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The last 50 bibliographies

Physical and Genetic Assays for the Study of DNA Joint Molecules Metabolism and Multi-invasion-Induced Rearrangements in S. cerevisiae

Author(s) : Piazza A, Rajput P, Heyer W,
Journal : Methods in Molecular Biology
2021
DNA double-strand breaks (DSBs) are genotoxic lesions that can be repaired in a templated fashion by homologous recombination (HR). HR is a complex pathway that involves the formation of DNA joint molecules (JMs) containing heteroduplex DNA. Various types of JMs are formed throughout the pathway, including displacement loops (D-loops), multi-invasions (MI), and double Holliday junction intermediates. Dysregulation of JM metabolism in various mutant contexts revealed the propensity of HR to generate repeat-mediated chromosomal rearrangements. Specifically, we recently identified MI-induced rearrangements (MIR), a tripartite recombination mechanism initiated by one end of a DSB that exploits repeated regions to generate rearrangements between intact chromosomal regions. MIR occurs upon MI-JM processing by endonucleases and is suppressed by JM disruption activities. Here, we detail two assays: a physical assay for JM detection in Saccharomyces cerevisiae cells and genetic assays to determine the frequency of MIR in various chromosomal contexts. These assays enable studying the regulation of the HR pathway and the consequences of their defects for genomic instability by MIR.

[Circular RNA, actors and biomarkers of cancers]

Author(s) : Ladet J, Mortreux F,
Journal : Med Sci (Paris)
2020

ZRANB2 and SYF2-mediated splicing programs converging on ECT2 are involved in breast cancer cell resistance to doxorubicin

Author(s) : Tanaka I, Chakraborty A, Saulnier O, Benoit-Pilven C, Vacher S, Labiod D, Lam E, Bi?che I, Delattre O, Pouzoulet F, Auboeuf D, Vagner S, Dutertre M,
Journal : Nucleic Acids Res
2020

Physicochemical Foundations of Life that Direct Evolution: Chance and Natural Selection are not Evolutionary Driving Forces

Author(s) : Auboeuf D,
Journal : Life (Basel)
2020

Intragenic recruitment of NF-κB drives splicing modifications upon activation by the oncogene Tax of HTLV-1

Author(s) : Ameur L, Marie P, Thenoz M, Giraud G, Combe E, Claude J, Lemaire S, Fontrodona N, Polveche H, Bastien M, Gessain A, Wattel E, Bourgeois C, Auboeuf D, Mortreux F,
Journal : Nat Commun
2020

Cell-to-cell expression dispersion of B-cell surface proteins is linked to genetic variants in humans.

Author(s) : Triqueneaux G, Burny C, Symmons O, Janczarski S, Gruffat H, Yvert G,
Journal : Commun Biol
2020
Variability in gene expression across a population of homogeneous cells is known toinfluence various biological processes. In model organisms, natural genetic variantswere found that modify expression dispersion (variability at a fixed mean) but veryfew studies have detected such effects in humans. Here, we analyzed single-cellexpression of four proteins (CD23, CD55, CD63 and CD86) across cell lines derivedfrom individuals of the Yoruba population. Using data from over 30 million cells, wefound substantial inter-individual variation of dispersion. We demonstrate, via denovo cell line generation and subcloning experiments, that this variation exceedsthe variation associated with cellular immortalization. We detected a geneticassociation between the expression dispersion of CD63 and the rs971 SNP. Our resultsshow that human DNA variants can have inherently-probabilistic effects on geneexpression. Such subtle genetic effects may participate to phenotypic variation anddisease outcome.

Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software.

Author(s) : Decamps C, Prive F, Bacher R, Jost D, Waguet A, Houseman E, Lurie E, Lutsik P, Milosavljevic A, Scherer M, Blum M, Richard M,
Journal : BMC Bioinformatics
2020
BACKGROUND: Cell-type heterogeneity of tumors is a key factor in tumor progression and response to chemotherapy. Tumor cell-type heterogeneity, definedas the proportion of the various cell-types in a tumor, can be inferred from DNAmethylation of surgical specimens. However, confounding factors known to associate with methylation values, such as age and sex, complicate accurate inference of cell-type proportions. While reference-free algorithms have been developed to infer cell-type proportions from DNA methylation, a comparative evaluation of the performance of these methods is still lacking. RESULTS: Here we use simulations to evaluate several computational pipelines based on the software packages MeDeCom, EDec, and RefFreeEWAS. We identify that accounting for confounders, feature selection, and the choice of the number of estimated cell types are critical steps for inferring cell-type proportions. We find that removal of methylation probes which are correlated with confounder variables reduces the error of inference by 30-35%, and that selection of cell-type informative probes has similar effect. We show that Cattell's rule based on the scree plot is a powerful tool to determine the number of cell-types. Once the pre-processing steps are achieved, the three deconvolution methods provide comparable results. We observe that all the algorithms' performance improves when inter-sample variation of cell-type proportions is large or when the number of available samples is large. We find that under specific circumstances the methods are sensitive to the initialization method, suggesting that averaging different solutions or optimizing initialization is an avenue for future research. CONCLUSION: Based on the lessons learned, to facilitate pipeline validation and catalyze further pipeline improvement by the community, we develop a benchmark pipeline for inference of cell-type proportions and implement it in the R package medepir.

4D Genome Rewiring during Oncogene-Induced and Replicative Senescence.

Author(s) : Sati S, Bonev B, Szabo Q, Jost D, Bensadoun P, Serra F, Loubiere V, Papadopoulos G, Rivera-Mulia J, Fritsch L, Bouret P, Castillo D, Gelpi J, Orozco M, Vaillant C, Pellestor F, Bantignies F, Marti-Renom M, Gilbert D, Lemaitre J, Cavalli G,
Journal : Mol Cell
2020
To understand the role of the extensive senescence-associated 3D genome reorganization, we generated genome-wide chromatin interaction maps, epigenome, replication-timing, whole-genome bisulfite sequencing, and gene expression profiles from cells entering replicative senescence (RS) or upon oncogene-induced senescence (OIS). We identify senescence-associated heterochromatin domains (SAHDs). Differential intra- versus inter-SAHD interactions lead to the formation of senescence-associated heterochromatin foci (SAHFs) in OIS but not in RS. ThisOIS-specific configuration brings active genes located in genomic regions adjacent to SAHDs in close spatial proximity and favors their expression. We also identify DNMT1 as a factor that induces SAHFs by promoting HMGA2 expression. Upon DNMT1 depletion, OIS cells transition to a 3D genome conformation akin to that of cells in replicative senescence. These data show how multi-omics and imaging canidentify critical features of RS and OIS and discover determinants of acute senescence and SAHF formation.

Chromosome dynamics during interphase: a biophysical perspective.

Author(s) : Tortora M, Salari H, Jost D,
Journal : Curr Opin Genet Dev
2020
The dynamic nature of chromosome organization plays a central role in the regulation of many crucial processes, such as DNA transcription and replication.However, the molecular bases of the link between genomic function, structure anddynamics remain elusive. In this review, we focus on how biophysical modelling can be instrumentally used to rationalize experimental studies of chromosome dynamics, and to probe the impact of putative mechanisms on genome folding kinetics during interphase. We introduce the general connection between chromatin internal organization and dynamics, and outline the potential effects of passiveinteractions mediated by architectural proteins and of active, energy-dependent processes on chromatin motion. Finally, we discuss current ambiguities emerging from in vivo observations, in particular related to ATP depletion and transcriptional activation, and highlight future perspectives.

PenDA, a rank-based method for personalized differential analysis: Application to lung cancer.

Author(s) : Richard M, Decamps C, Chuffart F, Brambilla E, Rousseaux S, Khochbin S, Jost D,
Journal : PLoS Comput Biol
2020
The hopes of precision medicine rely on our capacity to measure various high-throughput genomic information of a patient and to integrate them for personalized diagnosis and adapted treatment. Reaching these ambitious objectives will require the development of efficient tools for the detection of molecular defects at the individual level. Here, we propose a novel method, PenDA, to perform Personalized Differential Analysis at the scale of a single sample. PenDA is based on the local ordering of gene expressions within individual cases and infers the deregulation status of genes in a sample of interest compared to a reference dataset. Based on realistic simulations of RNA-seq data of tumors, we showed that PenDA outcompetes existing approaches with very high specificity andsensitivity and is robust to normalization effects. Applying the method to lung cancer cohorts, we observed that deregulated genes in tumors exhibit a cancer-type-specific commitment towards up- or down-regulation. Based on the individual information of deregulation given by PenDA, we were able to define two new molecular histologies for lung adenocarcinoma cancers strongly correlated tosurvival. In particular, we identified 37 biomarkers whose up-regulation lead tobad prognosis and that we validated on two independent cohorts. PenDA provides arobust, generic tool to extract personalized deregulation patterns that can thenbe used for the discovery of therapeutic targets and for personalized diagnosis.An open-access, user-friendly R package is available at https://github.com/bcm-uga/penda.