GeneCensus: genome comparisons in terms of metabolic pathway activity and protein family sharing

Abstract

We present a prototype of a new database tool, GeneCensus, which focuses on comparing genomes globally, in terms of the collective properties of many genes, rather than in terms of the attributes of a single gene (e.g. sequence similarity for a particular ortholog). The comparisons are presented in a visual fashion over the web at GeneCensus.org. The system concentrates on two types of comparisons: (i) trees based on the sharing of generalized protein families between genomes, and (ii) whole pathway analysis in terms of activity levels. For the trees, we have developed a module (TreeViewer) that clusters genomes in terms of the folds, superfamilies or orthologs--all can be considered as generalized 'families' or 'protein parts'--they share, and compares the resulting trees side-by-side with those built from sequence similarity of individual genes (e.g. a traditional tree built on ribosomal similarity). We also include comparisons to trees built on whole-genome dinucleotide or codon composition. For pathway comparisons, we have implemented a module (PathwayPainter) that graphically depicts, in selected metabolic pathways, the fluxes or expression levels of the associated enzymes (i.e. generalized 'activities'). One can, consequently, compare organisms (and organism states) in terms of representations of these systemic quantities. Develop ment of this module involved compiling, calculating and standardizing flux and expression information from many different sources. We illustrate pathway analysis for enzymes involved in central metabolism. We are able to show that, to some degree, flux and expression fluctuations have characteristic values in different sections of the central metabolism and that control points in this system (e.g. hexokinase, pyruvate kinase, phosphofructokinase, isocitrate dehydrogenase and citric synthase) tend to be especially variable in flux and expression. Both the TreeViewer and PathwayPainter modules connect to other information sources related to individual-gene or organism properties (e.g. a single-gene structural annotation viewer).

Figure 1

(Opposite) A pictorial overview of GeneCensus through screenshots. The top image shows the homepage, which, in addition to linking to pages in GeneCensus, also provides links to multiple other bioinformatics resources, including pages on gene expression, protein interactions and pseudogenes. GeneCensus bifurcates into two semi-independent modules, the TreeViewer and PathwayPainter, shown on the second level of the diagram. Information relevant to TreeViewer can be accessed through the secondary modules (as seen on the third tier) such as the OrganismViewer. Similarly, enzyme-specific data, as opposed to genome-specific enzyme data, can be viewed. The fourth and final level of GeneCensus provides more specific information for many of the ORFs in the ORFViewer, as well as some smaller modules with less generalized information. These include information on: (i) transmembrane proteins, (ii) pseudogenes, (iii) thermophile analysis, and (iv) data on folds for both the worm and yeast genome.

Figure 2

An annotated close-up of the TreeViewer module. The figure highlights the important parts of the web page format. The top bar, which is maintained throughout the site, provides a search option, a help file and links to PartsList (36), NESGC (41) and Molecular Motions Database (48). To manipulate the view of the data on the web page we provide a menu bar to select which type of tree to view and a second menu bar to determine in which secondary dimension to view the tree. In addition, there are multiple color-coded links next to each organism—green for metabolic pathways, blue for the organism page and red for other Yale pages associated with that organism. For examples of the multiple views, we present a COG tree viewed through genome composition and a fold tree viewed in comparison to the traditional ribosomal tree.

Figure 3

The second major module, the PathwayPainter. As with the TreeViewer, the top menu bar is maintained. This page is built around the metabolic pathway. We present an overview image of two pathways in the center of the page. Flanking the image are the component enzymes, with a value next to each enzyme. Using the menu on the top of the page, the user can select the desired value for those enzymes in that pathway. These include the flux values for multiple organisms, various expression values for yeast and E.coli, and PID variability. Additionally, each enzyme links to an enzyme-oriented page which displays the data in its entirety for that specific enzyme.

Figure 4

A cross-section of the results that can be seen with the PathwayPainter module. Enzymes were chosen from three metabolic pathways: citric acid cycle (blue), glycolysis (green) and pentose phosphate pathway (red); the information presented includes expression, flux and sequence similarity data. We present expression data, the relative expression of a gene in relation to a control, from six experiments: yeast diauxic shift, yeast sporulation, E.coli UV response, C.elegans mutant germline, and two yeast cell-cycle expression sets. We summarized the data by calculating the standard deviation and the average for each enzyme profile in each experiment, as well as combined statistics for all the experiments. Values in the top quartile were shaded black, in the middle two, gray, and in the bottom quartile, white. Sequence similarities of the enzymes were calculated by averaging the percentage sequence identity between orthologous genes. These are shaded in the same fashion as the expression values. For the flux values, we calculated the standard deviation of the flux values for all organisms examined. Values in the top quartile are colored aqua, middle two quartiles, yellow, and bottom quartile, purple. We show a schematic of all three pathways, with enzyme numbers color coded by pathway. The arrows representing the reaction are colored by the degree of flux variation; this seems to correlate closely with the pathways. TCA shows the greatest flux values and the pentose phosphate pathway comprises the lowest. The pink crosses label all the irreversible control points in the metabolic system. The average PID of the enzyme seems to have little correlation with the expression or flux values. Clearly, the figure also shows a relationship between flux and the enzyme’s resources, including placement in the overall pathway structure.