Time-dependent ARMA modeling of genomic sequences

<p>Abstract</p> <p>Background</p> <p>Over the past decade, many investigators have used sophisticated time series tools for the analysis of genomic sequences. Specifically, the correlation of the nucleotide chain has been studied by examining the properties of the power spectrum. The main limitation of the power spectrum is that it is restricted to stationary time series. However, it has been observed over the past decade that genomic sequences exhibit non-stationary statistical behavior. Standard statistical tests have been used to verify that the genomic sequences are indeed not stationary. More recent analysis of genomic data has relied on time-varying power spectral methods to capture the statistical characteristics of genomic sequences. Techniques such as the evolutionary spectrum and evolutionary periodogram have been successful in extracting the time-varying correlation structure. The main difficulty in using time-varying spectral methods is that they are extremely unstable. Large deviations in the correlation structure results from very minor perturbations in the genomic data and experimental procedure. A fundamental new approach is needed in order to provide a stable platform for the non-stationary statistical analysis of genomic sequences.</p> <p>Results</p> <p>In this paper, we propose to model non-stationary genomic sequences by a time-dependent autoregressive moving average (TD-ARMA) process. The model is based on a classical ARMA process whose coefficients are allowed to vary with time. A series expansion of the time-varying coefficients is used to form a generalized Yule-Walker-type system of equations. A recursive least-squares algorithm is subsequently used to estimate the time-dependent coefficients of the model. The non-stationary parameters estimated are used as a basis for statistical inference and biophysical interpretation of genomic data. In particular, we rely on the TD-ARMA model of genomic sequences to investigate the statistical properties and differentiate between coding and non-coding regions in the nucleotide chain. Specifically, we define a quantitative measure of randomness to assess how far a process deviates from white noise. Our simulation results on various gene sequences show that both the coding and non-coding regions are non-random. However, coding sequences are "whiter" than non-coding sequences as attested by a higher index of randomness.</p> <p>Conclusion</p> <p>We demonstrate that the proposed TD-ARMA model can be used to provide a stable time series tool for the analysis of non-stationary genomic sequences. The estimated time-varying coefficients are used to define an index of randomness, in order to assess the statistical correlations in coding and non-coding DNA sequences. It turns out that the statistical differences between coding and non-coding sequences are more subtle than previously thought using stationary analysis tools: Both coding and non-coding sequences exhibit statistical correlations, with the coding regions being "whiter" than the non-coding regions. These results corroborate the evolutionary periodogram analysis of genomic sequences and revoke the stationary analysis' conclusion that coding DNA behaves like random sequences.</p

Protein kinase D interacts with neuronal nitric oxide synthase and phosphorylates the activatory residue serine1412

Neuronal Nitric Oxide Synthase (nNOS) is the biosynthetic enzyme responsible for nitric oxide (·NO) production in muscles and in the nervous system. This constitutive enzyme, unlike its endothelial and inducible counterparts, presents an N-terminal PDZ domain known to display a preference for PDZ-binding motifs bearing acidic residues at -2 position. In a previous work, we discovered that the C-terminal end of two members of protein kinase D family (PKD1 and PKD2) constitutes a PDZ-ligand. PKD1 has been shown to regulate multiple cellular processes and, when activated, becomes autophosphorylated at Ser 916, a residue located at -2 position of its PDZ-binding motif. Since nNOS and PKD are spatially enriched in postsynaptic densities and dendrites, the main objective of our study was to determine whether PKD1 activation could result in a direct interaction with nNOS through their respective PDZ-ligand and PDZ domain, and to analyze the functional consequences of this interaction. Herein we demonstrate that PKD1 associates with nNOS in neurons and in transfected cells, and that kinase activation enhances PKD1-nNOS co-immunoprecipitation and subcellular colocalization. However, transfection of mammalian cells with PKD1 mutants and yeast two hybrid assays showed that the association of these two enzymes does not depend on PKD1 PDZ-ligand but its pleckstrin homology domain. Furthermore, this domain was able to pull-down nNOS from brain extracts and bind to purified nNOS, indicating that it mediates a direct PKD1-nNOS interaction. In addition, using mass spectrometry we demonstrate that PKD1 specifically phosphorylates nNOS in the activatory residue Ser 1412, and that this phosphorylation increases nNOS activity and ·NO production in living cells. In conclusion, these novel findings reveal a crucial role of PKD1 in the regulation of nNOS activation and synthesis of ·NO, a mediator involved in physiological neuronal signaling or neurotoxicity under pathological conditions such as ischemic stroke or neurodegeneration.This work was supported by the Ministerio de Economía y Competitividad [SAF2011-26233 to T.I., BFU2009-10442 and BFU2012-37934 to I.R-C.]; Comunidad de Madrid [S2010/BMD-2331-Neurodegmodels-CM to T.I.]; and Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas – CIBERNED, Instituto de Salud Carlos III, to T.I. Postdoctoral fellows L.S-R. and L.G-G. have been funded by research contracts from CIBERNED; Clara Aicart-Ramos is a recipient of a FPU predoctoral fellowship from Ministerio de Economía y Competitividad.Peer Reviewe