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At least 1,495 records · Page 83Linked to original sources
PubMed2006

Comparative sequence analysis reveals an intricate network among REST, CREB and miRNA in mediating neuronal gene expression.

BACKGROUND: Two distinct classes of regulators have been implicated in regulating neuronal gene expression and mediating neuronal identity: transcription factors such as REST/NRSF (RE1 silencing transcription factor) and CREB (cAMP response element-binding protein), and microRNAs (miRNAs). How these two classes of regulators act together to mediate neuronal gene expression is unclear. RESULTS: Using comparative sequence analysis, here we report the identification of 895 sites (NRSE) as the putative targets of REST. A set of the identified NRSE sites is present in the vicinity of the miRNA genes that are specifically expressed in brain-related tissues, suggesting the transcriptional regulation of these miRNAs by REST. We have further identified target genes of these miRNAs, and discovered that REST and its cofactor complex are targets of multiple brain-related miRNAs including miR-124a, miR-9 and miR-132. Given the role of both REST and miRNA as repressors, these findings point to a double-negative feedback loop between REST and the miRNAs in stabilizing and maintaining neuronal gene expression. Additionally, we find that the brain-related miRNA genes are highly enriched with evolutionarily conserved cAMP response elements (CRE) in their regulatory regions, implicating the role of CREB in the positive regulation of these miRNAs. CONCLUSION: The expression of neuronal genes and neuronal identity are controlled by multiple factors, including transcriptional regulation through REST and post-transcriptional modification by several brain-related miRNAs. We demonstrate that these different levels of regulation are coordinated through extensive feedbacks, and propose a network among REST, CREB proteins and the brain-related miRNAs as a robust program for mediating neuronal gene expression.

Cyclic AMP Response Element-Binding Protein↗
PubMed2005

Identification and quantification of industrial grade glycerol adulteration in red wine with fourier transform infrared spectroscopy using chemometrics and artificial neural networks.

Fourier transform infrared (FT-IR) single bounce micro-attenuated total reflectance (mATR) spectroscopy, combined with multivariate and artificial neural network (ANN) data analysis, was used to determine the adulteration of industrial grade glycerol in selected red wines. Red wine samples were artificially adulterated with industrial grade glycerol over the concentration range from 0.1 to 15% and calibration models were developed and validated. Single bounce infrared spectra of glycerol adulterated wine samples were recorded in the fingerprint mid-infrared region, 900-1500 cm(-1). Partial least squares (PLS) and PLS first derivatives were used for quantitative analysis (r2 = 0.945 to 0.998), while linear discriminant analysis (LDA) and canonical variate analysis (CVA) were used for classification and discrimination. The standard error of prediction (SEP) in the validation set was between 1.44 and 2.25%. Classification of glycerol adulterants in the different brands of red wine using CVA resulted in a classification accuracy in the range between 94 and 98%. Artificial neural network analysis based on the quick back propagation network (BPN) and the radial basis function network (RBFN) algorithms had classification success rates of 93% using BPN and 100% using RBFN. The genetic algorithm network was able to predict the concentrations of glycerol in wine up to an accuracy of r2 = 0.998.

Algorithms↗
PubMed2006

Synchronization reveals topological scales in complex networks.

We study the relationship between topological scales and dynamic time scales in complex networks. The analysis is based on the full dynamics towards synchronization of a system of coupled oscillators. In the synchronization process, modular structures corresponding to well-defined communities of nodes emerge in different time scales, ordered in a hierarchical way. The analysis also provides a useful connection between synchronization dynamics, complex networks topology, and spectral graph analysis.

Journal Article↗
PubMed2004

Structural double-mutant cycle analysis of a hydrogen bond network in ketosteroid isomerase from Pseudomonas putida biotype B.

KSI (ketosteroid isomerase) catalyses an allylic isomerization reaction at a diffusion-controlled rate. A hydrogen bond network, Asp(99).Water(504).Tyr(14).Tyr(55).Tyr(30), connects two critical catalytic residues, Tyr(14) and Asp(99), with Tyr(30), Tyr(55) and a water molecule in the highly apolar active site of the Pseudomonas putida KSI. In order to characterize the interactions among these amino acids in the hydrogen bond network of KSI, double-mutant cycle analysis was performed, and the crystal structure of each mutant protein within the cycle was determined respectively to interpret the coupling energy. The DeltaDeltaG(o) values of Y14F/D99L (Tyr(14)-->Phe/Asp(99)-->Leu) KSI, 25.5 kJ/mol for catalysis and 28.9 kJ/mol for stability, were smaller than the sums (i.e. 29.7 kJ/mol for catalysis and 34.3 kJ/mol for stability) for single mutant KSIs respectively, indicating that the effect of the Y14F/D99L mutation was partially additive for both catalysis and stability. The partially additive effect of the Y14F/D99L mutation suggests that Tyr(14) and Asp(99) should interact positively for the stabilization of the transition state during the catalysis. The crystal structure of Y14F/D99L KSI indicated that the Y14F/D99L mutation increased the hydrophobic interaction while disrupting the hydrogen bond network. The DeltaDeltaG(o) values of both Y30F/D99L and Y55F/D99L KSIs for the catalysis and stability were larger than the sum of single mutants, suggesting that either Tyr(30) and Asp(99) or Tyr(55) and Asp(99) should interact negatively for the catalysis and stability. These synergistic effects of both Y30F/D99L and Y55F/D99L mutations resulted from the disruption of the hydrogen bond network. The synergistic effect of the Y55F/D99L mutation was larger than that of the Y30F/D99L mutation, since the former mutation impaired the proper positioning of a critical catalytic residue, Tyr(14), involved in the catalysis of KSI. The present study can provide insight into interpreting the coupling energy measured by double-mutant cycle analysis based on the crystal structures of the wild-type and mutant proteins.

Binding Sites↗
PubMed2002

Digital dermoscopy analysis and artificial neural network for the differentiation of clinically atypical pigmented skin lesions: a retrospective study.

Noninvasive diagnostic methods such as dermoscopy or epiluminescence light microscopy have been developed in an attempt to improve diagnostic accuracy of pigmented skin lesions. The evaluation of the many morphologic characteristics of pigmented skin lesions observable by epiluminescence light microscopy, however, is often extremely complex and subjective. With the aim of obviating these problems of qualitative interpretation, methods based on mathematical analysis of pigmented skin lesions have recently been designed. These methods are based on computerized analysis of digital images obtained by epiluminescence light microscopy. In this study we used a digital dermoscopy analyzer with 147 clinically atypical pigmented skin lesions (90 nevi and 57 melanomas) to determine its discriminating power with respect to histologic diagnosis. The system evaluated 48 objective parameters used to train an artificial neural network. Using the artificial neural network with 10 variables selected by a stepwise procedure, we obtained a maximum accuracy in distinguishing melanoma from benign lesions of about 93%. Comparing this result with those of the many studies using classical epiluminescence light microscopy, it emerges that the method proposed is equal or even superior in diagnostic accuracy and has the advantage of not depending on the expertise of the clinician who examines the lesion.

Humans↗
PubMed1999

Model-free functional MRI analysis using Kohonen clustering neural network and fuzzy C-means.

Conventional model-based or statistical analysis methods for functional MRI (fMRI) suffer from the limitation of the assumed paradigm and biased results. Temporal clustering methods, such as fuzzy clustering, can eliminate these problems but are difficult to find activation occupying a small area, sensitive to noise and initial values, and computationally demanding. To overcome these adversities, a cascade clustering method combining a Kohonen clustering network and fuzzy, means is developed. Receiver operating characteristic (ROC) analysis is used to compare this method with correlation coefficient analysis and t test on a series of testing phantoms. Results shown that this method can efficiently and stably identify the actual functional response with typical signal change to noise ratio, from a small activation area occupying only 0.2% of head size, with phase delay, and from other noise sources such as head motion. With the ability of finding activities of small sizes stably this method can not only identify the functional responses and the active regions more precisely, but also discriminate responses from different signal sources, such as large venous vessels or different types of activation patterns in human studies involving motor cortex activation. Even when the experimental paradigm is unknown in a blind test such that model-based methods are inapplicable, this method can identify the activation patterns and regions correctly.

Adult↗
PubMed1993

Self-organizing neural networks as a means of cluster analysis in clinical chemistry.

Connectionist systems (often termed "neural networks") are an alternative way to solve data processing tasks. They differ radically from conventional "von-Neumann" computing devices. Recent work on neural networks in clinical chemistry was done using supervised learning schemes, resulting in models which resemble classical discriminant analysis. The aim of the present study is to make clinical chemists familiar with basic concepts of self-organizing neural networks employing unsupervised learning schemes. Using a benchmark data set on the composition of milk from 22 different mammals, it is demonstrated that self-organizing neural networks are capable of performing tasks similar to classical cluster analysis and principal component analysis. Self-organizing neural networks could be envisaged to provide an alternative way for reducing the dimensionality of complex multivariate data sets, thus producing easily comprehensible low-dimensional "maps" of essential features.

Animals↗
PubMed2006

Bifurcation and singularity analysis of a molecular network for the induction of long-term memory.

Withdrawal reflexes of the mollusk Aplysia exhibit sensitization, a simple form of long-term memory (LTM). Sensitization is due, in part, to long-term facilitation (LTF) of sensorimotor neuron synapses. LTF is induced by the modulatory actions of serotonin (5-HT). Pettigrew et al. developed a computational model of the nonlinear intracellular signaling and gene network that underlies the induction of 5-HT-induced LTF. The model simulated empirical observations that repeated applications of 5-HT induce persistent activation of protein kinase A (PKA) and that this persistent activation requires a suprathreshold exposure of 5-HT. This study extends the analysis of the Pettigrew model by applying bifurcation analysis, singularity theory, and numerical simulation. Using singularity theory, classification diagrams of parameter space were constructed, identifying regions with qualitatively different steady-state behaviors. The graphical representation of these regions illustrates the robustness of these regions to changes in model parameters. Because persistent protein kinase A (PKA) activity correlates with Aplysia LTM, the analysis focuses on a positive feedback loop in the model that tends to maintain PKA activity. In this loop, PKA phosphorylates a transcription factor (TF-1), thereby increasing the expression of an ubiquitin hydrolase (Ap-Uch). Ap-Uch then acts to increase PKA activity, closing the loop. This positive feedback loop manifests multiple, coexisting steady states, or multiplicity, which provides a mechanism for a bistable switch in PKA activity. After the removal of 5-HT, the PKA activity either returns to its basal level (reversible switch) or remains at a high level (irreversible switch). Such an irreversible switch might be a mechanism that contributes to the persistence of LTM. The classification diagrams also identify parameters and processes that might be manipulated, perhaps pharmacologically, to enhance the induction of memory. Rational drug design, to affect complex processes such as memory formation, can benefit from this type of analysis.

Algorithms↗
PubMed1998

On the relationship of neuropeptide Y Y1 receptor-immunoreactive neuronal structures to the neuropeptide Y-immunoreactive nerve terminal networks. A double immunolabelling analysis in the rat brain.

Neuropeptide Y is the most abundant peptide in the mammalian central nervous system and exhibits a variety of potent neurobiological functions. In the present study, double immunolabelling histochemistry was performed, using previously characterized antibodies against neuropeptide Y and the neuropeptide Y Y1 receptor subtype, to clarify the cellular distribution of Y1 receptors in the rat brain in relation to the neuropeptide Y-immunoreactive systems. Based on fluorescence and confocal laser microscopy analysis, morphological evidence is presented that the perikaryal and dendritic Y1 receptor-like immunoreactivity demonstrated in discrete regions of the tel-, diencephalon and of the lower brain stem, shown to be cytoplasmic and membrane associated, in many brain regions is not co-distributed with the neuropeptide Y-immunoreactive terminal network. These findings may partly be explained by the existence of volume transmission in Y1 receptor-mediated neuropeptide Y transmission involving short to long distance diffusion and/or convection of neuropeptide Y from its site of release to the neuronal target cells, containing the high-affinity Y1 receptors. Furthermore, neuropeptide Y and Y1 receptor-like immunoreactivities were in no case co-localized in the same nerve cell, suggesting that, in the rat brain, the Y1 receptor subtype may not be a neuropeptide Y autoreceptor.

Amino Acid Sequence↗
PubMed1987

Morphometric analysis of sparse capillary networks.

Two methods were used to assess the heterogeneity of capillary supply to muscles of widely differing metabolic capacity and fibre size. Using the method of capillary domains (DOM; Hoofd et al., 1985) and the closest-individual method (CI; Kayar et al., 1981) radii of Kroghian cylinders (R) can be calculated, and the heterogeneity of their lognormal distribution represented by the logarithmic standard deviation (Log SD). Both methods yield similar values for mean R in a tissue. DOM is more direct and quicker than CI, and may be particularly useful in the analysis of capillary oxygen supply during functional hypertrophy and in muscle regeneration where a broad distribution of fibre areas may be found. Despite a 500-fold range of capillary density, to a minimum of 20 capillaries mm-2, heterogeneity of capillary supply was similar in all muscles, indicating a functionally homologous spatial distribution. The relationship between number of fibres overlapped by a capillary domain, and domain area has zero correlation in most tissues but shows a negative trend in fish fast muscle, reflecting hyperplastic and hypertrophic growth. Capillary/fibre ratio is inappropriate for sparse networks whereas the cumulative fraction of domains vs fibre area shows a strong correlation, suggesting that maximal oxygen supply to muscle fibres is not restricted to contiguous capillaries, but also involves those remote from the fibre surface.

Animals↗
PubMed2006

Metabolic networks in motion: 13C-based flux analysis.

Many properties of complex networks cannot be understood from monitoring the components--not even when comprehensively monitoring all protein or metabolite concentrations--unless such information is connected and integrated through mathematical models. The reason is that static component concentrations, albeit extremely informative, do not contain functional information per se. The functional behavior of a network emerges only through the nonlinear gene, protein, and metabolite interactions across multiple metabolic and regulatory layers. I argue here that intracellular reaction rates are the functional end points of these interactions in metabolic networks, hence are highly relevant for systems biology. Methods for experimental determination of metabolic fluxes differ fundamentally from component concentration measurements; that is, intracellular reaction rates cannot be detected directly, but must be estimated through computer model-based interpretation of stable isotope patterns in products of metabolism.

Animals↗
PubMed2006

Robustness and modular design of the Drosophila segment polarity network.

Biomolecular networks have to perform their functions robustly. A robust function may have preferences in the topological structures of the underlying network. We carried out an exhaustive computational analysis on network topologies in relation to a patterning function in Drosophila embryogenesis. We found that whereas the vast majority of topologies can either not perform the required function or only do so very fragilely, a small fraction of topologies emerges as particularly robust for the function. The topology adopted by Drosophila, that of the segment polarity network, is a top ranking one among all topologies with no direct autoregulation. Furthermore, we found that all robust topologies are modular-each being a combination of three kinds of modules. These modules can be traced back to three subfunctions of the patterning function, and their combinations provide a combinatorial variability for the robust topologies. Our results suggest that the requirement of functional robustness drastically reduces the choices of viable topology to a limited set of modular combinations among which nature optimizes its choice under evolutionary and other biological constraints.

Animals↗
PubMed2005

Transcriptional regulation and metabolism.

Understanding organisms from a systems perspective is essential for predicting cellular behaviour as well as designing gene-metabolic circuits for novel functions. The structure, dynamics and interactions of cellular networks are all vital components of systems biology. To facilitate investigation of these aspects, we have developed an integrative technique called network component analysis, which utilizes mRNA expression and transcriptional network connectivity to determine network component dynamics, functions and interactions. This approach has been applied to elucidate transcription factor dynamics in Saccharomyces cerevisiae cell-cycle regulation, detect cross-talks in Escherichia coli two-component signalling pathways, and characterize E. coli carbon source transition. An ultimate test of system-wide understanding is the ability to design and construct novel gene-metabolic circuits. To this end, artificial feedback regulation, cell-cell communication and oscillatory circuits have been constructed, which demonstrate the design principles of gene-metabolic regulation in the cell.

Escherichia coli↗
PubMed2005

A cross-species analysis of the rodent uterotrophic program: elucidation of conserved responses and targets of estrogen signaling.

Physiological, morphological, and transcriptional alterations elicited by ethynyl estradiol in the uteri of Sprague-Dawley rats and C57BL/6 mice were assessed using comparable study designs, microarray platforms, and analysis methods to identify conserved estrogen signaling networks. Comparative analysis identified 153 orthologous gene pairs that were positively correlated, suggesting conserved transcriptional targets important in uterine proliferation. Functional annotation for these responses were associated with angiogenesis, water and solute transport, cell cycle control, redox control, DNA replication, protein synthesis and transport, xenobiotic metabolism, cell-cell communication, energetics, and cholesterol and fatty acid regulation. The identification of conserved temporal expression patterns of these orthologs provides experimental support for the transfer of functional annotation from mouse orthologs to 44 previously unannotated rat expressed sequence tags based on their homology and co-expression patterns. The identification of comparable temporal phenotypic responses linked to related gene expression profiles demonstrates the ability of systematic comparative genomic assessments to elucidate important conserved mechanisms in rodent estrogen signaling during uterine proliferation.

Animals↗
PubMed2004

Complexity analysis of yeast proteome network.

Topological and compositional complexity of protein-protein networks is assessed in a variety of ways making use of graph theory and information theory. The methodology used is borrowed from mathematical chemistry and includes complexity descriptors such as substructure count, overall connectivity, walk count, and information on various vertex distributions. The approach is applied to the (incomplete) proteome of Saccharomyces cerevisiae containing 232 protein complexes of a total of 1,440 proteins. The proteome network and each of its nine functional subsets of protein complexes are disconnected graphs, containing a number of noninteracting species and a major component. A weighted edge between two vertices in these graphs stands for the number of shared proteins between the respective complexes. The major component is a highly connected, 'small-world' network, in which the average vertex distance between protein complexes does not exceed 2.2 (2.4 for the entire proteome), whereas the maximum distance does not exceed 4 (or 5 for the proteome). The vertex degree distribution in the major proteome component with 199 complexes follows the power law P(k) approximately k(-gamma), with gamma approximately = 1.7. The analysis of the functional organization of the yeast proteome has shown that, for any pair of biological functions, there always exist many proteins that can perform both functions. The potential application of the quantitative proteome descriptors discussed includes quantitative relationships between the structure and biological action of dynamic protein complexes in changing environment, identification of targets for markers/drugs, as well as system analysis and comparative studies of proteomes.

Fungal Proteins↗
PubMed2003

Topology-based orientation analysis of trabecular bone networks.

After bone mineral density, orientation is the major determinant of trabecular bone strength and is thus of significant interest in understanding the clinical implications of osteoporotic bone loss. The methods used to measure orientation and anisotropy of the trabecular bone have largely relied on deriving global measures along test lines, computing the best-fit ellipsoid, and decomposing to eigenvalue-eigenvector pairs that yield the mean orientation and anisotropy of the region. These techniques ignore the differences between measuring the orientation of trabecular plates versus rods, and do not provide insight into the relationship between local orientation and biomechanical stresses. Digital topological analysis allows a unique determination of each voxel's topological class as belonging to a plate, rod, or junction. The digital topology-based orientation analysis (DTA-O) method extracts the voxels belonging to plates and determines the local surface normal by fitting a plane through the local neighborhood BVF map. Modeling regional distributions of these vectors allows assessment of anisotropy measures, such as mean and variance of the orientation distribution. High-resolution microcomputed tomography, synthetic, and in vivo images were used for a validation of the new method and compare the results with the mean intercept length (MIL) technique. The results indicate that DTA-O is a better measure of trabecular orientation and anisotropy than MIL. Applying DTA-O to a recently completed study on the distal radius of 82 subjects [F.W. Wehrli et al., J. Bone Min. Res. 16, 1520 (2001)] shows that the mean orientation and anisotropy at the medial and lateral sides in the distal radius mataphyseal trabecular network are consistent with the mechanical stresses acting on the radius during common tasks.

Adult↗
PubMed2006

Functional analysis of protein kinase networks in living cells: beyond "knock-outs" and "knock-downs".

The identification of over 500 protein kinases encoded by the human genome sequence offers one measure of the importance of protein kinase networks in cell biology. High throughput technologies for inactivating genes are producing an awe-inspiring amount of data on the cellular and organismal effects of reducing the levels of individual protein kinases. Despite these technical advances, our understanding of kinase networks remains imprecise. Major challenges include correctly assigning kinases to particular networks, understanding how they are regulated, and identifying the relevant in vivo substrates. Genetic methods provide a way of addressing these questions, but their application requires understanding the nuances of how different types of mutations can affect protein kinases. The goal of this article is to provide a brief introductory primer into these issues using examples from yeast MAPK cascades and to motivate future systematic genetic analysis focusing on individual residues of protein kinases.

Genetic Techniques↗