Fast genomic μChIP-chip from 1,000 cells

A new method for rapid genome-wide μChIP-chip from as few as 1,000 cells

HA95 and LAP2β mediate a novel chromatin–nuclear envelope interaction implicated in initiation of DNA replication

HA95 is a chromatin-associated protein that interfaces the nuclear envelope (NE) and chromatin. We report an interaction between HA95 and the inner nuclear membrane protein lamina-associated polypeptide (LAP) 2β, and a role of this association in initiation of DNA replication. Precipitation of GST–LAP2β fusion proteins and overlays of immobilized HA95 indicate that a first HA95-binding region lies within amino acids 137–242 of LAP2β. A second domain sufficient to bind HA95 colocalizes with the lamin B–binding domain of LAP2β at residues 299–373. HA95–LAP2β interaction is not required for NE formation. However, disruption of the association of HA95 with the NH2-terminal HA95-binding domain of LAP2β abolishes the initiation, but not elongation, of DNA replication in purified G1 phase nuclei incubated in S-phase extract. Inhibition of replication initiation correlates with proteasome-mediated proteolysis of Cdc6, a component of the prereplication complex. Rescue of Cdc6 degradation with proteasome inhibitors restores replication. We propose that an interaction of LAP2β, or LAP2 proteins, with HA95 is involved in the control of initiation of DNA replication

Robust Geometric Metric Learning

This paper proposes new algorithms for the metric learning problem. We start by noticing that several classical metric learning formulations from the literature can be viewed as modified covariance matrix estimation problems. Leveraging this point of view, a general approach, called Robust Geometric Metric Learning (RGML), is then studied. This method aims at simultaneously estimating the covariance matrix of each class while shrinking them towards their (unknown) barycenter. We focus on two specific costs functions: one associated with the Gaussian likelihood (RGML Gaussian), and one with Tyler's M -estimator (RGML Tyler). In both, the barycenter is defined with the Riemannian distance, which enjoys nice properties of geodesic convexity and affine invariance. The optimization is performed using the Riemannian geometry of symmetric positive definite matrices and its submanifold of unit determinant. Finally, the performance of RGML is asserted on real datasets. Strong performance is exhibited while being robust to mislabeled data.Comment: Published in EUSIPCO 2022. Best student paper awar

Riemannian optimization for non-centered mixture of scaled Gaussian distributions

This paper studies the statistical model of the non-centered mixture of scaled Gaussian distributions (NC-MSG). Using the Fisher-Rao information geometry associated to this distribution, we derive a Riemannian gradient descent algorithm. This algorithm is leveraged for two minimization problems. The first one is the minimization of a regularized negative log- likelihood (NLL). The latter makes the trade-off between a white Gaussian distribution and the NC-MSG. Conditions on the regularization are given so that the existence of a minimum to this problem is guaranteed without assumptions on the samples. Then, the Kullback-Leibler (KL) divergence between two NC-MSG is derived. This divergence enables us to define a minimization problem to compute centers of mass of several NC-MSGs. The proposed Riemannian gradient descent algorithm is leveraged to solve this second minimization problem. Numerical experiments show the good performance and the speed of the Riemannian gradient descent on the two problems. Finally, a Nearest centroid classifier is implemented leveraging the KL divergence and its associated center of mass. Applied on the large scale dataset Breizhcrops, this classifier shows good accuracies as well as robustness to rigid transformations of the test set

Histone H3 Lysine 27 Methylation Asymmetry on Developmentally-Regulated Promoters Distinguish the First Two Lineages in Mouse Preimplantation Embryos

First lineage specification in the mammalian embryo leads to formation of the inner cell mass (ICM) and trophectoderm (TE), which respectively give rise to embryonic and extraembryonic tissues. We show here that this first differentiation event is accompanied by asymmetric distribution of trimethylated histone H3 lysine 27 (H3K27me3) on promoters of signaling and developmentally-regulated genes in the mouse ICM and TE. A genome-wide survey of promoter occupancy by H3K4me3 and H3K27me3 indicates that both compartments harbor promoters enriched in either modification, and promoters co-enriched in trimethylated H3K4 and H3K27 linked to developmental and signaling functions. The majority of H3K4/K27me3 co-enriched promoters are distinct between the two lineages, primarily due to differences in the distribution of H3K27me3. Derivation of embryonic stem cells leads to significant losses and gains of H3K4/K27me3 co-enriched promoters relative to the ICM, with distinct contributions of (de)methylation events on K4 and K27. Our results show histone trimethylation asymmetry on promoters in the first two developmental lineages, and highlight an epigenetic skewing associated with embryonic stem cell derivation