Biology subjects

Navarro, P.

Publications and source records attributed to Navarro, P..

5 recordsLinked to original sources

bioRxiv2018

Transcription factor activity and nucleosome organisation in mitosis

Mitotic bookmarking transcription factors (BFs) maintain the capacity to bind to their targets during mitosis, despite major rearrangements of the chromatin. While they were thought to propagate gene regulatory information through mitosis by statically occupying their DNA targets, it has recently become clear that BFs are highly dynamic in mitotic cells. This represents both a technical and a conceptual challenge to study and understand the function of BFs: first, formaldehyde has been suggested to be unable to efficiently capture these transient interactions, leading to profound contradictions in the literature; second, if BFs are not permanently bound to their targets during mitosis, it becomes unclear how they convey regulatory information to daughter cells. Here, comparing formaldehyde to alternative fixatives we clarify the nature of the chromosomal association of previously proposed BFs in embryonic stem cells: while Esrrb can be considered as a canonical BF that binds at selected regulatory regions in mitosis, Sox2 and Oct4 establish DNA sequence independent interactions with the mitotic chromosomes, either throughout the chromosomal arms (Sox2) or at pericentromeric regions (Oct4). Moreover, we show that ordered nucleosomal arrays are retained during mitosis at Esrrb book-marked sites, whereas regions losing transcription factor binding display a profound loss of order. By maintaining nucleosome positioning during mitosis, Esrrb might ensure the rapid post-mitotic re-establishment of functional regulatory complexes at selected enhancers and promoters. Our results provide a mechanistic framework that reconciles dynamic mitotic binding with the transmission of gene regulatory information across cell division.

genomics↗

bioRxiv2018

The molecular logic of Nanog-induced self-renewal

Transcription factor networks, together with histone modifications and signalling pathways, underlie the establishment and maintenance of gene regulatory architectures associated with the molecular identity of each cell type. However, how master transcription factors individually impact the epigenomic landscape and orchestrate the behaviour of regulatory networks under different environmental constraints is only very partially understood. Here, we show that the transcription factor Nanog deploys multiple distinct mechanisms to enhance embryonic stem cell self-renewal. In the presence of LIF, which fosters self-renewal, Nanog rewires the pluripotency network by promoting chromatin accessibility and binding of other pluripotency factors to thousands of enhancers. In the absence of LIF, Nanog blocks differentiation by sustaining H3K27me3, a repressive histone mark, at developmental regulators. Among those, we show that the repression of Otx2 plays a preponderant role. Our results underscore the versatility of master transcription factors, such as Nanog, to globally influence gene regulation during developmental processes.

genomics↗

bioRxiv2017

Genetic and environmental determinants of stressful life events and their overlap with depression and neuroticism

BackgroundStressful life events (SLEs) and neuroticism are risk factors for major depressive disorder (MDD). However, SLEs and neuroticism are heritable traits that are correlated with genetic risk for MDD. In the current study, we sought to investigate the genetic and environmental contributions to SLEs in a large family-based sample, and quantify any genetic overlap with MDD and neuroticism.\n\nMethodsA subset of Generation Scotland: the Scottish Family Health Study, consisting of 9618 individuals comprise the present study. We estimated the heritability of SLEs using pedigree-based and molecular genetic data. The environment was assessed by modelling familial, couple and sibling components. Using polygenic risk scores (PRS) and LD score regression we analysed the genetic overlap between MDD, neuroticism and SLEs.\n\nResultsPast 6-month life events were positively correlated with lifetime MDD status ({beta}=0.21, r2=1.1%, p=2.5 x 10-25) and neuroticism ({beta} =0.13, r2=1.9%, p=1.04 x 10-37). Common SNPs explained 8% of the variance in personal life events (those directly affecting the individual) (S.E.=0.03, p=9 x 10-4). A significant effect of couple environment accounted for 13% (S.E.=0.03, p=0.016) of variation in SLEs. PRS analyses found that individuals with higher PRS for MDD reported more SLEs ({beta} =0.05, r2=0.3%, p=3 x 10-5). LD score regression demonstrated genetic correlations between MDD and both SLEs (rG=0.33, S.E.=0.08) and neuroticism (rG=0.15, S.E.=0.07).\n\nConclusionsThese findings suggest that SLEs are partially heritable and this heritability is shared with risk for MDD and neuroticism. Further work should determine the causal direction and source of these associations.

genetics↗

bioRxiv2017

QuiXoT: quantification and statistics of high-throughput proteomics by stable isotope labelling

AbstractIn most software tools for quantification of mass spectrometry-based proteomics by stable isotope labelling (SIL), there is a recurrent disconnection between the use of a statistical model and convenient data visualisation to check correct data modelling. Most of them lack a robust statistical framework, using models which do not account for the major difficulties in proteomics, such as the unbalanced peptide-protein distribution, undersampling, or the correct separation of sources of variance. This makes especially difficult the interpretation of quantitative proteomics experiments. Here we present QuiXoT, an extensively tested quantification and statistics open source software based on a robust and extensively validated statistical model, the WSPP (weighted spectrum, peptide, and protein). Its associated software package allows the user to visually represent and inspect results at all modelled levels (scan, peptide and protein) on routine bases. It is applicable to practically any SIL method (SILAC, iTRAQ, and 18O among others) or MS instrument.

bioinformatics↗

bioRxiv2017

Genomic analysis of family data reveals additional genetic effects on intelligence and personality

Pedigree-based analyses of intelligence have reported that genetic differences account for 50-80% of the phenotypic variation. For personality traits these effects are smaller, with 34-48% of the variance being explained by genetic differences. However, molecular genetic studies using unrelated individuals typically report a heritability estimate of around 30% for intelligence and between 0% and 15% for personality variables. Pedigree-based estimates and molecular genetic estimates may differ because current genotyping platforms are poor at tagging causal variants, variants with low minor allele frequency, copy number variants, and structural variants. Using [~]20 000 individuals in the Generation Scotland family cohort genotyped for [~]700 000 single nucleotide polymorphisms (SNPs), we exploit the high levels of linkage disequilibrium (LD) found in members of the same family to quantify the total effect of genetic variants that are not tagged in GWASs of unrelated individuals. In our models, genetic variants in low LD with genotyped SNPs explain over half of the genetic variance in intelligence, education, and neuroticism. By capturing these additional genetic effects our models closely approximate the heritability estimates from twin studies for intelligence and education, but not for neuroticism and extraversion. We then replicated our finding using imputed molecular genetic data from unrelated individuals to show that [~]50% of differences in intelligence, and [~]40% of the differences in education, can be explained by genetic effects when a larger number of rare SNPs are included. From an evolutionary genetic perspective, a substantial contribution of rare genetic variants to individual differences in intelligence and education is consistent with mutation-selection balance.

genetics↗