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Dynalogo: an interactive sequence logo with dynamic thresholding of matched quantitative proteomic data.

ABSTRACT: SUMMARY:Current web-based sequence logo analyses for studying domain-peptide interactions are often conducted only on high affinity binders due to conservative data thresholding. We have developed Dynalogo, a combination of threshold varying tool and sequence logo generator written in the R statistical programming language, which allows on-the-fly visualization of binding specificity over a wide range of affinity interactions. Hence researchers can easily explore their dataset without the constraint of an arbitrary threshold. After importing quantitative data files, there are various data filtering and visualizing features available. Using a threshold control, users can easily track the dynamic change of enrichment and depletion of amino acid characters in the sequence logo panel. The built-in export function allows downloading filtered data and graphical outputs for further analyses. Dynalogo is optimized for analysis of modular domain-peptide binding experiments but the platform offers a broader application including quantitative proteomics. AVAILABILITY AND IMPLEMENTATION:Dynalogo application, user manual and sample data files are available at https://dynalogo.cam.uchc.edu. The source code is available at https://github.com/lafontaine-uchc/dynalogo. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

SUBMITTER: Lafontaine AT

PROVIDER: S-EPMC7523650 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Dynalogo: an interactive sequence logo with dynamic thresholding of matched quantitative proteomic data.

Lafontaine Adam T AT Mayer Bruce J BJ Machida Kazuya K

Bioinformatics (Oxford, England) 20200301 5

<h4>Summary</h4>Current web-based sequence logo analyses for studying domain-peptide interactions are often conducted only on high affinity binders due to conservative data thresholding. We have developed Dynalogo, a combination of threshold varying tool and sequence logo generator written in the R statistical programming language, which allows on-the-fly visualization of binding specificity over a wide range of affinity interactions. Hence researchers can easily explore their dataset without th ...[more]

PMID: 31609429

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Project description:BackgroundThe cellular signaling pathway (network) is one of the main topics of organismic investigations. The intracellular interactions between genes in a signaling pathway are considered as the foundation of functional genomics. Thus, what genes and how much they influence each other through transcriptional binding or physical interactions are essential problems. Under the synchronous measures of gene expression via a microarray chip, an amount of dynamic information is embedded and remains to be discovered. Using a systematically dynamic modeling approach, we explore the causal relationship among genes in cellular signaling pathways from the system biology approach.ResultsIn this study, a second-order dynamic model is developed to describe the regulatory mechanism of a target gene from the upstream causality point of view. From the expression profile and dynamic model of a target gene, we can estimate its upstream regulatory function. According to this upstream regulatory function, we would deduce the upstream regulatory genes with their regulatory abilities and activation delays, and then link up a regulatory pathway. Iteratively, these regulatory genes are considered as target genes to trace back their upstream regulatory genes. Then we could construct the regulatory pathway (or network) to the genome wide. In short, we can infer the genetic regulatory pathways from gene-expression profiles quantitatively, which can confirm some doubted paths or seek some unknown paths in a regulatory pathway (network). Finally, the proposed approach is validated by randomly reshuffling the time order of microarray data.ConclusionWe focus our algorithm on the inference of regulatory abilities of the identified causal genes, and how much delay before they regulate the downstream genes. With this information, a regulatory pathway would be built up using microarray data. In the present study, two signaling pathways, i.e. circadian regulatory pathway in Arabidopsis thaliana and metabolic shift pathway from fermentation to respiration in yeast Saccharomyces cerevisiae, are reconstructed using microarray data to evaluate the performance of our proposed method. In the circadian regulatory pathway, we identified mainly the interactions between the biological clock and the photoperiodic genes consistent with the known regulatory mechanisms. We also discovered the now less-known regulations between crytochrome and phytochrome. In the metabolic shift pathway, the casual relationship of enzymatic genes could be detected properly.

Project description:Excitotoxicity, caused by overstimulation or dysregulation of ionotropic glutamate receptors (iGluRs), is a pathological process directing neuronal death in many neurological disorders. The aberrantly stimulated iGluRs direct massive influx of calcium ions into the affected neurons, leading to changes in expression and phosphorylation of specific proteins to modulate their functions and direct their participation in the signalling pathways that induce excitotoxic neuronal death. To define these pathways, we used quantitative proteomic approaches to identify these neuronal proteins (referred to as the changed proteins) and determine how their expression and/or phosphorylation dynamically changed in association with excitotoxic cell death. Our data, available in ProteomeXchange with identifier PXD008353, identified over 100 changed proteins exhibiting significant alterations in abundance and/or phosphorylation levels at different time points (5-240 min) in neurons after glutamate overstimulation. Bioinformatic analyses predicted that many of them are components of signalling networks directing defective neuronal morphology and functions. Among them, the well-known neuronal survival regulators including mitogen-activated protein kinases Erk1/2, glycogen synthase kinase 3 (GSK3) and microtubule-associated protein (Tau), were selected for validation by biochemical approaches, which confirmed the findings of the proteomic analysis. Bioinformatic analysis predicted Protein Kinase B (Akt), c-Jun kinase (JNK), cyclin-dependent protein kinase 5 (Cdk5), MAP kinase kinase (MEK), Casein kinase 2 (CK2), Rho-activated protein kinase (Rock) and Serum/glucocorticoid-regulated kinase 1 (SGK1) as the potential upstream kinases phosphorylating some of the changed proteins. Further biochemical investigation confirmed the predictions of sustained changes of the activation states of neuronal Akt and CK2 in excitotoxicity. Thus, future investigation to define the signalling pathways directing the dynamic alterations in abundance and phosphorylation of the identified changed neuronal proteins will help elucidate the molecular mechanism of neuronal death in excitotoxicity.

Dataset Information

Dynalogo: an interactive sequence logo with dynamic thresholding of matched quantitative proteomic data.

Publications

Dynalogo: an interactive sequence logo with dynamic thresholding of matched quantitative proteomic data.

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