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

Is organizational justice climate at the workplace associated with individual-level quality of care and organizational affective commitment?:A multi-level, cross-sectional study on dentistry in Sweden

Purpose The aim of this study is to investigate whether organizational justice climate at the workplace level is associated with individual staff members’ perceptions of carequality and affective commitment to the workplace.Methods The study adopts a cross-sectional multi-level design. Data were collected using an electronic survey and a response rate of 75% was obtained. Organizational justice climate and affective commitment to the workplace were measured by items from Copenhagen Psychosocial Questionnaire and quality of care by three self-developed items. Non-managerial staff working at dental clinics with at least five respondents (n = 900 from 68 units) was included in analyses. A set of Level-2 random intercept models were built to predict individual-level organizational affective commitment and perceived quality of care from unit-level organizational justice climate, controlling for potential confoundingby group size, gender, age, and occupation.Results The results of the empty model showed substantial between-unit variation for both affective commitment (ICC-1 = 0.17) and quality of care (ICC-1 = 0.12). The overall results showed that the shared perception of organizational justice climate at the clinical unit level was significantly associated with perceived quality of care and affective commitment to the organization (p < 0.001).Conclusions Organizational justice climate at work unit level explained all variation in affective commitment among dental clinics and was associated with both the individualstaff members’ affective commitment and perceived quality of care. These findings suggest a potential for that addressing organizational justice climate may be a way to promote quality of care and enhancing affective commitment. However, longitudinal studies are needed to support causality in the examined relationships. Intervention research is also recommended to probe the effectiveness of actions increasingunit-level organizational justice climate and test their impact on quality of care and affective commitment

High Glucose Suppresses Human Islet Insulin Biosynthesis by Inducing miR-133a Leading to Decreased Polypyrimidine Tract Binding Protein-Expression

BACKGROUND: Prolonged periods of high glucose exposure results in human islet dysfunction in vitro. The underlying mechanisms behind this effect of high glucose are, however, unknown. The polypyrimidine tract binding protein (PTB) is required for stabilization of insulin mRNA and the PTB mRNA 3'-UTR contains binding sites for the microRNA molecules miR-133a, miR-124a and miR-146. The aim of this study was therefore to investigate whether high glucose increased the levels of these three miRNAs in association with lower PTB levels and lower insulin biosynthesis rates. METHODOLOGY/PRINCIPAL FINDINGS: Human islets were cultured for 24 hours in the presence of low (5.6 mM) or high glucose (20 mM). Islets were also exposed to sodium palmitate or the proinflammatory cytokines IL-1beta and IFN-gamma, since saturated free fatty acids and cytokines also cause islet dysfunction. RNA was then isolated for real-time RT-PCR analysis of miR-133a, miR-124a, miR-146, insulin mRNA and PTB mRNA contents. Insulin biosynthesis rates were determined by radioactive labeling and immunoprecipitation. Synthetic miR-133a precursor and inhibitor were delivered to dispersed islet cells by lipofection, and PTB was analyzed by immunoblotting following culture at low or high glucose. Culture in high glucose resulted in increased islet contents of miR-133a and reduced contents of miR-146. Cytokines increased the contents of miR-146. The insulin and PTB mRNA contents were unaffected by high glucose. However, both PTB protein levels and insulin biosynthesis rates were decreased in response to high glucose. The miR-133a inhibitor prevented the high glucose-induced decrease in PTB and insulin biosynthesis, and the miR-133a precursor decreased PTB levels and insulin biosynthesis similarly to high glucose. CONCLUSION: Prolonged high-glucose exposure down-regulates PTB levels and insulin biosynthesis rates in human islets by increasing miR-133a levels. We propose that this mechanism contributes to hyperglycemia-induced beta-cell dysfunction

Expression of zebrafish pax6b in pancreas is regulated by two enhancers containing highly conserved cis-elements bound by PDX1, PBX and PREP factors

BACKGROUND: PAX6 is a transcription factor playing a crucial role in the development of the eye and in the differentiation of the pancreatic endocrine cells as well as of enteroendocrine cells. Studies on the mouse Pax6 gene have shown that sequences upstream from the P0 promoter are required for expression in the lens and the pancreas; but there remain discrepancies regarding the precise location of the pancreatic regulatory elements. RESULTS: Due to genome duplication in the evolution of ray-finned fishes, zebrafish has two pax6 genes, pax6a and pax6b. While both zebrafish pax6 genes are expressed in the developing eye and nervous system, only pax6b is expressed in the endocrine cells of the pancreas. To investigate the cause of this differential expression, we used a combination of in silico, in vivo and in vitro approaches. We show that the pax6b P0 promoter targets expression to endocrine pancreatic cells and also to enteroendocrine cells, retinal neurons and the telencephalon of transgenic zebrafish. Deletion analyses indicate that strong pancreatic expression of the pax6b gene relies on the combined action of two conserved regulatory enhancers, called regions A and C. By means of gel shift assays, we detected binding of the homeoproteins PDX1, PBX and PREP to several cis-elements of these regions. In constrast, regions A and C of the zebrafish pax6a gene are not active in the pancreas, this difference being attributable to sequence divergences within two cis-elements binding the pancreatic homeoprotein PDX1. CONCLUSION: Our data indicate a conserved role of enhancers A and C in the pancreatic expression of pax6b and emphasize the importance of the homeoproteins PBX and PREP cooperating with PDX1, in activating pax6b expression in endocrine pancreatic cells. This study also provides a striking example of how adaptative evolution of gene regulatory sequences upon gene duplication progressively leads to subfunctionalization of the paralogous gene pair

Stochastic particle packing with specified granulometry and porosity

This work presents a technique for particle size generation and placement in arbitrary closed domains. Its main application is the simulation of granular media described by disks. Particle size generation is based on the statistical analysis of granulometric curves which are used as empirical cumulative distribution functions to sample from mixtures of uniform distributions. The desired porosity is attained by selecting a certain number of particles, and their placement is performed by a stochastic point process. We present an application analyzing different types of sand and clay, where we model the grain size with the gamma, lognormal, Weibull and hyperbolic distributions. The parameters from the resulting best fit are used to generate samples from the theoretical distribution, which are used for filling a finite-size area with non-overlapping disks deployed by a Simple Sequential Inhibition stochastic point process. Such filled areas are relevant as plausible inputs for assessing Discrete Element Method and similar techniques

Regulation of CCL2 Expression by an Upstream TALE Homeodomain Protein-Binding Site That Synergizes with the Site Created by the A-2578G SNP

CC Chemokine Ligand 2 (CCL2) is a potent chemoattractant produced by macrophages and activated astrocytes during periods of inflammation within the central nervous system. Increased CCL2 expression is correlated with disease progression and severity, as observed in pulmonary tuberculosis, HCV-related liver disease, and HIV-associated dementia. The CCL2 distal promoter contains an A/G polymorphism at position -2578 and the homozygous -2578 G/G genotype is associated with increased CCL2 production and inflammation. However, the mechanisms that contribute to the phenotypic differences in CCL2 expression are poorly understood. We previously demonstrated that the -2578 G polymorphism creates a TALE homeodomain protein binding site (TALE binding site) for PREP1/PBX2 transcription factors. In this study, we identified the presence of an additional TALE binding site 22 bp upstream of the site created by the -2578 G polymorphism and demonstrated the synergistic effects of the two sites on the activation of the CCL2 promoter. Using chromatin immunoprecipitation (ChIP) assays, we demonstrated increased binding of the TALE proteins PREP1 and PBX2 to the -2578 G allele, and binding of IRF1 to both the A and G alleles. The presence of TALE binding sites that form inverted repeats within the -2578 G allele results in increased transcriptional activation of the CCL2 distal promoter while the presence of only the upstream TALE binding site within the -2578 A allele exerts repression of promoter activity

Copy-number-variation and copy-number-alteration region detection by cumulative plots

Background: Regions with copy number variations (in germline cells) or copy number alteration (in somatic cells) are of great interest for human disease gene mapping and cancer studies. They represent a new type of mutation and are larger-scaled than the single nucleotide polymorphisms. Using genotyping microarray for copy number variation detection has become standard, and there is a need for improving analysis methods. Results: We apply the cumulative plot to the detection of regions with copy number variation/alteration, on samples taken from a chronic lymphocytic leukemia patient. Two sets of whole-genome genotyping of 317k single nucleotide polymorphisms, one from the normal cell and another from the cancer cell, are analyzed. We demonstrate the utility of cumulative plot in detecting a 9Mb (9 x 10^6 bases) hemizygous deletion and 1Mb homozygous deletion on chromosome 13. We also show the possibility to detect smaller copy number variation/alteration regions below the 100kb range. Conclusions: As a graphic tool, the cumulative plot is an intuitive and a scale-free (window-less) way for detecting copy number variation/alteration regions, especially when such regions are small