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Case Study: GALACTOSE METABOLISM This case study is focussed on the GAL genes involved in Galactose Metabolism and uses YETI to attempt to identify all the genes/proteins involved in the process of galactose metabolism by using YETI to explore the various functional genomic data sets and find associations. In addition to this case study guide, there are also Help functions on the menu bar within YETI to describe the various options and functions of YETI. Launch YETI and select Sections then Genome from the menu bar at the top to launch the Genome Section. In the Genome Section, select Chromosomes then Chromosome 2 from the menu bar to launch the Chromosome viewer. Using the horizontal scrollbar at the bottom, scroll along the chromosome until just after the centromere where a cluster of GAL genes are viewable: GAL1, GAL7 and GAL10. You can simply mouse click on any of these genes to launch a gene Datasheet on them, as can be seen they are all characterised with the Galactose Metabolism GO annotation. Select Array then Experiment from the menu bar to launch the microarray experiment selector. Scroll down to the “YP Galactose vs Reference Pool” experiment and select it by mouse clicking on it. Then click the Select button to re-launch the chromosome viewer but this time with the gene expression data from the selected experiment overlayed. As can now be seen the three GAL genes as well as the neighbouring FUR4 gene form a chromosome cluster of highly up-regulated genes. Remove the overlayed gene expression data by selecting Array then Experiment from the menu bar. Now select the cluster of 4 up-regulated (GAL1, GAL7, GAL10, and FUR4) from the chromosome by selecting Select then Multiple from the menu bar and then selecting the genes by mouse clicking on them – they well then be highlighted in red and are ready to be investigated in further detail in the other YETI sections.  Select Sections then Transcriptome from the menu bar to launch the Transcriptome Section. Using the vertical scrollbar, scroll right down to the bottom of the hierarchically clustered data set where the location of the GAL genes is highlighted with red lines. As can be seen, all the genes are located in the same gene expression cluster. Now, transfer the 4 originally selected genes from the red to the green group by selecting Groups then R to G from the menu bar. Then select the surrounding genes from the expression cluster by simply dragging the mouse vertically to create a selection box – where all the genes within the selection box will be selected, highlighted and assigned to the red group.  Select Sections then Analysis from the menu bar to launch the Analysis Section to display a data table containing a wide range of information on all the selected genes from both groups. Examining the members of data table, which are therefore members of the gene expression cluster, reveals the presence of another three GAL genes (GAL2, GAL3, and GAL80).  Close the Analysis Section and go back to the Transcriptome Section. Select Sections then Proteome from the menu bar to launch the Proteome Section. As can be seen, YETI automatically displays all the protein-protein interactions involving any of the genes in either of the selected groups. The green group comprises the 4 original genes from chromosome 2 and the red group comprises the surrounding genes from the expression cluster in the Transcriptome Section. As can be seen two members of the green group (GAL7 and GAL1) form an interaction cluster with two members of the red group (GAL80 and GAL3). In addition, another GAL gene is also present in this interaction cluster (GAL4).  At this point, it is worth comparing the observation made through using YETI to what is already known about the galactose metabolism pathway. The galactose metabolism pathway has been extensively studied with the majority of components already identified and characterised. It is a classic example of a genetic regulatory switch, in which enzymes required for the transport and catabolism of galactose are expressed only when galactose is present and repressing sugars such as glucose absent. The first component of the pathway is GAL2 which encodes a galactose permease that transports galactose into the cell. Next are the enzymatic proteins of the pathway consisting of GAL10 (galactose mutarotase & UDP-glucose 4-epimerase), GAL1 (galactokinase), GAL7 (galactose-1-phosphate uridyl transferase), and PGM2 and PGM1 (both phosphoglucomutases). GAL4, GAL3 and GAL80 are all involved in the regulation of the enzymatic proteins and transporter. GAL4 is a DNA-binding factor that can strongly activate their transcription, but in the absence of galactose GAL80 binds to the activation domain of GAL4 and inhibits its activity. When galactose is present in the cell, it causes the activation of GAL3 which can bind to GAL80 and alter the GAL4/GAL80 complex. This causes the GAL4 activation domain to become available and results in the high expression of the enzymatic and transporter genes.  Although YETI does not necessarily reveal anything new about the process of galactose metabolism, this case study does demonstrate the potential of YETI, as it was able to easily and rapidly identify the majority of this pathway, including transcription factors, based on the experimental data alone. Firstly, the Chromosome Window highlighted that three adjacent genes on chromosome 2 (GAL7, GAL1, GAL10) were highly up-regulated in the “YP Galactose vs. Reference Pool” microarrray experiment. Secondly, the Transcriptome Section showed that these three genes were located in the same cluster of the hierarchical tree as GAL2; this is now expected as these four genes are the core components of the galactose metabolism pathway and are regulated by the same factors. Thirdly, the Transcriptome Section also showed that GAL80 and GAL3 were located in the same cluster of the hierarchical tree as the above four genes. Furthermore, the Proteome Section showed that GAL80 interacts directly with both GAL3 and GAL4 (as well as GAL1). This is now expected as the interaction of GAL80 with GAL3 and GAL4 is the main regulatory mechanism of the galactose metabolism pathway. In addition, YETI also associated FUR4 (a permease involved in the transport of uracil) with the galactose pathway as it is co-located and co-expressed with the GAL cluster on chromosome 2. Uracil diphosphate (UDP) is involved in galactose metabolism via GAL10, which converts UDP-D-Galactose to UDP-D-Glucose, thus linking FUR4 to the pathway. Furthermore, the transcription of FUR4 has been shown to be induced in response to galactose in a GAL4 dependent manner. However, YETI did not manage to associate PGM1 or PGM2 with the other galactose metabolism genes. This can be explained by the fact that none of the enzymatic proteins of the pathway appear to interact with each other or with the transporter protein. This could be due to poor coverage and false-negatives resulting in an incomplete protein-protein interaction data set or could be expected as the enzymes in a pathway do not typically need to interact with each other to fulfil their biological functions. Furthermore, PGM1 and PGM2 do not share similar patterns of expression with the GAL genes because PGM1 and PGM2 are involved in many metabolic pathways (e.g. galactose metabolism, glycogen catabolism, lactose degradation and sucrose biosynthesis), which means the expression of PGM1 and PGM2 differs from the expression of the other GAL genes in the presence of other sugars. In general, this case study highlights a number of the advantages of YETI. One of the main aims of YETI was to integrate different functional genomic data sets and provide clear graphical representations that enable users to easily and rapidly explore the stored data sets and find interesting features. This is exemplified by the ability of YETI to overlay gene expression data onto entire chromosomes, which enables users to rapidly explore possible correlations between gene location and expression and easily select any regions of interest to investigate further. Furthermore, the group approach combined with the inter-linked sections of YETI enables users to collectively investigate if and how a group of potentially related genes are working together in order to achieve their biological goal and to also investigate what other genes/proteins they may be working with. This is demonstrated quite well in this case study as starting from a triplet of co-located and co-expressed genes involved in galactose metabolism, which YETI automatically highlighted, YETI was able to associate them with all the other genes involved in galactose metabolism, including transcription factors, through the collective investigation of their expression and interaction partners. Furthermore, this case study also highlights how combining the different functional genomic data sets can lead to a more complete understanding of the system being investigated, as no single YETI section (and therefore corresponding data set) would have been enough to reconstitute the entire galactose pathway. Although in this instance, nothing new was highlighted about the process of galactose metabolism and no genes of unknown function were associated, it does show the potential for such an approach in a less well studied biological process or organism. Furthermore, it also suggests that YETI may well be a useful tool for the reconstruction of pathways and gene networks in such instances.
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| YETI - Yeast Exploration Tool Integrator - Richard Orton |
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