contradictory evidence at other loci. Gene set and tissue-specific analyses show that expression in foetal brain cells and adult dorsolateral prefrontal cortex is enriched for lifespan variation, as are gene pathways involving lipid proteins and homeostasis, vesicle-mediated transport, and synaptic function. Individual genetic variants that increase dementia, cardiovascular disease, and lung cancer -but not other cancers-explain the most variance, possibly reflecting modern susceptibilities, whilst cancer may act through many rare variants, or the environment. Resultant polygenic scores predict a mean lifespan difference of around five years of life across the deciles.

genomics↗