A Hybrid Model of Mammalian Cell Cycle Regulation

The timing of DNA synthesis, mitosis and cell division is regulated by a complex network of biochemical reactions that control the activities of a family of cyclin-dependent kinases. The temporal dynamics of this reaction network is typically modeled by nonlinear differential equations describing the rates of the component reactions. This approach provides exquisite details about molecular regulatory processes but is hampered by the need to estimate realistic values for the many kinetic constants that determine the reaction rates. It is difficult to estimate these kinetic constants from available experimental data. To avoid this problem, modelers often resort to ‘qualitative’ modeling strategies, such as Boolean switching networks, but these models describe only the coarsest features of cell cycle regulation. In this paper we describe a hybrid approach that combines the best features of continuous differential equations and discrete Boolean networks. Cyclin abundances are tracked by piecewise linear differential equations for cyclin synthesis and degradation. Cyclin synthesis is regulated by transcription factors whose activities are represented by discrete variables (0 or 1) and likewise for the activities of the ubiquitin-ligating enzyme complexes that govern cyclin degradation. The discrete variables change according to a predetermined sequence, with the times between transitions determined in part by cyclin accumulation and degradation and as well by exponentially distributed random variables. The model is evaluated in terms of flow cytometry measurements of cyclin proteins in asynchronous populations of human cell lines. The few kinetic constants in the model are easily estimated from the experimental data. Using this hybrid approach, modelers can quickly create quantitatively accurate, computational models of protein regulatory networks in cells

Mammary molecular portraits reveal lineage-specific features and progenitor cell vulnerabilities.

The mammary epithelium depends on specific lineages and their stem and progenitor function to accommodate hormone-triggered physiological demands in the adult female. Perturbations of these lineages underpin breast cancer risk, yet our understanding of normal mammary cell composition is incomplete. Here, we build a multimodal resource for the adult gland through comprehensive profiling of primary cell epigenomes, transcriptomes, and proteomes. We define systems-level relationships between chromatin-DNA-RNA-protein states, identify lineage-specific DNA methylation of transcription factor binding sites, and pinpoint proteins underlying progesterone responsiveness. Comparative proteomics of estrogen and progesterone receptor-positive and -negative cell populations, extensive target validation, and drug testing lead to discovery of stem and progenitor cell vulnerabilities. Top epigenetic drugs exert cytostatic effects; prevent adult mammary cell expansion, clonogenicity, and mammopoiesis; and deplete stem cell frequency. Select drugs also abrogate human breast progenitor cell activity in normal and high-risk patient samples. This integrative computational and functional study provides fundamental insight into mammary lineage and stem cell biology

Evolutionary Stability of Small Molecular Regulatory Networks That Exhibit Near-Perfect Adaptation

Large-scale protein regulatory networks, such as signal transduction systems, contain small-scale modules (‘motifs’) that carry out specific dynamical functions. Systematic characterization of the properties of small network motifs is therefore of great interest to molecular systems biologists. We simulate a generic model of three-node motifs in search of near-perfect adaptation, the property that a system responds transiently to a change in an environmental signal and then returns near-perfectly to its pre-signal state (even in the continued presence of the signal). Using an evolutionary algorithm, we search the parameter space of these generic motifs for network topologies that score well on a pre-defined measure of near-perfect adaptation. We find many high-scoring parameter sets across a variety of three-node topologies. Of all possibilities, the highest scoring topologies contain incoherent feed-forward loops (IFFLs), and these topologies are evolutionarily stable in the sense that, under ‘macro-mutations’ that alter the topology of a network, the IFFL motif is consistently maintained. Topologies that rely on negative feedback loops with buffering (NFLBs) are also high-scoring; however, they are not evolutionarily stable in the sense that, under macro-mutations, they tend to evolve an IFFL motif and may—or may not—lose the NFLB motif

Aberrant DNA methylation reprogramming during induced pluripotent stem cell generation is dependent on the choice of reprogramming factors

Abstract The conversion of somatic cells into pluripotent stem cells via overexpression of reprogramming factors involves epigenetic remodeling. DNA methylation at a significant proportion of CpG sites in induced pluripotent stem cells (iPSCs) differs from that of embryonic stem cells (ESCs). Whether different sets of reprogramming factors influence the type and extent of aberrant DNA methylation in iPSCs differently remains unknown. In order to help resolve this critical question, we generated human iPSCs from a common fibroblast cell source using either the Yamanaka factors (OCT4, SOX2, KLF4 and cMYC) or the Thomson factors (OCT4, SOX2, NANOG and LIN28), and determined their genome-wide DNA methylation profiles. In addition to shared DNA methylation aberrations present in all our iPSCs, we identified Yamanaka-iPSC (Y-iPSC)-specific and Thomson-iPSC (T-iPSC)-specific recurrent aberrations. Strikingly, not only were the genomic locations of the aberrations different but also their types: reprogramming with Yamanaka factors mainly resulted in failure to demethylate CpGs, whereas reprogramming with Thomson factors mainly resulted in failure to methylate CpGs. Differences in the level of transcripts encoding DNMT3b and TET3 between Y-iPSCs and T-iPSCs may contribute partially to the distinct types of aberrations. Finally, de novo aberrantly methylated genes in Y-iPSCs were enriched for NANOG targets that are also aberrantly methylated in some cancers. Our study thus reveals that the choice of reprogramming factors influences the amount, location, and class of DNA methylation aberrations in iPSCs. These findings may provide clues into how to produce human iPSCs with fewer DNA methylation abnormalities

DNA hypomethylating agents increase activation and cytolytic activity of CD8(+) T cells

International audienceWe demonstrate that DNA hypomethylating agent (HMA) treatment can directly modulate the anti-tumor response and effector function of CD8(+) T cells. In vivo HMA treatment promotes CD8(+) T cell tumor infiltration and suppresses tumor growth via CD8(+) T cell-dependent activity. Ex vivo, HMAs enhance primary human CD8(+) T cell activation markers, effector cytokine production, and anti-tumor cytolytic activity. Epigenomic and transcriptomic profiling shows that HMAs vastly regulate T cell activation-related transcriptional networks, culminating with over-activation of NFATc1 short isoforms. Mechanistically, demethylation of an intragenic CpG island immediately downstream to the 3’ UTR of the short isoform was associated with antisense transcription and alternative polyadenylation of NFATc1 short isoforms. High-dimensional single-cell mass cytometry analyses reveal a selective effect of HMAs on a subset of human CD8(+) T cell subpopulations, increasing both the number and abundance of a granzyme B(high), perforin(high) effector subpopulation. Overall, our findings support the use of HMAs as a therapeutic strategy to boost anti-tumor immune response

Nucleolar RNA polymerase II drives ribosome biogenesis

Proteins are manufactured by ribosomes-macromolecular complexes of protein and RNA molecules that are assembled within major nuclear compartments called nucleoli . Existing models suggest that RNA polymerases I and III (Pol I and Pol III) are the only enzymes that directly mediate the expression of the ribosomal RNA (rRNA) components of ribosomes. Here we show, however, that RNA polymerase II (Pol II) inside human nucleoli operates near genes encoding rRNAs to drive their expression. Pol II, assisted by the neurodegeneration-associated enzyme senataxin, generates a shield comprising triplex nucleic acid structures known as R-loops at intergenic spacers flanking nucleolar rRNA genes. The shield prevents Pol I from producing sense intergenic noncoding RNAs (sincRNAs) that can disrupt nucleolar organization and rRNA expression. These disruptive sincRNAs can be unleashed by Pol II inhibition, senataxin loss, Ewing sarcoma or locus-associated R-loop repression through an experimental system involving the proteins RNaseH1, eGFP and dCas9 (which we refer to as 'red laser'). We reveal a nucleolar Pol-II-dependent mechanism that drives ribosome biogenesis, identify disease-associated disruption of nucleoli by noncoding RNAs, and establish locus-targeted R-loop modulation. Our findings revise theories of labour division between the major RNA polymerases, and identify nucleolar Pol II as a major factor in protein synthesis and nuclear organization, with potential implications for health and disease

Set-Based Analysis for Biological Modeling

The understanding of biological systems and processes requires the development of dynamical models characterized by nonlinear laws and often intricate regulation architectures. Differential and difference equations are common formalisms to characterize such systems. Hybrid dynamical systems come in handy when the modeled system combines continuous and discrete evolutions or different evolution modes such as where slow evolution phases are interrupted by fast ones. Biological data with kinetic content are often scarce, thus it can be appropriate to reason in terms of sets of (parametrized) models and sets of trajectories. In doing so, uncertainties and lack of knowledge are explicitly taken into account and more reliable predictions can be made. A crucial problem in Systems Biology is thus to identify regions of parameter space for which model behavior is consistent with experimental observations. In this chapter, we investigate the use of set-based analysis techniques, designed to compute on sets of behaviors, for the validation of biological models under uncertainties and perturbations. In addition, these techniques can be used for the synthesis of model parameter sets, so that the execution of the considered biological model under the influence of the synthesized parameters is guaranteed to satisfy a given constraint or property. The proposed approach is illustrated by several case studies, namely a model of iron homeostasis in mammalian cells and some epidemic models