Proceedings of the National Academy of Sciences

Cervical cancer is known as the possible outcome of genital infection, while the molecular mechanisms of initiation, development, and metastasis of cervical cancer have not yet been fully elucidated. Our study aims to investigate the effects of microRNA‐92a (miR‐92a) on tumor growth and immune function by targeting PTEN via the MAPK/ERK signaling pathway in tumor‐bearing mice. C57BL/6 female mice were used for tumor‐bearing mouse models and their tumor and adjacent normal tissues were collected, and normal cervical tissues were obtained from normal mice. Serum levels of tumor necrosis factor‐α (TNF‐α) and soluble interleukin‐2 receptor (sIL‐2R) were detected by ELISA. The cells were divided into the normal, blank, negative control (NC), miR‐92a mimic, miR‐92a inhibitor, siRNA‐PTEN, and miR‐92a inhibitor + siRNA‐PTEN groups. Dual‐luciferase reporter assay was adopted to determine the relationship between PTEN and miR‐92a. Expressions of miR‐92a, PTEN, TNF‐α, sIL‐2R, ERK1, and ERK2 were tested by RT‐qPCR and Western blotting. Cell proliferation was detected by cell count kit‐8 ( CCK‐8); cell cycle and apoptosis were detected by flow cytometry. Compared with the normal cervical tissues and adjacent normal tissues, the cervical cancer tissues exhibited increased expressions of miR‐92a, p‐ERK1/2, and serum levels of TNF‐α and sIL‐2R while decreased PTEN expression. PTEN was confirmed to be the target gene of miR‐92a. As compared with the blank and NC groups, expressions of miR‐92a, ERK1 and ERK2 increased, and expressions of PTEN decreased in the miR‐92a mimic group. The miR‐92a mimic group exhibited increased expression levels of TNF‐α and sIL‐2R, cell proliferation, and cell number in S phase but decreased cell apoptosis, and cell number in G0/G1 phase, while the miR‐92a inhibitor group followed opposite trends. miR‐92a promotes tumor growth and suppresses immune function by inhibiting PTEN via activation of the MAPK/ERK signaling pathway in mice bearing U14 cervical cancer.

/pdf/microrna-92a-promotes-tumor-growth-and-suppresses-immune-5gizmvd2be.pdf

MicroRNA-92a promotes tumor growth and suppresses immune function through activation of MAPK/ERK signaling pathway by inhibiting PTEN in mice bearing U14 cervical cancer.

Virtually all network analyses involve structural measures between pairs of vertices, or of the vertices themselves, and the large amount of symmetry present in real-world complex networks is inherited by such measures. This has practical consequences which have not yet been explored in full generality, nor systematically exploited by network practitioners. Here we study the effect of network symmetry on arbitrary network measures, and show how this can be exploited in practice in a number of ways, from redundancy compression, to computational reduction. We also uncover the spectral signatures of symmetry for an arbitrary network measure such as the graph Laplacian. Computing network symmetries is very efficient in practice, and we test real-world examples up to several million nodes. Since network models are ubiquitous in the Applied Sciences, and typically contain a large degree of structural redundancy, our results are not only significant, but widely applicable.

/pdf/exploiting-symmetry-in-network-analysis-2hu4l9vn3j.pdf

Exploiting symmetry in network analysis

Coarse-grained molecular dynamics simulations are performed to investigate the interaction and complex formation of a spherical polyelectrolyte brush (SPB) consisting of uniformly distributed grafted polyelectrolytes (GPE) and an oppositely charged linear polyelectrolyte (LPE). This system is considered as a model for complexation of a polyelectrolyte and a soft nanoparticle. The effects of the LPE rigidity, length of the GPE chains, grafting density and core radius on complexation behavior are addressed. Depending on these parameters the GPE layer exhibits different structures, and acts on the LPE like a bare charged sphere, brush or star-like polyelectrolyte. Unlike the charged-sphere case, where the LPE binds to the solid body of the nanoparticle, the GPE arms can bind partly or completely to the LPE which in these cases is less affected by the core surface. The rigidity of the LPE also influences the conformational behavior of the brush layer. If the grafting density is small, the semiflexible LPE adopts a highly ordering solenoid conformation on the surface of the highly charged sphere. The ordered arrangement is disrupted as the GPE length increases. A higher ordering degree is also found for the semiflexible LPE at an intermediate core radius and relatively low grafting density. If the amount of surface charges is fixed, short GPEs suppress the complexation of the LPE with the SPB, compared to the cases of the long GPEs and bare charged sphere. However, the complexation propensity increases for lower grafting densities. Its dependence on the core radius is significantly different for flexible and semiflexible LPEs.

Polyelectrolyte adsorption on an oppositely charged spherical polyelectrolyte brush

Understanding the dynamics of biomolecules is the key to understanding their biological activities. Computational methods ranging from all-atom molecular dynamics simulations to coarse-grained normal-mode analyses based on simplified elastic networks provide a general framework to studying these dynamics. Despite recent successes in studying very large systems with up to a 100,000,000 atoms, those methods are currently limited to studying small- to medium-sized molecular systems due to computational limitations. One solution to circumvent these limitations is to reduce the size of the system under study. In this paper, we argue that coarse-graining, the standard approach to such size reduction, must define a hierarchy of models of decreasing sizes that are consistent with each other, i.e., that each model contains the information of the dynamics of its predecessor. We propose a new method, Decimate, for generating such a hierarchy within the context of elastic networks for normal-mode analysis. This metho...

The Renormalization Group and Its Applications to Generating Coarse-Grained Models of Large Biological Molecular Systems.

We analyze the structural behavior of a single polymer chain grafted to an attractive, flexible surface. Our model is composed of a coarse-grained bead-and-spring polymer and a tethered membrane. By means of extensive parallel tempering Monte Carlo simulations it is shown that the system exhibits a rich phase behavior ranging from highly ordered, compact to extended random coil structures, and from desorbed to completely adsorbed or even partially embedded conformations. These findings are summarized in a pseudophase diagram indicating the predominant class of conformations as a function of the external parameters temperature and polymer-membrane interaction strength. By comparison with adsorption to a stiff membrane surface it is shown that the flexibility of the membrane gives rise to qualitatively new behavior such as stretching of adsorbed conformations.

/pdf/thermodynamics-of-polymer-adsorption-to-a-flexible-membrane-55up51xnuk.pdf

Thermodynamics of polymer adsorption to a flexible membrane.

Understanding the mutual interdependence between the behavior of dynamical processes on networks and the underlying topologies promises new insight for a large class of empirical networks. We present a generic approach to investigate this relationship which is applicable to a wide class of dynamics, namely to evolve networks using a performance measure based on the whole spectrum of the dynamics' time evolution operator. As an example, we consider the graph Laplacian describing diffusion processes, and we evolve the network structure such that a given sub-diffusive behavior emerges.

Network evolution towards optimal dynamical performance

Networks with a prescribed power-law scaling in the spectrum of the graph Laplacian can be generated by evolutionary optimization. The Laplacian spectrum encodes the dynamical behavior of many important processes. Here, the networks are evolved to exhibit subdiffusive dynamics. Under the additional constraint of degree-regularity, the evolved networks display an abundance of symmetric motifs arranged into loops and long linear segments. Exploiting results from algebraic graph theory on symmetric networks, we find the underlying backbone structures and how they contribute to the spectrum. The resulting coarse-grained networks provide an intuitive view of how the anomalous diffusive properties can be realized in the evolved structures.

Symmetry-based coarse-graining of evolved dynamical networks

We study the importance of local structural properties in networks which have been evolved for a power-law scaling in their Laplacian spectrum. To this end, the degree distribution, two-point degree correlations, and degree-dependent clustering are extracted from the evolved networks and used to construct random networks with the prescribed distributions. In the analysis of these reconstructed networks it turns out that the degree distribution alone is not sufficient to generate the spectral scaling and the degree-dependent clustering has only an indirect influence. The two-point correlations are found to be the dominant characteristic for the power-law scaling over a broader eigenvalue range.

Reconstruction of evolved dynamic networks from degree correlations.

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Papers

Thermodynamics of polymer adsorption to a flexible membrane.

Network evolution towards optimal dynamical performance

Symmetry-based coarse-graining of evolved dynamical networks

Symmetry-based coarse-graining of evolved dynamical networks

Reconstruction of evolved dynamic networks from degree correlations.