Biology subjects

Defrance, M.

Publications and source records attributed to Defrance, M..

8 recordsLinked to original sources

bioRxiv2022

Histone H2A monoubiquitination in the thalamus regulates cocaine effects and addiction risk

The individual risk of developing drug addiction is highly determined by the epigenetic landscape1,2. Chromatin remodeling regulates drug-induced transcriptional and behavioral effects and the consequent development of addictive behaviors2,3. Several chromatin modifications in the ventral tegmental area and nucleus accumbens, including histone H3 methylation, H3 and H4 acetylation, have been implicated in drug addiction. Still, the contribution of other histones and their post-translational modifications (PTMs), such as monoubiquitination is unclear4-8. In the course of investigating the underlying mechanisms associated with melanoma-associated antigen D1 (Maged1)9, a scaffold protein involved in drug addiction9, we found that H2A monoubiquitination in the paraventricular thalamus (PVT) plays a major role in cocaine-adaptive behaviors and cocaine-evoked transcriptional repression. Mice undergoing chronic cocaine administration showed a significant increased monoubiquitination of H2A. Furthermore, we showed that this histone PTM is controlled, in the PVT, by Maged1, along with one of its partner, the deubiquitinase USP710. Accordingly, Maged1 specific inactivation in thalamic vGluT2 neurons, or USP7 inhibition, blocked cocaine-evoked H2A monoubiquitination and abolished cocaine locomotor sensitization. Finally, we identified genetic variations of MAGED1 and USP7 associated with modified transition to cocaine addiction and cocaine-induced aggressive behavior in human subjects. These findings identified a new epigenetic modification in a non-canonical reward pathway of the brain and a potent marker of epigenetic risk factor for drug addiction in human.

neuroscience↗

bioRxiv2022

Facing lethal temperatures: heat shock response in desert and temperate ants

Several genera of desert ants have adapted to endure prolonged exposure to high temperatures. The study of these ants is essential to unravel how species respond and adapt to thermal stress. We investigated the thermal tolerance and the transcriptomic heat stress response of three desert ant genera (Cataglyphis, Melophorus and Ocymyrmex) and two temperate genera (Formica and Myrmica) to explore convergent and specific adaptations. We found a variable transcriptomic response among desert species exposed to similar levels of physiological heat-stress: Cataglyphis holgerseni and Melophorus bagoti differentially regulated very few transcripts, 0.12% (54/44,525) and 0.14% (53/38,726) respectively, while Cataglyphis bombycina and Ocymyrmex robustior showed greater expression alterations affecting 0.6% (253/41,912) and 1.53% (698/45,701) of their transcriptomes, respectively. These two responsive mechanisms - reactive and constitutive - were related to desert species thermal tolerance survival pattern and convergently evolved in distinct desert ant genera. By comparison, the two temperate species differentially expressed thousands of transcripts more than desert ants in response to heat stress (affecting 8% and 12,71% of F. fusca and Myr. sabuleti transcriptomes), suggesting that keeping restrained gene expression is an important adaptation in heat adapted species. Finally, we found a significant overlap of the molecular pathways activated in response to heat-stress in temperate and desert species, and our data revealed that larger gene expression responses also affected a greater number of taxonomically restricted genes. These results suggest that the molecular processes involved in heat-stress response are mostly evolutionary conserved in ants, but new genes may also play a role.

molecular biology↗

bioRxiv2022

RedRibbon: A new rank-rank hypergeometric overlap pipeline to compare gene and transcript expression signatures

MotivationHigh throughput omics technologies have generated a wealth of large protein, gene and transcript datasets that have exacerbated the need for new methods to analyse and compare big datasets. Rank-rank hypergeometric overlap is an important threshold-free method to combine and visualize two ranked lists of P-values or fold-changes, usually from differential gene expression analyses. Here, we introduce a new rank-rank hypergeometric overlap-based method aimed at both gene level and alternative splicing analyses at transcript or exon level, hitherto unreachable as transcript numbers are an order of magnitude larger than gene numbers. ResultsWe tested the tool on synthetic and real datasets at gene and transcript levels to detect correlation and anti-correlation patterns and found it to be fast and accurate, even on very large datasets thanks to an evolutionary algorithm based minimal P-value search. The tool comes with a ready-to-use permutation scheme allowing the computation of adjusted P-values at low time cost. Additionally, the package is a drop-in replacement to previous packages as a compatibility mode is included, allowing to re-run older studies with close to no change to existing pipelines. RedRibbon holds the promise to accurately extricate detailed information from large analyses. AvailabilityRNA-sequencing datasets are available through the Gene Expression Omnibus (GEO) portal with accession numbers GSE159984, GSE133218, GSE137136, GSE98485, GSE148058 and GSE108413. The C libraries and R package code are open to the community with a permissive licence (GPL3) and available for download from GitHub https://github.com/antpiron/ale, https://github.com/antpiron/cRedRibbon and https://github.com/antpiron/RedRibbon. Contactanthony.piron@ulb.be

bioinformatics↗

bioRxiv2021

Explainability methods for differential gene analysis of single cell RNA-seq clustering models

Single-cell RNA sequencing (scRNA-seq) produces transcriptomic profiling for individual cells. Due to the lack of cell-class annotations, scRNA-seq is routinely analyzed with unsupervised clustering methods. Because these methods are typically limited to producing clustering predictions (that is, assignment of cells to clusters of similar cells), numerous model agnostic differential expression (DE) libraries have been proposed to identify the genes expressed differently in the detected clusters, as needed in the downstream analysis. In parallel, the advancements in neural networks (NN) brought several model-specific explainability methods to identify salient features based on gradients, eliminating the need for external models. We propose a comprehensive study to compare the performance of dedicated DE methods, with that of explainability methods typically used in machine learning, both model agnostic (such as SHAP, permutation importance) and model-specific (such as NN gradient-based methods). The DE analysis is performed on the results of 3 state-of-the-art clustering methods based on NNs. Our results on 36 simulated datasets indicate that all analyzed DE methods have limited agreement between them and with ground-truth genes. The gradients method outperforms the traditional DE methods, which en-courages the development of NN-based clustering methods to provide an out-of-the-box DE capability. Employing DE methods on the input data preprocessed by clustering method outperforms the traditional approach of using the original count data, albeit still performing worse than gradient-based methods.

bioinformatics↗

bioRxiv2021

Optimization algorithm for omic data subspace clustering

Subspace clustering identifies multiple feature subspaces embedded in a dataset together with the underlying sample clusters. When applied to omic data, subspace clustering is a challenging task, as additional problems have to be addressed: the curse of dimensionality, the imperfect data quality and cluster separation, the presence of multiple subspaces representative of divergent views of the dataset, and the lack of consensus on the best clustering method. First, we propose a computational method (discover) to perform subspace clustering on tabular high dimensional data by maximizing the internal clustering score (i.e. cluster compactness) of feature subspaces. Our algorithm can be used in both unsupervised and semi-supervised settings. Secondly, by applying our method to a large set of omic datasets (i.e. microarray, bulk RNA-seq, scRNA-seq), we show that the subspace corresponding to the provided ground truth annotations is rarely the most compact one, as assumed by the methods maximizing the internal quality of clusters. Our results highlight the difficulty of fully validating subspace clusters (justified by the lack of feature annotations). Tested on identifying the ground-truth subspace, our method compared favorably with competing techniques on all datasets. Finally, we propose a suite of techniques to interpret the clustering results biologically in the absence of annotations. We demonstrate that subspace clustering can provide biologically meaningful sample-wise and feature-wise information, typically missed by traditional methods. CCS Concepts: * Computing methodologies [->] Genetic algorithms; Mixture models; Cluster analysis. ACM Reference FormatMadalina Ciortan and Matthieu Defrance. 2021. Optimization algorithm for omic data subspace clustering. 1, 1 (September 2021), 40 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn

bioinformatics↗

bioRxiv2021

TIGER: The gene expression regulatory variation landscape of human pancreatic islets

GWAS have identified more than 700 genetic signals associated with type 2 diabetes (T2D). To gain insight into the underlying molecular mechanisms, we created the Translational human pancreatic Islet Genotype tissue-Expression Resource (TIGER), aggregating >500 human islet RNA-seq and genotyping datasets. We imputed genotypes using 4 reference panels and meta-analyzed cohorts to improve coverage of expression quantitative trait loci (eQTL) and developed a method to combine allele-specific expression across samples (cASE). We identified >1 million islet eQTLs (56% novel), of which 53 colocalize with T2D signals (60% novel). Among them, a low-frequency allele that reduces T2D risk by half increases CCND2 expression. We identified 8 novel cASE colocalizations, among which an SLC30A8 T2D associated variant. We make all the data available through the open-access TIGER portal (http://tiger.bsc.es), which represents a comprehensive human islet genomic data resource to elucidate how genetic variation affects islet function and translate this into therapeutic insight and precision medicine for T2D.

bioinformatics↗

bioRxiv2019

Combining single-cell RNA-sequencing with a molecular atlas unveils new markers for C. elegans neuron classes.

Single-cell RNA-sequencing (scRNA-seq) of the Caenorhabditis elegans (C. elegans) nervous system offers the unique opportunity to obtain a partial expression profile for each neuron within a known connectome. Building on recent scRNA-seq data [1] and on a molecular atlas describing the expression pattern of ~800 genes at the single cell resolution [2], we designed an iterative clustering analysis aiming to match each cell-cluster to the ~100 anatomically defined neuron classes of C. elegans. This heuristic approach successfully assigned 58 clusters to their corresponding neuron class. Another 11 clusters grouped neuron classes sharing close molecular signatures and 7 clusters were not assigned. Based on these 76 molecular profiles, we designed 15 new neuron class-specific promoters validated in vivo. Amongst them, 10 represent the only specific promoter reported to this day, expanding the list of neurons amenable to genetic manipulations. Finally, we observed a differential expression of functionally relevant genes between sensory-, inter-, and motor neurons in C. elegans, suggesting the mode of functional diversification may vary accordingly to the neuronal modalities.

genetics↗

bioRxiv2018

Distinct mesoderm migration phenotypes in extra-embryonic and embryonic regions of the early mouse embryo

In the gastrulating mouse embryo, epiblast cells delaminate at the primitive streak to form mesoderm and definitive endoderm, through an epithelial-mesenchymal transition.\n\nMosaic expression of a membrane reporter in nascent mesoderm enabled recording cell shape and trajectory through live imaging. Upon leaving the streak, cells changed shape and extended protrusions of distinct size and abundance depending on the neighboring germ layer, as well as the region of the embryo. Embryonic trajectories were meandrous but directional, while extra-embryonic mesoderm cells showed little net displacement.\n\nEmbryonic and extra-embryonic mesoderm transcriptomes highlighted distinct guidance, cytoskeleton, adhesion, and extracellular matrix signatures. Specifically, intermediate filaments were highly expressed in extra-embryonic mesoderm, while live imaging for F-actin showed abundance of actin filaments in embryonic mesoderm only. Accordingly, RhoA or Rac1 conditional deletion in mesoderm inhibited embryonic, but not extra-embryonic mesoderm migration.\n\nOverall, this indicates separate cytoskeleton regulation coordinating the morphology and migration of mesoderm subpopulations.

developmental biology↗