Adam J. Richards

Revealing a signaling role of phytosphingosine-1-phosphate in yeast.

Sphingolipids including sphingosine‐1‐phosphate and ceramide participate in numerous cell programs through signaling mechanisms. This class of lipids has important functions in stress responses; however, determining which sphingolipid mediates specific events has remained encumbered by the numerous metabolic interconnections of sphingolipids, such that modulating a specific lipid of interest through manipulating metabolic enzymes causes ‘ripple effects’, which change levels of many other lipids. Here, we develop a method of integrative analysis for genomic, transcriptomic, and lipidomic data to address this previously intractable problem. This method revealed a specific signaling role for phytosphingosine‐1‐phosphate, a lipid with no previously defined specific function in yeast, in regulating genes required for mitochondrial respiration through the HAP complex transcription factor. This approach could be applied to extract meaningful biological information from a similar experimental design that produces multiple sets of high‐throughput data.

Assessing the functional coherence of gene sets with metrics based on the Gene Ontology graph

Motivation: The results of initial analyses for many high-throughput technologies commonly take the form of gene or protein sets, and one of the ensuing tasks is to evaluate the functional coherence of these sets. The study of gene set function most commonly makes use of controlled vocabulary in the form of ontology annotations. For a given gene set, the statistical significance of observing these annotations or ‘enrichment’ may be tested using a number of methods. Instead of testing for significance of individual terms, this study is concerned with the task of assessing the global functional coherence of gene sets, for which novel metrics and statistical methods have been devised. Results: The metrics of this study are based on the topological properties of graphs comprised of genes and their Gene Ontology annotations. A novel aspect of these methods is that both the enrichment of annotations and the relationships among annotations are considered when determining the significance of functional coherence. We applied our methods to perform analyses on an existing database and on microarray experimental results. Here, we demonstrated that our approach is highly discriminative in terms of differentiating coherent gene sets from random ones and that it provides biologically sensible evaluations in microarray analysis. We further used examples to show the utility of graph visualization as a tool for studying the functional coherence of gene sets. Availability: The implementation is provided as a freely accessible web application at: http://projects.dbbe.musc.edu/gosteiner. Additionally, the source code written in the Python programming language, is available under the General Public License of the Free Software Foundation. Contact: lux@musc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Long-term ERG analysis in the partially light-damaged mouse retina reveals regressive and compensatory changes

Most of the blinding retinopathies are due to progressive photoreceptor degeneration. Treatment paradigms that are currently being investigated include strategies to either halt or slow down photoreceptor cell loss, or to replace useful vision with retinal prosthesis. However, more information is required on the pathophysiological changes of the diseased retina, in particular the inner retina, that occur as a consequence of photoreceptor cell loss. Here we wished to use light damage as a stoppable insult to determine the structural and functional consequences on inner and outer retina, with the overall goal of determining whether survival of a functional inner retina is possible even if the outer retina is damaged. Mice were exposed to a 20-day light-damage period. Electroretinograms (ERG) and morphology were used to assess subsequent recovery. Outer retina was monitored analyzing a-waves, which represent photoreceptor cell responses, and histology. Integrity of the inner retina was monitored, analyzing b-waves and oscillatory potentials (OP1-OP4) and immunohistochemical markers for known proteins of the inner retina. All six ERG components were significantly suppressed with respect to amplitudes and kinetics, but stabilized in a wave-dependent manner within 40-70 days after the end of light exposure. As expected, damage of the outer retina was permanent. However, function of the inner retina was found to recover significantly. While b-wave amplitudes remained suppressed to 60% of their baseline values, OP amplitudes recovered completely, and implicit times of all components of the inner retina (b-wave and OP1-OP4) recovered to a level close to baseline values. Histological analyses confirmed the lack of permanent damage to the inner retina. In summary, these data suggests that the inner retina has the potential for significant recovery as well as plasticity if treatment is available to stop the deterioration of the outer retina.

Revealing functionally coherent subsets using a spectral clustering and an information integration approach

BackgroundContemporary high-throughput analyses often produce lengthy lists of genes or proteins. It is desirable to divide the genes into functionally coherent subsets for further investigation, by integrating heterogeneous information regarding the genes. Here we report a principled approach for managing and integrating multiple data sources within the framework of graph-spectrum analysis in order to identify coherent gene subsets.ResultsWe investigated several approaches to integrate information derived from different sources that reflect distinct aspects of gene functional relationships including: functional annotations of genes in the form of the Gene Ontology, co-mentioning of genes in the literature, and shared transcription factor binding sites among genes. Given a list of genes, we construct a graph containing the genes in each information space; then the graphs were kernel transformed so they could be integrated; finally functionally coherent subsets were identified using a spectral clustering algorithm. In a series of simulation experiments, known functionally coherent gene sets were mixed and recovered using our approach.ConclusionsThe results indicate that spectral clustering approaches are capable of recovering coherent gene modules even under noisy conditions, and that information integration serves to further enhance this capability. When applied to a real-world data set, our methods revealed biologically sensible modules, and highlighted the importance of information integration. The implementation of the statistical model is provided under the GNU general public license, as an installable Python module, at: http://code.google.com/p/spectralmix.

A Nonparametric Approach to Detect Nonlinear Correlation in Gene Expression

We propose a distribution-free approach to detect nonlinear relationships by reporting local correlation. The effect of our proposed method is analogous to piecewise linear approximation although the method does not utilize any linear dependency. The proposed metric, maximum local correlation, was applied to both simulated cases and expression microarray data comparing the rd mouse with age-matched control animals. The rd mouse is an animal model (with a mutation for the gene Pde6b) for photoreceptor degeneration. Using simulated data, we show that maximum local correlation detects nonlinear association, which could not be detected using other correlation measures. In the microarray study, our proposed method detects nonlinear association between the expression levels of different genes, which could not be detected using the conventional linear methods. The simulation dataset, microarray expression data, and the Nonparametric Nonlinear Correlation (NNC) software library, implemented in Matlab, are included as part of the online supplemental materials.

GOGrapher: A Python library for GO graph representation and analysis.

BackgroundThe Gene Ontology is the most commonly used controlled vocabulary for annotating proteins. The concepts in the ontology are organized as a directed acyclic graph, in which a node corresponds to a biological concept and a directed edge denotes the parent-child semantic relationship between a pair of terms. A large number of protein annotations further create links between proteins and their functional annotations, reflecting the contemporary knowledge about proteins and their functional relationships. This leads to a complex graph consisting of interleaved biological concepts and their associated proteins. What is needed is a simple, open source library that provides tools to not only create and view the Gene Ontology graph, but to analyze and manipulate it as well. Here we describe the development and use of GOGrapher, a Python library that can be used for the creation, analysis, manipulation, and visualization of Gene Ontology related graphs.FindingsAn object-oriented approach was adopted to organize the hierarchy of the graphs types and associated classes. An Application Programming Interface is provided through which different types of graphs can be pragmatically created, manipulated, and visualized. GOGrapher has been successfully utilized in multiple research projects, e.g., a graph-based multi-label text classifier for protein annotation.ConclusionThe GOGrapher project provides a reusable programming library designed for the manipulation and analysis of Gene Ontology graphs. The library is freely available for the scientific community to use and improve.