Amphetamine and pseudoephedrine cross-tolerance measured by c-Fos protein expression in brains of chronically treated rats

Background

The use of self-generated and externally provided information in performance monitoring is reflected by the appearance of error-related and feedback-related negativities (ERN and FRN), respectively. Several authors proposed that ERN and FRN are supported by similar neural mechanisms residing in the anterior cingulate cortex (ACC) and the mesolimbic dopaminergic system. The present study is aimed to test the functional relationship between ERN and FRN. Using an Eriksen-Flanker task with a moving response deadline we tested 17 young healthy subjects. Subjects received feedback with respect to their response accuracy and response speed. To fulfill both requirements of the task, they had to press the correct button and had to respond in time to give a valid response.

Results

When performance monitoring based on self-generated information was sufficient to detect a criterion violation an ERN was released, while the subsequent feedback became redundant and therefore failed to trigger an FRN. In contrast, an FRN was released if the feedback contained information which was not available before and action monitoring processes based on self-generated information failed to detect an error.

Conclusion

The described pattern of results indicates a functional interrelationship of response and feedback related negativities in performance monitoring.     

Spatio-temporal expression of a novel neuron-derived neurotrophic factor (NDNF) in mouse brains during development

Background  

Neuron-derived neurotrophic factor (NDNF) is evolutionarily well conserved, being present in invertebrate animals such as the nematode, Caenorhabditis elegans, as well as in the fruit fly, Drosophila melanogaster. Multiple cysteines are conserved between species and secondary structure prediction shows that NDNF is mainly composed of beta-strands. In this study, we aimed to investigate the function of NDNF.     

Power-law distribution of gene expression fluctuations

Large-scale genomic technologies has opened new possibilities to infer gene regulatory networks from time series data. Here, we investigate the relationship between the dynamic information of gene expression in time series and the underlying network structure. First, our results show that the distribution of gene expression fluctuations (i.e., standard deviation) follows a power-law. This finding indicates that while most genes exhibit a relatively low variation in expression level, a few genes are revealed as highly variable genes. Second, we propose a stochastic model that explains the emergence of this power-law behavior. The model derives a relationship that connects the standard deviation (variance) of each node to its degree. In particular, it allows us to identify a global property of the underlying genetic regulatory network, such as the degree exponent, by only computing dynamic information. This result not only offers an interesting link to explore the topology of real systems without knowing the real structure but also supports earlier findings showing that gene networks may follow a scale-free distribution.     

Zipf's law in gene expression

Using data from gene expression databases on various organisms and tissues, including yeast, nematodes, human normal and cancer tissues, and embryonic stem cells, we found that the abundances of expressed genes exhibit a power-law distribution with an exponent close to -1; i.e., they obey Zipf's law. Furthermore, by simulations of a simple model with an intracellular reaction network, we found that Zipf's law of chemical abundance is a universal feature of cells where such a network optimizes the efficiency and faithfulness of self-reproduction. These findings provide novel insights into the nature of the organization of reaction dynamics in living cells.     

Classification of genes based on gene expression analysis

Systems biology and bioinformatics are now major fields for productive research. DNA microarrays and other array technologies and genome sequencing have advanced to the point that it is now possible to monitor gene expression on a genomic scale. Gene expression analysis is discussed and some important clustering techniques are considered. The patterns identified in the data suggest similarities in the gene behavior, which provides useful information for the gene functionalities. We discuss measures for investigating the homogeneity of gene expression data in order to optimize the clustering process. We contribute to the knowledge of functional roles and regulation of E. coli genes by proposing a classification of these genes based on consistently correlated genes in expression data and similarities of gene expression patterns. A new visualization tool for targeted projection pursuit and dimensionality reduction of gene expression data is demonstrated. The text was submitted by the authors in English.     

A constructive approach to gene expression dynamics

Recently, experiments on mRNA abundance (gene expression) have revealed that gene expression shows a stationary organization described by a scale-free distribution. Here we propose a constructive approach to gene expression dynamics which restores the scale-free exponent and describes the intermediate state dynamics. This approach requires only one assumption: Markov property.     

Statistical analysis of gene regulatory networks reconstructed from gene expression data of lung cancer

Recently, inferring gene regulatory network from large-scale gene expression data has been considered as an important effort to understand the life system in whole. In this paper, for the purpose of getting further information about lung cancer, a gene regulatory network of lung cancer is reconstructed from gene expression data. In this network, vertices represent genes and edges between any two vertices represent their co-regulatory relationships. It is found that this network has some characteristics which are shared by most cellular networks of health lives, such as power-law, small-world behaviors. On the other hand, it also presents some features which are obviously different from other networks, such as assortative mixing. In the last section of this paper, the significance of these findings in the context of biological processes of lung cancer is discussed.     

A generic 3D kinetic model of gene expression

Recent experiments show that mRNAs and proteins can be localized both in prokaryotic and eukaryotic cells. To describe such situations, I present a 3D mean-field kinetic model aimed primarily at gene expression in prokaryotic cells, including the formation of mRNA, its translation into protein, and slow diffusion of these species. Under steady-state conditions, the mRNA and protein spatial distribution is described by simple exponential functions. The protein concentration near the gene transcribed into mRNA is shown to depend on the protein and mRNA diffusion coefficients and degradation rate constants.     

Combined clustering models for the analysis of gene expression

Clustering has become one of the fundamental tools for analyzing gene expression and producing gene classifications. Clustering models enable finding patterns of similarity in order to understand gene function, gene regulation, cellular processes and sub-types of cells. The clustering results however have to be combined with sequence data or knowledge about gene functionality in order to make biologically meaningful conclusions. In this work, we explore a new model that integrates gene expression with sequence or text information.     

Qualitative assessment of gene expression in affymetrix genechip arrays

Affymetrix Genechip microarrays are used widely to determine the simultaneous expression of genes in a given biological paradigm. Probes on the Genechip array are atomic entities which by definition are randomly distributed across the array and in turn govern the gene expression. In the present study, we make several interesting observations. We show that there is considerable correlation between the probe intensities across the array which defy the independence assumption. While the mechanism behind such correlations is unclear, we show that scaling behavior and the profiles of perfect match (PM) as well as mismatch (MM) probes are similar and immune-to-background subtraction. We believe that the observed correlations are possibly an outcome of inherent non-stationarities or patchiness in the array devoid of biological significance. This is demonstrated by inspecting their scaling behavior and profiles of the PM and MM probe intensities obtained from publicly available Genechip arrays from three eukaryotic genomes, namely: Drosophila melanogaster (fruit fly), Homo sapiens (humans) and Mus musculus (house mouse) across distinct biological paradigms and across laboratories, with and without background subtraction. The fluctuation functions were estimated using detrended fluctuation analysis (DFA) with fourth-order polynomial detrending. The results presented in this study provide new insights into correlation signatures of PM and MM probe intensities and suggests the choice of DFA as a tool for qualitative assessment of Affymetrix Genechip microarrays prior to their analysis. A more detailed investigation is necessary in order to understand the source of these correlations.     

Emergence of the self-similar property in gene expression dynamics

Many theoretical models have recently been proposed to understand the structure of cellular systems composed of various types of elements (e.g., proteins, metabolites and genes) and their interactions. However, the cell is a highly dynamic system with thousands of functional elements fluctuating across temporal states. Therefore, structural analysis alone is not sufficient to reproduce the cell's observed behavior.In this article, we analyze the gene expression dynamics (i.e., how the amount of mRNA molecules in cell fluctuate in time) by using a new constructive approach, which reveals a symmetry embedded in gene expression fluctuations and characterizes the dynamical equation of gene expression (i.e., a specific stochastic differential equation). First, by using experimental data of human and yeast gene expression time series, we found a symmetry in short-time transition probability from time t to time t+1. We call it self-similarity symmetry (i.e., the gene expression short-time fluctuations contain a repeating pattern of smaller and smaller parts that are like the whole, but different in size). Secondly, we reconstruct the global behavior of the observed distribution of gene expression (i.e., scaling-law) and the local behavior of the power-law tail of this distribution. This approach may represent a step forward toward an integrated image of the basic elements of the whole cell.     

Sex-dependent gene expression in early brain development of chicken embryos

Background  

Differentiation of the brain during development leads to sexually dimorphic adult reproductive behavior and other neural sex dimorphisms. Genetic mechanisms independent of steroid hormones produced by the gonads have recently been suggested to partly explain these dimorphisms.