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Replication of vertebrate genomes is tightly regulated to ensure accurate duplication, but our understanding of the interplay between genetic and epigenetic factors in this regulation remains incomplete. Here, we investigated the involvement of three elements enriched at gene promoters and replication origins: guanine-rich motifs potentially forming G-quadruplexes (pG4s), nucleosome-free regions (NFRs), and the histone variant H2A.Z, in the firing of origins of replication in vertebrates. We show that two pG4s on the same DNA strand (dimeric pG4s) are sufficient to induce the assembly of an efficient minimal replication origin without inducing transcription in avian DT40 cells. Dimeric pG4s in replication origins are associated with formation of an NFR next to precisely-positioned nucleosomes enriched in H2A.Z on this minimal origin and genome-wide. Thus, our data suggest that dimeric pG4s are important for the organization and duplication of vertebrate genomes. It supports the hypothesis that a nucleosome close to an NFR is a shared signal for the formation of replication origins in eukaryotes. A program regulating replication origins ensures the exact duplication of vertebrate genomes. The authors identify a combination of guanine-rich motifs, known to form secondary DNA structures, which are sufficient to assemble efficient replication origins.

The plant hormone auxin is thought to provide positional information for patterning during development. It is still unclear, however, precisely how auxin is distributed across tissues and how the hormone is sensed in space and time. The control of gene expression in response to auxin involves a complex network of over 50 potentially interacting transcriptional activators and repressors, the auxin response factors (ARFs) and Aux/IAAs. Here, we perform a large‐scale analysis of the Aux/IAA‐ARF pathway in the shoot apex of Arabidopsis , where dynamic auxin‐based patterning controls organogenesis. A comprehensive expression map and full interactome uncovered an unexpectedly simple distribution and structure of this pathway in the shoot apex. A mathematical model of the Aux/IAA‐ARF network predicted a strong buffering capacity along with spatial differences in auxin sensitivity. We then tested and confirmed these predictions using a novel auxin signalling sensor that reports input into the signalling pathway, in conjunction with the published DR5 transcriptional output reporter. Our results provide evidence that the auxin signalling network is essential to create robust patterns at the shoot apex. Synopsis The plant hormone auxin is a key morphogenetic signal involved in the control of cell identity throughout development. A striking example of auxin action is at the shoot apical meristem (SAM), a population of stem cells generating the aerial parts of the plant. Organ positioning and patterning depends on local accumulations of auxin in the SAM, generated by polar transport of auxin (Vernoux et al , 2010 ). However, it is still unclear how auxin is distributed at cell resolution in tissues and how the hormone is sensed in space and time during development. A complex ensemble of 29 Aux/IAAs and 23 ARFs is central to the regulation of gene transcription in response to auxin (for review, see Leyser, 2006 ; Guilfoyle and Hagen, 2007 ; Chapman and Estelle, 2009 ). Protein–protein interactions govern the properties of this transduction pathway (Del Bianco and Kepinski, 2011 ). Limited interaction studies suggest that, in the absence of auxin, the Aux/IAA repressors form heterodimers with the ARF transcription factors, preventing them from regulating target genes. In the presence of auxin, the Aux/IAA proteins are targeted to the proteasome by an SCF E3 ubiquitin ligase complex (Chapman and Estelle, 2009 ; Leyser, 2006 ). In this process, auxin promotes the interaction between Aux/IAA proteins and the TIR1 F‐box of the SCF complex (or its AFB homologues) that acts as an auxin co‐receptor (Dharmasiri et al , 2005a , 2005b ; Kepinski and Leyser, 2005 ; Tan et al , 2007 ). The auxin‐induced degradation of Aux/IAAs would then release ARFs to regulate transcription of their target genes. This includes activation of most of the Aux/IAA genes themselves, thus establishing a negative feedback loop (Guilfoyle and Hagen, 2007 ). Although this general scenario provides a framework for understanding gene regulation by auxin, the underlying protein–protein network remains to be fully characterized. In this paper, we combined experimental and theoretical analyses to understand how this pathway contributes to sensing auxin in space and time (Figure 1 ). We first analysed the expression patterns of the ARFs , Aux/IAAs and TIR1 / AFBs genes in the SAM. Our results demonstrate a general tendency for most of the 25 ARFs and Aux/IAAs detected in the SAM: a differential expression with low levels at the centre of the meristem (where the stem cells are located) and high levels at the periphery of the meristem (where organ initiation takes place). We also observed a similar differential expression for TIR1/AFB co‐receptors. To understand the functional significance of the distribution of ARFs and Aux/IAAs in the SAM, we next investigated the global structure of the Aux/IAA‐ARF network using a high‐throughput yeast two‐hybrid approach and uncover a rather simple topology that relies on three basic generic features: (i) Aux/IAA proteins interact with themselves, (ii) Aux/IAA proteins interact with ARF activators and (iii) ARF repressors have no or very limited interactions with other proteins in the network. The results of our interaction analysis suggest a model for the Aux/IAA‐ARF signalling pathway in the SAM, where transcriptional activation by ARF activators would be negatively regulated by two independent systems, one involving the ARF repressors, the other the Aux/IAAs. The presence of auxin would remove the inhibitory action of Aux/IAAs, but leave the ARF repressors to compete with ARF activators for promoter‐binding sites. To explore the regulatory properties of this signalling network, we developed a mathematical model to describe the transcriptional output as a function of the signalling input that is the combinatorial effect of auxin concentration and of its perception. We then used the model and a simplified view of the meristem (where the same population of Aux/IAAs and ARFs exhibit a low expression at the centre and a high expression in the peripheral zone) for investigating the role of auxin signalling in SAM function. We show that in the model, for a given ARF activator‐to‐repressor ratio, the gene induction capacity increases with the absolute levels of ARF proteins. We thus predict that the differential expression of the ARF s generates differences in auxin sensitivities between the centre (low sensitivity) and the periphery (high sensitivity), and that the expression of TIR1/AFB participates to this regulation (prediction 1). We also use the model to analyse the transcriptional response to rapidly changing auxin concentrations. By simulating situations equivalent either to the centre or the periphery of our simplified representation of the SAM, we predict that the signalling pathway buffers its response to the auxin input via the balance between ARF activators and repressors, in turn generated by their differential spatial distributions (prediction 2). To test the predictions from the model experimentally, we needed to assess both the input (auxin level and/or perception) and the output (target gene induction) of the signalling cascade. For measuring the transcriptional output, the widely used DR5 reporter is perfectly adapted (Figure 5 ) (Ulmasov et al , 1997 ; Sabatini et al , 1999 ; Benkova et al , 2003 ; Heisler et al , 2005 ). For assaying pathway input, we designed DII‐VENUS, a novel auxin signalling sensor that comprises a constitutively expressed fusion of the auxin‐binding domain (termed domain II or DII) (Dreher et al , 2006 ; Tan et al , 2007 ) of an IAA to a fast‐maturating variant of YFP, VENUS (Figure 5 ). The degradation patterns from DII‐VENUS indicate a high auxin signalling input both in flower primordia and at the centre of the SAM. This is in contrast to the organ‐specific expression pattern of DR5::VENUS (Figure 5 ). These results indicate that the signalling pathway limits gene activation in response to auxin at the meristem centre and confirm the differential sensitivity to auxin between the centre and the periphery (prediction 1). We further confirmed the buffering capacities of the signalling pathway (prediction 2) by carrying out live imaging experiments to monitor DII‐VENUS and DR5::VENUS expression in real time (Figure 5 ). This analysis reveals the presence of important temporal variations of DII‐VENUS fluorescence, while DR5::VENUS does not show such global variations. Our approach thus provides evidence that the Aux/IAA‐ARF pathway has a key role in patterning in the SAM, alongside the auxin transport system. Our results illustrate how the tight spatio‐temporal regulation of both the distribution of a morphogenetic signal and the activity of the downstream signalling pathway provides robustness to a dynamic developmental process. We provide a comprehensive expression map of the different genes (TIR1/AFBs, ARFs and Aux/IAAs) involved in the signalling pathway regulating gene transcription in response to auxin in the shoot apical meristem (SAM). We demonstrate a relatively simple structure of this pathway using a high‐throughput yeast two‐hybrid approach to obtain the Aux/IAA‐ARF full interactome. The topology of the signalling network was used to construct a model for auxin signalling and to predict a role for the spatial regulation of auxin signalling in patterning of the SAM. We used a new sensor to monitor the input in the auxin signalling pathway and to confirm the model prediction, thus demonstrating that auxin signalling is essential to create robust patterns at the SAM.

The duplication of mammalian genomes is under the control of a spatiotemporal program that orchestrates the positioning and the timing of firing of replication origins. The molecular mechanisms coordinating the activation of about [Formula: see text] predicted origins remain poorly understood, partly due to the intrinsic rarity of replication bubbles, making it difficult to purify short nascent strands (SNS). The precise identification of origins based on the high-throughput sequencing of SNS constitutes a new methodological challenge. We propose a new statistical method with a controlled resolution, adapted to the detection of replication origins from SNS data. We detected an average of 80,000 replication origins in different cell lines. To evaluate the consistency between different protocols, we compared SNS detections with bubble trapping detections. This comparison demonstrated a good agreement between genome-wide methods, with 65% of SNS-detected origins validated by bubble trapping, and 44% of bubble trapping origins validated by SNS origins, when compared at the same resolution. We investigated the interplay between the spatial and the temporal programs of replication at fine scales. We show that most of the origins detected in regions replicated in early S phase are shared by all the cell lines investigated whereas cell-type-specific origins tend to be replicated in late S phase. We shed a new light on the key role of CpG islands, by showing that 80% of the origins associated with CGIs are constitutive. Our results further show that at least 76% of CGIs are origins of replication. The analysis of associations with chromatin marks at different timing of cell division revealed new potential epigenetic regulators driving the spatiotemporal activity of replication origins. We highlight the potential role of H4K20me1 and H3K27me3, the coupling of which is correlated with increased efficiency of replication origins, clearly identifying those marks as potential key regulators of replication origins.

Background Cell differentiation requires the integration of two opposite processes, a stabilizing cellular memory, especially at the transcriptional scale, and a burst of gene expression variability which follows the differentiation induction. Therefore, the actual capacity of a cell to undergo phenotypic change during a differentiation process relies upon a modification in this balance which favors change-inducing gene expression variability. However, there are no experimental data providing insight on how fast the transcriptomes of identical cells would diverge on the scale of the very first two cell divisions during the differentiation process. Results In order to quantitatively address this question, we developed different experimental methods to recover the transcriptomes of related cells, after one and two divisions, while preserving the information about their lineage at the scale of a single cell division. We analyzed the transcriptomes of related cells from two differentiation biological systems (human CD34+ cells and T2EC chicken primary erythrocytic progenitors) using two different single-cell transcriptomics technologies (scRT-qPCR and scRNA-seq). Conclusions We identified that the gene transcription profiles of differentiating sister cells are more similar to each other than to those of non-related cells of the same type, sharing the same environment and undergoing similar biological processes. More importantly, we observed greater discrepancies between differentiating sister cells than between self-renewing sister cells. Furthermore, a progressive increase in this divergence from first generation to second generation was observed when comparing differentiating cousin cells to self renewing cousin cells. Our results are in favor of a gradual erasure of transcriptional memory during the differentiation process.

Background According to Waddington’s epigenetic landscape concept, the differentiation process can be illustrated by a cell akin to a ball rolling down from the top of a hill (proliferation state) and crossing furrows before stopping in basins or “attractor states” to reach its stable differentiated state. However, it is now clear that some committed cells can retain a certain degree of plasticity and reacquire phenotypical characteristics of a more pluripotent cell state. In line with this dynamic model, we have previously shown that differentiating cells (chicken erythrocytic progenitors (T2EC)) retain for 24 h the ability to self-renew when transferred back in self-renewal conditions. Despite those intriguing and promising results, the underlying molecular state of those “reverting” cells remains unexplored. The aim of the present study was therefore to molecularly characterize the T2EC reversion process by combining advanced statistical tools to make the most of single-cell transcriptomic data. For this purpose, T2EC, initially maintained in a self-renewal medium (0H), were induced to differentiate for 24H (24H differentiating cells); then, a part of these cells was transferred back to the self-renewal medium (48H reverting cells) and the other part was maintained in the differentiation medium for another 24H (48H differentiating cells). For each time point, cell transcriptomes were generated using scRT-qPCR and scRNAseq. Results Our results showed a strong overlap between 0H and 48H reverting cells when applying dimensional reduction. Moreover, the statistical comparison of cell distributions and differential expression analysis indicated no significant differences between these two cell groups. Interestingly, gene pattern distributions highlighted that, while 48H reverting cells have gene expression pattern more similar to 0H cells, they are not completely identical, which suggest that for some genes a longer delay may be required for the cells to fully recover. Finally, sparse PLS (sparse partial least square) analysis showed that only the expression of 3 genes discriminates 48H reverting and 0H cells. Conclusions Altogether, we show that reverting cells return to an earlier molecular state almost identical to undifferentiated cells and demonstrate a previously undocumented physiological and molecular plasticity during the differentiation process, which most likely results from the dynamic behavior of the underlying molecular network.

The melanocortin-4 receptor (MC4R), a centrally expressed G protein-coupled receptor (GPCR), is essential for the maintenance of long-term energy balance in humans. Mutations in MC4R are the most common genetic cause of obesity. Since activation of this receptor leads to a decrease in food intake, MC4R is also a major therapeutic target for the treatment of obesity. Control of MC4R activity in vivo is modulated by the opposing effects of the anorexigenic agonist alpha-melanocyte-stimulating hormone (alpha-MSH) and the orexigenic antagonist agouti-related protein (AGRP). In addition, experiments in vitro have demonstrated that the human MC4R has an intrinsic constitutive activity on which AGRP also acts as an inverse agonist. The physiological role of this constitutive activity in the control of energy balance as well as the domain of the protein implicated in its maintenance are unknown. By systematically studying functional defects in naturally occurring MC4R mutations from obese patients, we defined a cluster of N-terminal mutations that selectively impair the constitutive activity of the receptor. Further characterization of this domain demonstrated that it functions as a tethered intramolecular ligand that maintains the constitutive activity of MC4R and may provide novel avenues for the design of drugs targeting this receptor. Our results also suggest that the tonic satiety signal provided by the constitutive activity of MC4R may be required for maintaining long-term energy homeostasis in humans.

Background As biological networks often show complex topological features, mathematical methods are required to extract meaningful information. Clustering methods are useful in this setting, as they allow the summary of the network's topology into a small number of relevant classes. Different strategies are possible for clustering, and in this article we focus on a model-based strategy that aims at clustering nodes based on their connectivity profiles. Results We present MixNet, the first publicly available computer software that analyzes biological networks using mixture models. We apply this method to various networks such as the E. coli transcriptional regulatory network, the macaque cortex network, a foodweb network and the Buchnera aphidicola metabolic network. This method is also compared with other approaches such as module identification or hierarchical clustering. Conclusion We show how MixNet can be used to extract meaningful biological information, and to give a summary of the networks topology that highlights important biological features. This approach is powerful as MixNet is adaptive to the network under study, and finds structural information without any a priori on the structure that is investigated. This makes MixNet a very powerful tool to summarize and decipher the connectivity structure of biological networks.

We propose a continuous testing framework to test the intensities of Poisson processes that allows a rigorous definition of the complete testing procedure, from an infinite number of hypotheses to joint error rates. Our work extends procedures based on scanning windows by controlling the familywise error rate and the false discovery rate in a non-asymptotic manner and in a continuous way. We introduce the p-value process on which the decision rule is based. Our method is applied in neuroscience via the standard homogeneity and two-sample tests.

Internal standard (IS) plays an important role in LC−MS bioanalysis by compensating for the variability of the analyte of interest in bioanalytical workflow. Due to the complexity of biological sample compositions and bioanalytical processes, a certain level of IS response variability across a run or a study is anticipated. However, an extensive variability may raise doubts to the accuracy of the measured results and also suggest nonoptimal analytical method. In this current paper, recent publications and guidelines regarding IS response in LC−MS bioanalysis were thoroughly reviewed with focus on the evaluation, identification and impact assessment of ‘abnormal’ IS response variability. A systematic decision tree was proposed to facilitate investigation into abnormal IS response variability after each run.

In reality, this approach is not in line with attributed function of the IS in the quantitative analysis by LC-MS/MS. Since the IS has the same physical properties to that of the analyte and the IS co-elute, both will experience the same sort of ionization (enhancement or suppression);hence, the analyte to IS peak area (height) ratio remains unaffected (10). [...]a unique matrix, human plasma for example, can be considered to analyze samples from other species such as monkey, rat or dog, providing that a proper IS, preferably labeled with 13 C or 15 N atoms with identical physical properties (same elution time and ionization pattern) to that of the analyte is used. [...]these assessments will not always help understanding the behavior of the analyte in incurred sample matrices. Since we are moving toward the concept of ‘personalized incurred samples’, it is important to monitor the IS signal during the sample analysis, and if abnormal signal is observed, assess each case without necessarily rejecting the sample concentration found.In fact, in the unknown samples displaying abnormal IS signal, the MS ionization will affect in a similar manner the analyte of interest and its corresponding IS.