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Ribosomal RNA (rRNA) production represents the most active transcription in the cell. Synthesis of the large rRNA precursors (35S/47S in yeast/human) is achieved by up to hundreds of RNA polymerase I (Pol I) enzymes simultaneously transcribing a single rRNA gene. In this review, we present recent advances in understanding the coupling between rRNA production and nascent rRNA folding. Mapping of the distribution of Pol I along ribosomal DNA at nucleotide resolution, using either native elongating transcript sequencing (NET-Seq) or crosslinking and analysis of cDNAs (CRAC), revealed frequent Pol I pausing, and CRAC results revealed a direct coupling between pausing and nascent RNA folding. High density of Pol I per gene imposes topological constraints that establish a defined pattern of polymerase distribution along the gene, with a persistent spacing between transcribing enzymes. RNA folding during transcription directly acts as an anti-pausing mechanism, implying that proper folding of the nascent rRNA favors elongation in vivo. Defects in co-transcriptional folding of rRNA are likely to induce Pol I pausing. We propose that premature termination of transcription, at defined positions, can control rRNA production in vivo.

While many computational methods have been proposed for 3D chromosome reconstruction from chromosomal contact maps, these methods are rarely used for the interpretation of such experimental data, in particular Hi-C data. We posit that this is due to the lack of an easy-to-use implementation of the proposed algorithms, as well as to the important computational cost of most methods. We here give a detailed implementation of the fast ShRec3D algorithm. We provide a tutorial that will enable the reader to reconstruct 3D consensus structures for human chromosomes and to decorate these structures with chromatin epigenetic states. We use this methodology to show that the bivalent chromatin, including Polycomb-rich domains, is spatially segregated and located in between the active and the quiescent chromatin compartments.

While many computational methods have been proposed for 3D chromosome reconstruction from chromosomal contact maps, these methods are rarely used for the interpretation of such experimental data, in particular Hi-C data. We posit that this is due to the lack of an easy-to-use implementation of the proposed algorithms, as well as to the important computational cost of most methods. We here give a detailed implementation of the fast ShRec3D algorithm. We provide a tutorial that will enable the reader to reconstruct 3D consensus structures for human chromosomes and to decorate these structures with chromatin epigenetic states. We use this methodology to show that the bivalent chromatin, including Polycomb-rich domains, is spatially segregated and located in between the active and the quiescent chromatin compartments.

Background Genome-wide association studies have identified statistical associations between various diseases, including cancers, and a large number of single-nucleotide polymorphisms (SNPs). However, they provide no direct explanation of the mechanisms underlying the association. Based on the recent discovery that changes in three-dimensional genome organization may have functional consequences on gene regulation favoring diseases, we investigated systematically the genome-wide distribution of disease-associated SNPs with respect to a specific feature of 3D genome organization: topologically associating domains (TADs) and their borders. Results For each of 449 diseases, we tested whether the associated SNPs are present in TAD borders more often than observed by chance, where chance (i.e., the null model in statistical terms) corresponds to the same number of pointwise loci drawn at random either in the entire genome, or in the entire set of disease-associated SNPs listed in the GWAS catalog. Our analysis shows that a fraction of diseases displays such a preferential localization of their risk loci. Moreover, cancers are relatively more frequent among these diseases, and this predominance is generally enhanced when considering only intergenic SNPs. The structure of SNP-based diseasome networks confirms that localization of risk loci in TAD borders differs between cancers and non-cancer diseases. Furthermore, different TAD border enrichments are observed in embryonic stem cells and differentiated cells, consistent with changes in topological domains along embryogenesis and delineating their contribution to disease risk. Conclusions Our results suggest that, for certain diseases, part of the genetic risk lies in a local genetic variation affecting the genome partitioning in topologically insulated domains. Investigating this possible contribution to genetic risk is particularly relevant in cancers. This study thus opens a way of interpreting genome-wide association studies, by distinguishing two types of disease-associated SNPs: one with an effect on an individual gene, the other acting in interplay with 3D genome organization., Human Genomics, 16 (1), ISSN:1473-9542, ISSN:1479-7364

Genome-wide association studies have identified statistical associations between various diseases, including cancers, and a large number of single-nucleotide polymorphisms (SNPs). However, they provide no direct explanation of the mechanisms underlying the association. Based on the recent discovery that changes in 3-dimensional genome organization may have functional consequences on gene regulation favoring diseases, we investigated systematically the genome-wide distribution of disease-associated SNPs with respect to a specific feature of 3D genome organization: topologically-associating domains (TADs) and their borders.For each of 449 diseases, we tested whether the associated SNPs are present in TAD borders more often than observed by chance, where chance (i.e. the null model in statistical terms) corresponds to the same number of pointwise loci drawn at random either in the entire genome, or in the entire set of disease-associated SNPs listed in the GWAS catalog. Our analysis shows that a fraction of diseases display such a preferential location of their risk loci. Moreover, cancers are relatively more frequent among these diseases, and this predominance is generally enhanced when considering only intergenic SNPs. The structure of SNP-based diseasome networks confirms that TAD border enrichment in risk loci differ between cancers and non-cancer diseases. Different TAD border enrichments are observed in embryonic stem cells and differentiated cells, which agrees with an evolution along embryogenesis of the 3D genome organization into topological domains.Our results suggest that, for certain diseases, part of the genetic risk lies in a local genetic variation affecting the genome partitioning in topologically-insulated domains. Investigating this possible contribution to genetic risk is particularly relevant in cancers. This study thus opens a way of interpreting genome-wide association studies, by distinguishing two types of disease-associated SNPs: one with a direct effect on an individual gene, the other acting in interplay with 3D genome organization.Author summaryGenome-wide association studies comparing patients and healthy subjects have evidenced correlations between diseases and the presence of pointwise genetic variations known as single-nucleotide polymorphisms (SNPs). We exploit and extend this statistical analysis by investigating the location of risk loci, i.e. disease-associated SNPs, with respect to the 3D organization of the genome into spatially-insulated domains, the topologically-associating domains (TADs).We show that for certain diseases, mostly cancers, their associated risk loci are preferentially located in the borders of these topological domains. The predominance of cancers among these diseases is confirmed and even enhanced when considering only intergenic SNPs. A different enrichment behavior is observed in embryonic stem cells and derived cell lines at an early developmental stage, presumably due to the not fully mature TAD structure in these cells.Overall, our results show that genome variations in specific TAD borders may increase the risk of developing certain diseases, especially cancers. Our work underlines the importance of considering the genetic risk loci within their 3D genomic context, and suggests a role of 3D genome partitioning into topological domains in the genetic risk which differs between cancers and non-cancer diseases.

Reaction-diffusion systems implemented as dynamical processes on networks have recently renewed the interest in their self-organized collective patterns known as Turing patterns. We investigate the influence of network topology on the emerging patterns and their diversity, defined as the variety of stationary states observed with random initial conditions and the same dynamics. We show that a seemingly minor change, the removal or rewiring of a single link, can prompt dramatic changes in pattern diversity. The determinants of such critical occurrences are explored through an extensive and systematic set of numerical experiments. We identify situations where the topological sensitivity of the attractor landscape can be predicted without a full simulation of the dynamical equations, from the spectrum of the graph Laplacian and the linearized dynamics. Unexpectedly, the main determinant appears to be the degeneracy of the eigenvalues or the growth rate and not the number of unstable modes.

De nombreux scientifiques ont etudie l'apparition des formes vivantes a partir du mineral. Des notions fondamentales ont ainsi emerge, allant de la caracterisation des materiaux du vivant a leur auto-organisation par des processus actifs, consommant de l'energie pour produire des structures ordonnees. Dans un cheminement inverse, les artistes ceramistes ont explore les differentes facons de reproduire les formes vivantes dans la matiere minerale, pour depasser la dualite entre anime et inanime, entre vivant et inorganique. De nouveaux points de rencontre entre les formes vivantes et la ceramique se situent aujourd'hui dans les materiaux bio-inspires et les morphogeneses organiques.

accepted 2020-08-04; International audience; Recurrence quantification analysis (RQA) is an acknowledged method for the characterization of experimental time series. We propose a parametric version of RQA, pRQA, allowing a fast processing of spatial arrays of time series, once each is modeled by an autoregressive stochastic process. This method relies on the analytical derivation of asymptotic expressions for five current RQA measures as a function of the model parameters. By avoiding the construction of the recurrence plot of the time series, pRQA is computationally efficient. As a proof of principle, we apply pRQA to pattern recognition in multichannel electroencephalographic (EEG) data from a patient with a brain tumor.

International audience; When nonlinear measures are estimated from sampled temporal signals with finitelength, a radius parameter must be carefully selected to avoid a poor estimation. These measures are generally derived from the correlation integral which quantifies the probability of finding neighbors, i.e. pair of points spaced by less than the radius parameter. While each nonlinear measure comes with several specific empirical rules to select a radius value, we provide a systematic selection method. We show that the optimal radius for nonlinear measures can be approximated by the optimal bandwidth of a Kernel Density Estimator (KDE) related to the correlation sum. The KDE framework provides non-parametric tools to approximate a density function from finite samples (e.g. histograms) and optimal methods to select a smoothing parameter, the bandwidth (e.g. bin width in histograms). We use results from KDE to derive a closed-form expression for the optimal radius. The latter is used to compute the correlation dimension and to construct recurrence plots yielding an estimate of Kolmogorov-Sinai entropy. We assess our method through numerical experiments on signals generated by nonlinear systems and experimental electroencephalographic time series.

La molécule d'ADN est le support physique de l'information génétique. Ce long polymère est compacté grâce à des protéines pour former les chromosomes. Ce repliement assure la régulation de l’expression des gènes au cours de la vie des cellules. La biochimie des chromosomes est un sujet d'intenses investigations, mais il apparait aujourd'hui que les propriétés physiques des chromosomes sont aussi au coeur de leur fonctionnement. Ces propriétés peuvent être décrites en combinant physique statistique et physique des polymères en solution. Dans cet article, nous présentons les méthodes multi-échelles que nous avons développées pour reconstruire et animer l'architecture fonctionnelle des chromosomes.

We revisit the notion of gene regulatory code in embryonic development in the light of recent findings about genome spatial organisation. By analogy with the genetic code, we posit that the concept of code can only be used if the corresponding adaptor can clearly be identified. An adaptor is here defined as an intermediary physical entity mediating the correspondence between codewords and objects in a gratuitous and evolvable way. In the context of the gene regulatory code, the encoded objects are the gene expression levels, while the concentrations of specific transcription factors in the cell nucleus provide the codewords. The notion of code is meaningful in the absence of direct physicochemical relationships between the objects and the codewords, when the mediation by an adaptor is required. We propose that a plausible adaptor for this code is the gene domain, that is, the genome segment delimited by topological insulators and comprising the gene and its enhancer regulatory sequences. We review recent evidences, based on genome-wide chromosome conformation capture experiments, showing that preferential contact domains found in metazoan genomes are the physical traces of gene domains. Accordingly, genome 3D folding plays a direct role in shaping the developmental gene regulatory code., Comment: Journal of Molecular Biology, Elsevier, 2019

International audience; The importance of genome organization at the supranucleosomal scale in the control of gene expression is increasingly recognized today. In mammals, Topologically Associating Domains (TADs) and the active/inactive chromosomal compartments are two of the main nuclear structures that contribute to this organization level. However, recent works reviewed here indicate that, at specific loci, chromatin interactions with nuclear bodies could also be crucial to regulate genome functions, in particular transcription. They moreover suggest that these nuclear bodies are membrane-less organelles dynamically self-assembled and disassembled through mechanisms of phase separation. We have recently developed a novel genome-wide experimental method, High-salt Recovered Sequences sequencing (HRS-seq), which allows the identification of chromatin regions associated with large ribonucleoprotein (RNP) complexes and nuclear bodies. We argue that the physical nature of such RNP complexes and nuclear bodies appears to be central in their ability to promote efficient interactions between distant genomic regions. The development of novel experimental approaches, including our HRS-seq method, is opening new avenues to understand how self-assembly of phase-separated nuclear bodies possibly contributes to mammalian genome organization and gene expression.

International audience; Given a pattern on a network, i.e. a subset of nodes, can we assess, whether they are randomly distributed on the network or have been generated in a systematic fashion following the network architecture? This question is at the core of network-based data analyses across a range of disciplines-from incidents of infection in social networks to sets of differentially expressed genes in biological networks. Here we introduce generic 'pattern generators' based on an Eden growth model. We assess the capacity of different pattern measures like connectivity, edge density or various average distances, to infer the parameters of the generator from the observed patterns. Some measures perform consistently better than others in inferring the underlying pattern generator, while the best performing measures depend on the global topology of the underlying network. Moreover, we show that pattern generator inference remains possible in case of limited visibility of the patterns.

We investigate the statistical properties of recurrence plots (RPs) of data generated by discrete-time stationary Gaussian random processes. We analytically derive the theoretical values of the probabilities of occurrence of recurrence points and consecutive recurrence points forming diagonals in the RP, with an embedding dimension equal to 1 . These results allow us to obtain theoretical values of three measures: (i) the recurrence rate ( R E C ) (ii) the percent determinism ( D E T ) and (iii) RP-based estimation of the e -entropy κ ( e ) in the sense of correlation entropy. We apply these results to two Gaussian processes, namely first order autoregressive processes and fractional Gaussian noise. For these processes, we simulate a number of realizations and compare the RP-based estimations of the three selected measures to their theoretical values. These comparisons provide useful information on the quality of the estimations, such as the minimum required data length and threshold radius used to construct the RP.

We investigate the kinetics of a polymer collapse due to the formation of irreversible crosslinks between its monomers. Using the contact probability $P(s)$ as a scale-dependent order parameter depending on the chemical distance $s$, our simulations show the emergence of a cooperative pearling instability. Namely, the polymer undergoes a sharp conformational transition to a set of absorbing states characterized by a length scale $\xi$ corresponding to the mean pearl size. This length and the transition time depend on the polymer equilibrium dynamics and the crosslinking rate. We confirm experimentally this transition using a DNA conformation capture experiment in yeast., Comment: Article: 12 pages, 4 figures. Supplementary material: 22 pages, 7 supplementary figures

RNA polymerase (RNAP) is, in its elongation phase, an emblematic example of a molecular motor whose activity is highly sensitive to DNA supercoiling. After a review of DNA supercoiling basic features, we discuss how supercoiling controls polymerase velocity, while being itself modified by polymerase activity. This coupling is supported by single-molecule measurements. Physical modeling allows us to describe quantitatively how supercoiling and torsional constraints mediate a mechanical coupling between adjacent polymerases. On this basis, we obtain a description that may explain the existence and functioning of RNAP convoys.

RNA polymerase (RNAP) is, in its elongation phase, an emblematic example of a molecular motor whose activity is highly sensitive to DNA supercoiling. After a review of DNA supercoiling basic features, we discuss how supercoiling controls polymerase velocity, while being itself modified by polymerase activity. This coupling is supported by single-molecule measurements. Physical modeling allows us to describe quantitatively how supercoiling and torsional constraints mediate a mechanical coupling between adjacent polymerases. On this basis, we obtain a description that may explain the existence and functioning of RNAP convoys.

The mammalian cell nucleus contains numerous discrete suborganelles named nuclear bodies. While recruitment of specific genomic regions into these large ribonucleoprotein (RNP) complexes critically contributes to higher-order functional chromatin organization, such regions remain ill-defined. We have developed the high-salt–recovered sequences-sequencing (HRS-seq) method, a straightforward genome-wide approach whereby we isolated and sequenced genomic regions associated with large high-salt insoluble RNP complexes. By using mouse embryonic stem cells (ESCs), we showed that these regions essentially correspond to the most highly expressed genes, and to cis-regulatory sequences like super-enhancers, that belong to the active A chromosomal compartment. They include both cell-type–specific genes, such as pluripotency genes in ESCs, and housekeeping genes associated with nuclear bodies, such as histone and snRNA genes that are central components of Histone Locus Bodies and Cajal bodies. We conclude that HRSs are associated with the active chromosomal compartment and with large RNP complexes including nuclear bodies. Association of such chromosomal regions with nuclear bodies is in agreement with the recently proposed phase separation model for transcription control and might thus play a central role in organizing the active chromosomal compartment in mammals.

As rock textures reflect the physical conditions and the mechanisms of formation of the rocks, new approaches are used for improving texture analyses, both qualitatively and quantitatively. Pioneer work has recently boosted interest in fractal analysis for quantifying and correlating patterns. Fractal-like patterns relate to a degree of multiscale organization, and fractal dimensions (FD) and their potential variations can be used to infer the physical conditions of rock formation at various scales of observation. Here, we characterize quantitatively the shape and distribution of orthopyroxene grains in ultramafic xenoliths in terms of FD and their relation with temperature of equilibration. Fractal analysis has been applied to several populations of mantle xenoliths: 7 xenoliths collected in the vicinity of Pico Santa Isabel on Bioko Island, an alkaline basaltic volcano in oceanic domain (Gulf of Guinea, Equatorial Atlantic), 9 samples from Sangilen, in the Agardag alkaline lamprophyre dyke (Russia), and 11 samples form Śnieznik (Lutynia, Poland), in the continental domain. Fractal analysis has been conducted to characterize the degree of complexity of the petrographic textures: it is indeed known that large values of FD are associated to more complex textures. The correlation here observed between the orthopyroxene fractal dimension and the temperature of equilibration suggests that FD captures a significant textural feature directly related to the temperature (i.e. generated by a temperature-controlled process). The significant difference in the FD–T correlation observed for the continental and oceanic mantle domains suggests that the mechanical and rheological behaviour is distinct in the oceanic and continental lithospheres. These first promising results should be confirmed by analysing other mantle suites of rocks in different geodynamic settings. Copyright © 2015 John Wiley & Sons, Ltd.

International audience; Plant leaves host species rich communities of foliar endophytic fungi (FEF). Compared to the other compartments of the plant microbiome, FEF diversity is poorly known. Here we document the communities of FEF associated with the endemic Corsican pine Pinus nigra subsp. laricio at three sites across its natural range and examine the effect of forest site, tree age and light exposure on FEF composition. Metabarcoding using next-generation sequencing provided 8,243,608 Ascomycota ITS2 sequences clustered into 642 FEF operational taxonomic units (OTUs). FEF communities did not differ in species richness across sites and tree cohorts, but differed in composition among forest patches and according to tree age. FEF community composition did not correlate with needle location (shade vs full light). Results are robust against the various options of the bioinformatic pipeline specifically developed. This study provides the first picture of FEF diversity in a Mediterranean island and underlines the complementarity of forest massifs for fungal conservation.

(peer-reviewed, accepted 2019-05-14); International audience; We present our use of analytical mean-field approaches in investigating how the interplay between graph topology and excitable dynamics produce spatio-temporal patterns. We first detail the derivation of mean-field equations for a few simple model situations, mainly 3-state discrete-time excitable dynamics with an absolute or a relative excita-tion threshold. Comparison with direct numerical simulation shows that their solution satisfactorily predicts the steady-state excitation density. In contrast, they often fail to capture more complex dynamical features, however we argue that the analysis of this failure is in itself insightful, by pinpointing the key role of mechanisms neglected in the mean-field approach. Moreover, we show how second-order mean-field approaches, in which a topological object (e.g. a cycle or a hub) is considered as embedded in a mean-field surrounding, allow us to go beyond the spatial homogenization currently associated with plain mean-field calculations. The confrontation between these refined analytical predictions and simulation quantitatively evidences the specific contribution of this topological object to the dynamics. Mathematics Subject Classification (2010). Primary 05C82; Secondary 92C42.

Emotional interactions have been considered dynamical processes involved in the affective life of humans and their disturbances may induce mental disorders. Most studies of emotional interactions have focused on dyadic behaviors or self-reports of emotional states but neglected the dynamical processes involved in family therapy. The main objective of this study is to quantify the dynamics of emotional expressions and their changes using the family therapy of patients with anorexia nervosa as an example. Nonlinear methods characterize the variability of the dynamics at the level of the whole therapeutic system and reciprocal influence between the participants during family therapy. Results show that the variability of the dynamics is higher at the end of the therapy than at the beginning. The reciprocal influences between therapist and each member of the family and between mother and patient decrease with the course of family therapy. Our results support the development of new interpersonal strategies of emotion regulation during family therapy. The quantification of emotional dynamics can help understanding the emotional processes underlying psychopathology and evaluating quantitatively the changes achieved by the therapeutic intervention. 253

Simple models of excitable dynamics on graphs are an efficient framework for studying the interplay between network topology and dynamics. This topic is of practical relevance to diverse fields, ranging from neuroscience to engineering. Here we analyze how a single excitation propagates through a random network as a function of the excitation threshold, that is, the relative amount of activity in the neighborhood required for the excitation of a node. We observe that two sharp transitions delineate a region of sustained activity. Using analytical considerations and numerical simulation, we show that these transitions originate from the presence of barriers to propagation and the excitation of topological cycles, respectively, and can be predicted from the network topology. Our findings are interpreted in the context of network reverberations and self-sustained activity in neural systems, which is a question of long-standing interest in computational neuroscience.

International audience; The notion of disease-associated single-nucleotide polymorphisms (da-SNP), as determined in genome-wide association studies (GWAS), is relevant for many complex pathologies, including cancers. It appeared that da-SNPs are not only markers of causal genetic variation but may contribute to the disease development through an influence on gene expression levels. We argue that understanding this possible functional role of da-SNPs requires to consider their embedding in the tridimensional (3D) multi-scale organization of the human genome. We then focus on the potential impact of da-SNPs on chromatin loops and recently observed topologically associating domains (TADs). We show that for some diseases and cancer types, da-SNPs are over-represented in the borders of these topological domains, in a way that cannot be explained by an increased exon density. This analysis of the distribution of da-SNPs within the 3D genome organization suggests candidate loci for further experimental investigation of the mechanisms underlying genetic susceptibility to diseases, in particular cancer.