Recent advances in understanding CD4+ T-cell differentiation suggest that previous models of a few distinct, stable effector phenotypes were too simplistic. Although several well-characterized phenotypes are still recognized, some states display plasticity, and intermediate phenotypes exist. As a framework for re-examining these concepts, we use Waddington’s landscape paradigm, augmented with explicit consideration of stochastic variations. Our animation program “LAVA” visualizes T-cell differentiation as cells moving across a landscape of hills and valleys, leading to attractor basins representing stable or semi-stable differentiation states. The model illustrates several principles, including: i) cell populations may behave more predictably than individual cells; ii) analogous to reticulate evolution, differentiation may proceed through a network of interconnected states, rather than a single well-defined pathway; iii) relatively minor changes in the barriers between attractor basins can change the stability or plasticity of a population; iv) intra-population variability of gene expression may be an important regulator of differentiation, rather than inconsequential noise; v) the behavior of some populations may be defined mainly by the behavior of outlier cells. While not a quantitative representation of actual differentiation, our model is intended to provoke discussion of T-cell differentiation pathways, particularly highlighting a probabilistic view of transitions between states.
Breast cancer stem cells (CSCs) are thought to drive recurrence and metastasis. Their identity has been linked to the epithelial to mesenchymal transition (EMT) but remains highly controversial since—depending on the cell-line studied—either epithelial (E) or mesenchymal (M) markers, alone or together have been associated with stemness. Using distinct transcript expression signatures characterizing the three different E, M and hybrid E/M cell-types, our data support a novel model that links a mixed EM signature with stemness in 1) individual cells, 2) luminal and basal cell lines, 3) in vivo xenograft mouse models, and 4) in all breast cancer subtypes. In particular, we found that co-expression of E and M signatures was associated with poorest outcome in luminal and basal breast cancer patients as well as with enrichment for stem-like cells in both E and M breast cell-lines. This link between a mixed EM expression signature and stemness was explained by two findings: first, mixed cultures of E and M cells showed increased cooperation in mammosphere formation (indicative of stemness) compared to the more differentiated E and M cell-types. Second, single-cell qPCR analysis revealed that E and M genes could be co-expressed in the same cell. These hybrid E/M cells were generated by both E or M cells and had a combination of several stem-like traits since they displayed increased plasticity, self-renewal, mammosphere formation, and produced ALDH1+ progenies, while more differentiated M cells showed less plasticity and E cells showed less self-renewal. Thus, the hybrid E/M state reflecting stemness and its promotion by E-M cooperation offers a dual biological rationale for the robust association of the mixed EM signature with poor prognosis, independent of cellular origin. Together, our model explains previous paradoxical findings that breast CSCs appear to be M in luminal cell-lines but E in basal breast cancer cell-lines. Our results suggest that targeting E/M heterogeneity by eliminating hybrid E/M cells and cooperation between E and M cell-types could improve breast cancer patient survival independent of breast cancer-subtype.
The perinucleolar compartment (PNC) is a nuclear substructure associated with, but structurally distinct from, the nucleolus. The PNC contains several RNA processing proteins and several RNA pol III transcripts, which form novel complexes. As determined by cell culture experiments and human tumor samples, the PNC forms exclusively in cancer cells and the percentage of cancer cells in a population that have one or more PNCs directly correlates with the malignancy of that population of cells. Therefore, the PNC is being developed as a prognostic marker for several malignancies. PNC elimination in cancer cells has proven to be a useful as screening method to discover probe compounds used to elucidate PNC biology and to discover compounds with the potential to be developed as minimally toxic anti-cancer drugs.
Nuclear architecture; PNC; RNPs; Chromatin; Cancer
Recent advances in understanding CD4+ T-cell differentiation suggest that previous models of a few distinct, stable effector phenotypes were too simplistic. Although several well-characterized phenotypes are still recognized, some states display plasticity, and intermediate phenotypes exist. As a framework for reexamining these concepts, we use Waddington's landscape paradigm, augmented with explicit consideration of stochastic variations. Our animation program “LAVA” visualizes T-cell differentiation as cells moving across a landscape of hills and valleys, leading to attractor basins representing stable or semistable differentiation states. The model illustrates several principles, including: (i) cell populations may behave more predictably than individual cells; (ii) analogous to reticulate evolution, differentiation may proceed through a network of interconnected states, rather than a single well-defined pathway; (iii) relatively minor changes in the barriers between attractor basins can change the stability or plasticity of a population; (iv) intrapopulation variability of gene expression may be an important regulator of differentiation, rather than inconsequential noise; (v) the behavior of some populations may be defined mainly by the behavior of outlier cells. While not a quantitative representation of actual differentiation, our model is intended to provoke discussion of T-cell differentiation pathways, particularly highlighting a probabilistic view of transitions between states.
CD4+ T cells; Cell differentiation; Cytokines; Modeling
Alus are transposable elements belonging to the short interspersed element family. They occupy over 10% of human genome and have been spreading through genomes over the past 65 million years. In the past, they were considered junk DNA with little function that took up genome volumes. Today, Alus and other transposable elements emerge to be key players in cellular function, including genomic activities, gene expression regulations, and evolution. Here we summarize the current understanding of Alu function in genome and gene expression regulation in human cell nuclei.
alus; transposable elements; gene expression regulation; genome function; evolution
Tumorigenesis is a dynamic biological process that involves distinct cancer cell subpopulations proliferating at different rates and interconverting between them. In this paper we proposed a mathematical framework of population dynamics that considers both distinctive growth rates and intercellular transitions between cancer cell populations. Our mathematical framework showed that both growth and transition influence the ratio of cancer cell subpopulations but the latter is more significant. We derived the condition that different cancer cell types can maintain distinctive subpopulations and we also explain why there always exists a stable fixed ratio after cell sorting based on putative surface markers. The cell fraction ratio can be shifted by changing either the growth rates of the subpopulations (Darwinism selection) or by environment-instructed transitions (Lamarckism induction). This insight can help us to understand the dynamics of the heterogeneity of cancer cells and lead us to new strategies to overcome cancer drug resistance.
The distinct cell types of multicellular organisms arise due to constraints imposed by gene regulatory networks on the collective change of gene expression across the genome, creating self-stabilizing expression states, or attractors. We compiled a resource of curated human expression data comprising 166 cell types and 2,602 transcription regulating genes and developed a data driven method built around the concept of expression reversal defined at the level of gene pairs, such as those participating in toggle switch circuits. This approach allows us to organize the cell types into their ontogenetic lineage-relationships and to reflect regulatory relationships among genes that explain their ability to function as determinants of cell fate. We show that this method identifies genes belonging to regulatory circuits that control neuronal fate, pluripotency and blood cell differentiation, thus offering a novel large-scale perspective on lineage specification.
Digital PCR (dPCR) exploits limiting dilution of a template into an array of PCR reactions. From this array the number of reactions that contain at least one (as opposed to zero) initial template is determined, allowing inferring the original template concentration. Here we present a novel protocol to efficiently infer the concentration of a sample and its optimal dilution for dPCR from few targeted qPCR assays. By taking advantage of the real-time amplification feature of qPCR as opposed to relying on endpoint PCR assessment as in standard dPCR prior knowledge of template concentration is not necessary. This eliminates the need for serial dilutions in a separate titration and reduces the number of necessary reactions. We describe the theory underlying our approach and discuss experimental moments that contribute to uncertainty. We present data from a controlled experiment where the initial template concentration is known as proof of principle and apply our method on directly monitoring transcript level change during cell differentiation as well as gauging amplicon numbers in cDNA samples after pre-amplification.
Inflammation in the tumour microenvironment is now recognized as one of the hallmarks of cancer. Endogenously produced lipid autacoids, locally acting small molecule lipid mediators, play a central role in inflammation and tissue homeostasis, and have recently been implicated in cancer. A well-studied group of autacoid mediators that are the products of arachidonic acid metabolism include: the prostaglandins, leukotrienes, lipoxins and cytochrome P450 (CYP) derived bioactive products. These lipid mediators are collectively referred to as eicosanoids and are generated by distinct enzymatic systems initiated by cyclooxygenase (COX 1 and 2), lipoxygenases (5-LOX, 12-LOX, 15-LOXa, 15-LOXb), and cytochrome P450s, respectively. These pathways are the target of approved drugs for the treatment of inflammation, pain, asthma, allergies, and cardiovascular disorders. Beyond their potent anti-inflammatory and anti-cancer effects, non-steroidal anti-inflammatory drugs (NSAIDs) and COX-2 specific inhibitors have been evaluated in both preclinical tumor models and clinical trials. Eicosanoid biosynthesis and actions can also be directly influenced by nutrients in the diet, as evidenced by the emerging role of omega-3 fatty acids in cancer prevention and treatment. Most research dedicated to using eicosanoids to inhibit tumor-associated inflammation has focused on the COX and LOX pathways. Novel experimental approaches that demonstrate the anti-tumor effects of inhibiting cancer-associated inflammation currently include: eicosanoid receptor antagonism, overexpression of eicosanoid metabolizing enzymes, and the use of endogenous anti-inflammatory lipid mediators. Here we review the actions of eicosanoids on inflammation in the context of tumorigenesis. Eicosanoids may represent a missing link between inflammation and cancer and thus could serve as therapeutic target(s) for inhibiting tumor growth.
Eicosanoids; Inflammation; Cancer; Metastasis; Tumor Microenvironment
war on cancer; stress-response; therapy failure; target-selective drugs; recurrence; somatic evolution
Expression of ABC family transporter proteins that promote drug efflux from cancer cells is a widely observed mechanism of multi-drug resistance of cancer cells. Cell adaptation in long-term culture of HL60 leukemic cells in the presence of chemotherapy leads to induction and maintenance of the ABC transporters expression, preventing further accumulation of drugs. However, we found that decreased accumulation of drugs and fluorescent dyes also contributed by a reduced uptake by the resistant cells. Confocal time-lapse microscopy and flow cytometry revealed that fluid-phase endocytosis was diminished in drug-resistant cells compared to drug-sensitive cells. Drug uptake was increased by insulin co-treatment when cells were grown in methylcellulose and monitored under the microscope, but not when cultured in suspension. We propose that multi-drug resistance is not only solely achieved by enhanced efflux capacity but also by supressed intake of the drug, offering an alternative target to overcome drug resistance or potentiate chemotherapy.
endocytosis-related multi-drug resistance; ABC transporters dependent drug resistance; cellular responses to anticancer drugs; novel mechanism of multi-drug resistance; acute myeloid leukemia model
The perinucleolar compartment (PNC) is a unique nuclear substructure, forming predominantly in cancer cells both in vitro and in vivo. PNC prevalence (percentage of cells containing at least one PNC) has been found to positively correlate with disease progression in several cancers (breast, ovarian, and colon). While there is a clear association between PNCs and cancer, the molecular function of the PNC remains unclear. Here we summarize the current understanding of the association of PNCs with cancer and its possible functions in cancer cells.
PNC; cancer; nuclear substructure; gene expression regulation; structure and function
The developmental dynamics of multicellular organisms is a process that takes place in a multi-stable system in which each attractor state represents a cell type, and attractor transitions correspond to cell differentiation paths. This new understanding has revived the idea of a quasi-potential landscape, first proposed by Waddington as a metaphor. To describe development, one is interested in the ‘relative stabilities’ of N attractors (N > 2). Existing theories of state transition between local minima on some potential landscape deal with the exit part in the transition between two attractors in pair-attractor systems but do not offer the notion of a global potential function that relates more than two attractors to each other. Several ad hoc methods have been used in systems biology to compute a landscape in non-gradient systems, such as gene regulatory networks. Here we present an overview of currently available methods, discuss their limitations and propose a new decomposition of vector fields that permits the computation of a quasi-potential function that is equivalent to the Freidlin–Wentzell potential but is not limited to two attractors. Several examples of decomposition are given, and the significance of such a quasi-potential function is discussed.
multi-stable dynamical system; non-equilibrium dynamics; quasi-potential; state transition; epigenetic landscape; Freidlin–Wentzell theory
Polarization of cell phenotypes, a common strategy to achieve cell type diversity in metazoa, results from binary cell-fate decisions in the branching pedigree of development. Such “either-or” fate decisions are controlled by two opposing cell fate-determining transcription factors. Each of the two distinct “master regulators” promotes differentiation of its respective sister lineage. But they also suppress one other, leading to their mutually exclusive expression in the two ensuing lineages. Thus, promiscuous coexistence of the antagonist regulators in the same cell, the hallmark of the common “undecided” progenitor of two sister lineages, is considered unstable. This antagonism ensures robust polarization into two discretely distinct cell types. But now the immune system's T-helper (Th) cells and their two canonical subtypes, Th1 and Th2 cells, tell a different story, as revealed in three papers recently published in PLOS Biology. The intermediate state that co-expresses the two opposing master regulators of the Th1 and Th2 subtypes, T-bet and Gata3, is highly stable and is not necessarily an undecided precursor. Instead, the Th1/Th2 hybrid cell is a robust new type with properties of both Th1 and Th2 cells. These hybrid cells are functionally active and possess the benefit of moderation: self-limitation of effector T cell function to prevent excessive inflammation, a permanent risk in host defense that can cause tissue damage or autoimmunity. Gene regulatory network analysis suggests that stabilization of the intermediate center in a polarizing system can be achieved by minor tweaking of the architecture of the mutual suppression gene circuit, and thus is a design option readily available to evolution.
Alu repeats within human genes may potentially alter gene expression. Here, we show that 3′-UTR-located inverted Alu repeats significantly reduce expression of an AcGFP reporter gene. Mutational analysis demonstrates that the secondary structure, but not the primary nucleotide sequence, of the inverted Alu repeats is critical for repression. The expression levels and nucleocytoplasmic distribution of reporter mRNAs with or without 3′-UTR inverted Alu repeats are similar; suggesting that reporter gene repression is not due to changes in mRNA levels or mRNA nuclear sequestration. Instead, reporter gene mRNAs harboring 3′-UTR inverted Alu repeats accumulate in cytoplasmic stress granules. These findings may suggest a novel mechanism whereby 3′-UTR-located inverted Alu repeats regulate human gene expression through sequestration of mRNAs within stress granules.
3′ untranslated region; Alu; inverted repeats; mRNA; stress granules
Multifactorial injuries, such as ischemia, trauma, etc., have proven stubbornly elusive to clinical therapeutics, in spite of the binary outcome of recovery or death. This may be due, in part, to the lack of formal approaches to cell injury. We present a minimal system of nonlinear ordinary differential equations describing a theory of cell injury dynamics. A mutual antagonism between injury-driven total damage and total induced stress responses gives rise to attractors representing recovery or death. Solving across a range of injury magnitudes defines an ‘injury course' containing a well-defined tipping point between recovery and death. Via the model, therapeutics is the diverting of a system on a pro-death trajectory to a pro-survival trajectory on bistable phase planes. The model plausibly explains why laboratory-based therapies have tended to fail clinically. A survival outcome is easy to achieve when lethal injury is close to the tipping point, but becomes progressively difficult as injury magnitudes increase, and there is an upper limit to salvageable injuries. The model offers novel insights into cell injury that may assist in overcoming barriers that have prevented development of clinically effective therapies for multifactorial conditions, as exemplified by brain ischemia.
bistability; brain ischemia; cell injury; nonlinear dynamics; therapeutics
Retroperitoneal squamous cell carcinoma (SCC) of unknown origin is uncommon. It is extremely rare when the primary site detected in the esophagus after 18 months. A 59-year-old female patient with waist pain was initially diagnosed as retroperitoneal metastatic SCC of occult origin. Six cycles of chemotherapy with cisplatin, paclitaxel and 5-fluorouracil were administered and clinical complete response was observed. The primary site was detected in the esophagus after 18 months and the overall survival (OS) was 28 months. To the best of our knowledge, this is the first case of esophageal squamous cell carcinoma (ESCC) initially presenting as a metastatic site with long progression-free survival (PFS) and OS. In conclusion, the different biological characteristics and complete response to first-line chemotherapy likely contribute to relatively long PFS and OS.
cancer of unknown primary; chemotherapy; esophageal cancer; squamous cell carcinoma
AIM: To investigate the effect and the possible mechanism of ginsenoside Rb1 on small intestinal smooth muscle motility in mice.
METHODS: Intestinal smooth muscle strips were isolated from male ICR mice (5 wk old), and the effect of ginsenoside Rb1 on spontaneous contraction was recorded with an electrophysiolograph. The effect of ginsenoside Rb1 on ion channel currents, including the voltage-gated K+ channel current (IKV), calcium-activated potassium channel currents (IKCa), spontaneous transient outward currents and ATP-sensitive potassium channel current (IKATP), was recorded on freshly isolated single cells using the whole-cell patch clamp technique.
RESULTS: Ginsenoside Rb1 dose-dependently inhibited the spontaneous contraction of intestinal smooth muscle by 21.15% ± 3.31%, 42.03% ± 8.23% and 67.23% ± 5.63% at concentrations of 25 μmol/L, 50 μmol/L and 100 μmol/L, respectively (n = 5, P < 0.05). The inhibitory effect of ginsenoside Rb1 on spontaneous contraction was significantly but incompletely blocked by 10 mmol/L tetraethylammonium or 0.5 mmol/L 4-aminopyridine, respectively (n = 5, P < 0.05). However, the inhibitory effect of ginsenoside Rb1 on spontaneous contraction was not affected by 10 μmol/L glibenclamide or 0.4 μmol/L tetrodotoxin. At the cell level, ginsenoside Rb1 increased outward potassium currents, and IKV was enhanced from 1137.71 ± 171.62 pA to 1449.73 ± 162.39 pA by 50 μmol/L Rb1 at +60 mV (n = 6, P < 0.05). Ginsenoside Rb1 increased IKCa and enhanced the amplitudes of spontaneous transient outward currents from 582.77 ± 179.09 mV to 788.12 ± 278.34 mV (n = 5, P < 0.05). However, ginsenoside Rb1 (50 μmol/L) had no significant effect on IKATP (n = 3, P < 0.05).
CONCLUSION: These results suggest that ginsenoside Rb1 has an inhibitory effect on the spontaneous contraction of mouse intestinal smooth muscle mediated by the activation of IKV and IKCa, but the KATP channel was not involved in this effect.
Ginsenoside Rb1; Intestinal smooth muscle; Intestinal smooth muscle cell; Potassium channel; Spontaneous contraction; Whole-cell patch clamp technique
Accumulating experimental evidence suggests that the gene regulatory networks of living organisms operate in the critical phase, namely, at the transition between ordered and chaotic dynamics. Such critical dynamics of the network permits the coexistence of robustness and flexibility which are necessary to ensure homeostatic stability (of a given phenotype) while allowing for switching between multiple phenotypes (network states) as occurs in development and in response to environmental change. However, the mechanisms through which genetic networks evolve such critical behavior have remained elusive. Here we present an evolutionary model in which criticality naturally emerges from the need to balance between the two essential components of evolvability: phenotype conservation and phenotype innovation under mutations. We simulated the Darwinian evolution of random Boolean networks that mutate gene regulatory interactions and grow by gene duplication. The mutating networks were subjected to selection for networks that both (i) preserve all the already acquired phenotypes (dynamical attractor states) and (ii) generate new ones. Our results show that this interplay between extending the phenotypic landscape (innovation) while conserving the existing phenotypes (conservation) suffices to cause the evolution of all the networks in a population towards criticality. Furthermore, the networks produced by this evolutionary process exhibit structures with hubs (global regulators) similar to the observed topology of real gene regulatory networks. Thus, dynamical criticality and certain elementary topological properties of gene regulatory networks can emerge as a byproduct of the evolvability of the phenotypic landscape.
Dynamically critical systems are those which operate at the border of a phase transition between two behavioral regimes often present in complex systems: order and disorder. Critical systems exhibit remarkable properties such as fast information processing, collective response to perturbations or the ability to integrate a wide range of external stimuli without saturation. Recent evidence indicates that the genetic networks of living cells are dynamically critical. This has far reaching consequences, for it is at criticality that living organisms can tolerate a wide range of external fluctuations without changing the functionality of their phenotypes. Therefore, it is necessary to know how genetic criticality emerged through evolution. Here we show that dynamical criticality naturally emerges from the delicate balance between two fundamental forces of natural selection that make organisms evolve: (i) the existing phenotypes must be resilient to random mutations, and (ii) new phenotypes must emerge for the organisms to adapt to new environmental challenges. The joint effect of these two forces, which are essential for evolvability, is sufficient in our computational models to generate populations of genetic networks operating at criticality. Thus, natural selection acting as a tinkerer of evolvable systems naturally generates critical dynamics.
Stem cell behaviours, such as stabilization of the undecided state of pluripotency or multipotency, the priming towards a prospective fate, binary fate decisions and irreversible commitment, must all somehow emerge from a genome-wide gene-regulatory network. Its unfathomable complexity defies the standard mode of explanation that is deeply rooted in molecular biology thinking: the reduction of observables to linear deterministic molecular pathways that are tacitly taken as chains of causation. Such culture of proximate explanation that uses qualitative arguments, simple arrow–arrow schemes or metaphors persists despite the ceaseless accumulation of ‘omics’ data and the rise of systems biology that now offers precise conceptual tools to explain emergent cell behaviours from gene networks. To facilitate the embrace of the principles of physics and mathematics that underlie such systems and help to bridge the gap between the formal description of theorists and the intuition of experimental biologists, we discuss in qualitative terms three perspectives outside the realm of their familiar linear-deterministic view: (i) state space (ii), high-dimensionality and (iii) heterogeneity. These concepts jointly offer a new vista on stem cell regulation that naturally explains many novel, counterintuitive observations and their inherent inevitability, obviating the need for ad hoc explanations of their existence based on natural selection. Hopefully, this expanded view will stimulate novel experimental designs.
gene-regulatory networks; dynamics; high-dimensional state space; heterogeneity; cell fate
Gradual or sudden transitions among different states as exhibited by cell populations in a biological sample under particular conditions or stimuli can be detected and profiled by flow cytometric time course data. Often such temporal profiles contain features due to transient states that present unique modeling challenges. These could range from asymmetric non-Gaussian distributions to outliers and tail subpopulations, which need to be modeled with precision and rigor.
To ensure precision and rigor, we propose a parametric modeling framework StateProfiler based on finite mixtures of skew t-Normal distributions that are robust against non-Gaussian features caused by asymmetry and outliers in data. Further, we present in StateProfiler a new greedy EM algorithm for fast and optimal model selection. The parsimonious approach of our greedy algorithm allows us to detect the genuine dynamic variation in the key features as and when they appear in time course data. We also present a procedure to construct a well-fitted profile by merging any redundant model components in a way that minimizes change in entropy of the resulting model. This allows precise profiling of unusually shaped distributions and less well-separated features that may appear due to cellular heterogeneity even within clonal populations.
By modeling flow cytometric data measured over time course and marker space with StateProfiler, specific parametric characteristics of cellular states can be identified. The parameters are then tested statistically for learning global and local patterns of spatio-temporal change. We applied StateProfiler to identify the temporal features of yeast cell cycle progression based on knockout of S-phase triggering cyclins Clb5 and Clb6, and then compared the S-phase delay phenotypes due to differential regulation of the two cyclins. We also used StateProfiler to construct the temporal profile of clonal divergence underlying lineage selection in mammalian hematopoietic progenitor cells.
Epoxyeicosatrienoic acids (EETs) are small molecules produced by cytochrome P450 epoxygenases. They are lipid mediators that act as autocrine or paracrine factors to regulate inflammation and vascular tone. As a result, drugs that raise EET levels are in clinical trials for the treatment of hypertension and many other diseases. However, despite their pleiotropic effects on cells, little is known about the role of these epoxyeicosanoids in cancer. Here, using genetic and pharmacological manipulation of endogenous EET levels, we demonstrate that EETs are critical for primary tumor growth and metastasis in a variety of mouse models of cancer. Remarkably, we found that EETs stimulated extensive multiorgan metastasis and escape from tumor dormancy in several tumor models. This systemic metastasis was not caused by excessive primary tumor growth but depended on endothelium-derived EETs at the site of metastasis. Administration of synthetic EETs recapitulated these results, while EET antagonists suppressed tumor growth and metastasis, demonstrating in vivo that pharmacological modulation of EETs can affect cancer growth. Furthermore, inhibitors of soluble epoxide hydrolase (sEH), the enzyme that metabolizes EETs, elevated endogenous EET levels and promoted primary tumor growth and metastasis. Thus, our data indicate a central role for EETs in tumorigenesis, offering a mechanistic link between lipid signaling and cancer and emphasizing the critical importance of considering possible effects of EET-modulating drugs on cancer.
The PNC is a nuclear body that forms in malignant cells. We characterize a newly identified complex in the PNC; determine the dynamics of PNC proteins; and describe the recruitment, localization, and sedimentation properties of PNC components.
The perinucleolar compartment (PNC) forms in cancer cells and is highly enriched with a subset of polymerase III RNAs and RNA-binding proteins. Here we report that PNC components mitochondrial RNA–processing (MRP) RNA, pyrimidine tract–binding protein (PTB), and CUG-binding protein (CUGBP) interact in vivo, as demonstrated by coimmunoprecipitation and RNA pull-down experiments. Glycerol gradient analyses show that this complex is large and sediments at a different fraction from known MRP RNA–containing complexes, the MRP ribonucleoprotein ribozyme and human telomerase reverse transcriptase. Tethering PNC components to a LacO locus recruits other PNC components, further confirming the in vivo interactions. These interactions are present both in PNC-containing and -lacking cells. High-resolution localization analyses demonstrate that MRP RNA, CUGBP, and PTB colocalize at the PNC as a reticulated network, intertwining with newly synthesized RNA. Furthermore, green fluorescent protein (GFP)–PTB and GFP-CUGBP show a slower rate of fluorescence recovery after photobleaching at the PNC than in the nucleoplasm, illustrating the different molecular interaction of the complexes associated with the PNC. These findings support a working model in which the MRP RNA–protein complex becomes nucleated at the PNC in cancer cells and may play a role in gene expression regulation at the DNA locus that associates with the PNC.
The gene regulatory circuit motif in which two opposing fate-determining transcription factors inhibit each other but activate themselves has been used in mathematical models of binary cell fate decisions in multipotent stem or progenitor cells. This simple circuit can generate multistability and explains the symmetric “poised” precursor state in which both factors are present in the cell at equal amounts as well as the resolution of this indeterminate state as the cell commits to either cell fate characterized by an asymmetric expression pattern of the two factors. This establishes the two alternative stable attractors that represent the two fate options. It has been debated whether cooperativity of molecular interactions is necessary to produce such multistability.
Here we take a general modeling approach and argue that this question is not relevant. We show that non-linearity can arise in two distinct models in which no explicit interaction between the two factors is assumed and that distinct chemical reaction kinetic formalisms can lead to the same (generic) dynamical system form. Moreover, we describe a novel type of bifurcation that produces a degenerate steady state that can explain the metastable state of indeterminacy prior to cell fate decision-making and is consistent with biological observations.
The general model presented here thus offers a novel principle for linking regulatory circuits with the state of indeterminacy characteristic of multipotent (stem) cells.