Correlation of LLT-1 and NLRC4 inflammasome and its effect on glioblastoma prognosis.

Purpose: LLT-1 is a well-known ligand for the natural killer (NK) cell inhibitory receptor NKRP1A. Here, we examined NLRC4 inflammasome components and LLT-1 expression in glioblastoma (GBM) tissues to elucidate potential associations and interactions between these factors.

Methods: GBM tissues were collected for RNA sequencing (RNA-seq) and Immunofluorescent experiments.

General protocol for predicting outbreaks of infectious diseases in social networks.

Epidemic spreading on social networks with quenched connections is strongly influenced by dynamic correlations between connected nodes, posing theoretical challenges in predicting outbreaks of infectious diseases. The quenched connections introduce dynamic correlations, indicating that the infection of one node increases the likelihood of infection among its neighboring nodes. These dynamic correlations pose significant difficulties in developing comprehensive theories for threshold determination.

Dysregulation of inflammasome activation in glioma.

Gliomas are the most common brain tumors characterized by complicated heterogeneity. The genetic, molecular, and histological pathology of gliomas is characterized by high neuro-inflammation. The inflammatory microenvironment in the central nervous system (CNS) has been closely linked with inflammasomes that control the inflammatory response and coordinate innate host defenses.

Comparing the Expression of Canonical and Non-Canonical Inflammasomes Across Glioma Grades: Evaluating Their Potential as an Aggressiveness Marker.

Background: Inflammasomes are key in the initiation of inflammatory responses and serve to defend the organism. However, when the immune system is imbalanced, these complexes contribute to tumor progression. The purpose of this study was to investigate the effect of non-canonical inflammasomes on glioma malignancy.

Translocation of Hydrophobic Polyelectrolytes under Electrical Field: Molecular Dynamics Study.

We studied the translocation of polyelectrolyte (PE) chains driven by an electric field through a pore by means of molecular dynamics simulations of a coarse-grained HP model mimicking high salt conditions. Charged monomers were considered as polar (P) and neutral monomers as hydrophobic (H). We considered PE sequences that had equally spaced charges along the hydrophobic backbone.

Non-canonical NLRC4 inflammasomes in astrocytes contribute to glioma malignancy.

Background: The present study was designed to explore the pathological role of non-canonical NLRC4 inflammasome in glioma.

Methods: This retrospective study included bioinformatical analysis, including survival, gene ontology, ssGSEA, cox regression, IPA and drug repositioning with TCGA and DepMap database. Experimental validations were conducted in glioma patient's sample and evaluated with histological or cellular functional analysis.

Downregulation of Promotes Cell Growth and Motility in Human Gastric Cancer.

Gastric cancer is a common tumor, with a high mortality rate. The severity of gastric cancer is assessed by TNM staging. Long noncoding RNAs (lncRNAs) play a role in cancer treatment; investigating the clinical significance of novel biomarkers associated with TNM staging, such as lncRNAs, is important.

Association of Tim-3/Gal-9 Axis with NLRC4 Inflammasome in Glioma Malignancy: Tim-3/Gal-9 Induce the NLRC4 Inflammasome.

Tim-3/Gal-9 and the NLRC4 inflammasome contribute to glioma progression. However, the underlying mechanisms involved are unclear. Here, we observed that Tim-3/Gal-9 expression increased with glioma malignancy and found that Tim-3/Gal-9 regulate NLRC4 inflammasome formation and activation.

Translocation, Rejection and Trapping of Polyampholytes.

Polyampholytes (PA) are a special class of polymers comprising both positive and negative monomers along their sequence. Most proteins have positive and negative residues and are PAs. Proteins have a well-defined sequence while synthetic PAs have a random charge sequence.

Derivation of evolutionary entropy in the steady-state thermodynamics of evolutionary dynamics.

We investigate the parallel mutation-selection model with varying population size, which is formulated in terms of individuals undergoing the evolution processes of reproduction and mutation, to derive evolutionary entropy. Under the framework of the steady-state thermodynamics for evolutionary dynamics, the excess growth (the difference between the maximum growth rate and the total growth rate) can be interpreted as the evolutionary entropy defined in terms of the probability distributions characteristic of evolutionary dynamics. The Clausius inequality states that the excess growth is always less than or equal to the entropy difference in evolutionary dynamics.

Transcriptomic Landscape of Lower Grade Glioma Based on Age-Related Non-Silent Somatic Mutations.

Glioma accounts for 80% of all malignant brain tumours and is the most common adult primary brain tumour. Age is an important factor affecting the development of cancer, as somatic mutations accumulate with age. Here, we aimed to analyse the significance of age-dependent non-silent somatic mutations in glioma prognosis.

The Measurement of Information and Free Energy in Mechanical-Force-Driven Coil-Globule Transitions.

We study the role of information (the relative entropy) for polymers undergoing coil-globule transitions driven by a time-dependent force. Pulling experiments at various speeds are performed by Brownian dynamics simulations. We obtain the work distributions for the forward and time-reversed backward processes and information stored at the end of the nonequilibrium pulling processes.

Association of an Missense Variant with Risk of Premature Ovarian Failure in the Korean Female Population.

Premature ovarian failure (POF) is a complex disease of which the etiology is influenced by numerous genetic variations. Several POF candidate genes have been reported. However, no causal genes with high odds ratio (OR) have yet been discovered.

Free energy measurements by the generalized fluctuation theorems: Theory and numerical study of a model filament.

We measure the free energy of a model filament, which undergoes deformations and structural transitions, as a function of its extension, in silico. We perform Brownian Dynamics (BD) simulations of pulling experiments at various speeds, following a protocol close to experimental ones. The results from the fluctuation theorems are compared with the estimates from Monte Carlo (MC) simulation, where the rugged free energy landscape is produced by the density of states method.

Modularity enhances the rate of evolution in a rugged fitness landscape.

Biological systems are modular, and this modularity affects the evolution of biological systems over time and in different environments. We here develop a theory for the dynamics of evolution in a rugged, modular fitness landscape. We show analytically how horizontal gene transfer couples to the modularity in the system and leads to more rapid rates of evolution at short times.

Quasispecies theory for evolution of modularity.

Biological systems are modular, and this modularity evolves over time and in different environments. A number of observations have been made of increased modularity in biological systems under increased environmental pressure. We here develop a quasispecies theory for the dynamics of modularity in populations of these systems.

Evolution dynamics of a model for gene duplication under adaptive conflict.

We present and solve the dynamics of a model for gene duplication showing escape from adaptive conflict. We use a Crow-Kimura quasispecies model of evolution where the fitness landscape is a function of Hamming distances from two reference sequences, which are assumed to optimize two different gene functions, to describe the dynamics of a mixed population of individuals with single and double copies of a pleiotropic gene. The evolution equations are solved through a spin coherent state path integral, and we find two phases: one is an escape from an adaptive conflict phase, where each copy of a duplicated gene evolves toward subfunctionalization, and the other is a duplication loss of function phase, where one copy maintains its pleiotropic form and the other copy undergoes neutral mutation.

Evolutionary processes in finite populations.

We consider the evolution of large but finite populations on arbitrary fitness landscapes. We describe the evolutionary process by a Markov-Moran process. We show that to O(1/N), the time-averaged fitness is lower for the finite population than it is for the infinite population.

Effect of robustness on selection of a mutation-rate regulating gene.

We study the evolution of a population of sequences, where each sequence is divided into a reproduction-rate (fitness) encoding part and a mutation-rate regulating part. Evolutionary selection acts on the sequence both by a direct fitness landscape and by indirect selection on a mutation landscape through which the sequence's mutation rate is determined, thereby providing a model of a mutation-rate-regulating gene. Coupling of the fitness landscape and mutation landscape leads to adaptive evolution of the sequence.

Optimal mutation rates in dynamic environments: The Eigen model.

We consider the Eigen quasispecies model with a dynamic environment. For an environment with sharp-peak fitness in which the most-fit sequence moves by k spin-flips each period T we find an asymptotic stationary state in which the quasispecies population changes regularly according to the regular environmental change. From this stationary state we estimate the maximum and the minimum mutation rates for a quasispecies to survive under the changing environment and calculate the optimum mutation rate that maximizes the population growth.

Quasispecies theory for finite populations.

We present stochastic, finite-population formulations of the Crow-Kimura and Eigen models of quasispecies theory, for fitness functions that depend in an arbitrary way on the number of mutations from the wild type. We include back mutations in our description. We show that the fluctuation of the population numbers about the average values is exceedingly large in these physical models of evolution.

Maximum, minimum, and optimal mutation rates in dynamic environments.

We analyze the dynamics of the parallel mutation-selection quasispecies model with a changing environment. For an environment with the sharp-peak fitness function in which the most fit sequence changes by k spin flips every period T , we find analytical expressions for the minimum and maximum mutation rates for which a quasispecies can survive, valid in the limit of large sequence size. We find an asymptotic solution in which the quasispecies population changes periodically according to the periodic environmental change.

Quasispecies theory for horizontal gene transfer and recombination.

We introduce a generalization of the parallel, or Crow-Kimura, and Eigen models of molecular evolution to represent the exchange of genetic information between individuals in a population. We study the effect of different schemes of genetic recombination on the steady-state mean fitness and distribution of individuals in the population, through an analytic field theoretic mapping. We investigate both horizontal gene transfer from a population and recombination between pairs of individuals.

Generating function, path integral representation, and equivalence for stochastic exclusive particle systems.

We present the path integral representation of the generating function for classical exclusive particle systems. By introducing hard-core bosonic creation and annihilation operators and appropriate commutation relations, we construct the Fock space structure. Using the state vector, the generating function is defined and the master equation of the system is transformed into the equation for the generating function.

Universality classification of restricted solid-on-solid type surface growth models.

We consider the restricted solid-on-solid (RSOS) type surface growth models and classify them into dynamic universality classes according to their symmetry and conservation law. Four groups of RSOS-type microscopic models--asymmetric (A), asymmetric-conserved (AC), symmetric (S), and symmetric-conserved (SC) groups--are introduced and the corresponding stochastic differential equations (SDEs) are derived. Analyzing these SDEs using dynamic renormalization group theory, we confirm the previous results that A-RSOS, AC-RSOS, and S-RSOS groups belong to the Kardar-Parisi-Zhang class, the Villain-Lai-Das Sarma class, and the Edwards-Wilkinson class, respectively.