HISTORIA Y PROCESOS DE OCUPACION EN LA INTERFASE RUR-

The first application is the detection of significant changes within pathways for each ex- periment. This is accomplished using Fisher’s exact test (FET) on the p-values provided by Hughes et al. (2000) and the Wilcoxon-Mann-Whitney test (WMWT) on the corre- sponding expression ratios as described in section 1.4. The goal of this analysis is to figure out the effect of the observed regulation on the metabolism of the yeast. This application has been carried out with two students at the LMU, Maria Piskarev and Theresa Nieder- berger, and presented as a poster at the German Conference on Bioinformatics (GCB) 2005 in Hamburg.

For each experiment, all genes with a p-value less than 0.05 were selected. The signifi- cance of the overlap with each pathway was then computed using FET. As a second method to estimate the significance of the changes in a pathway, the rank test was employed. The ratios of the genes in each pathway were compared with the ratios of all other genes and the corresponding p-value computed. Figure 6.1 visualizes the results for both tests.

In order to test the performance of the methods, an automatic evaluation scheme was devised. The problem is considered as a classification task where it has to be decided in which pathways a knocked-out gene participates. This property has to be predicted for each pathway/gene pair. Obviously, the participation of a gene in a pathway is not what is measured by the statistical tests. The tests only indicate if the knock-out of a gene influences a pathway. Still, this validation approach is used, as no other ‘hard’ information about the relationship between genes and pathways is available. Furthermore, we assume that in many cases where a gene is part of pathway, the knock-out of the gene should affect that pathway.

We define a classifier that is based on the p-values from one of the two tests: A path- way/gene pair is classified as a correct pair by the prediction method if the corresponding p-value from FET or WMWT is lower than a given threshold. For a given threshold the number of true positive (TP), false positive (FP), true negative (TN) and false negative (FN) assignments can be computed. The performance of the prediction algorithm can be

6.1 Analysis of yeast compendium data 103

Blood group glycolipid biosynthesis−neolactoseries Ganglioside biosynthesis

O−Glycan biosynthesis Fatty acid biosynthesis path 2 Novobiocin biosynthesis Oxidative phosphorylation Benzoate degradation via hydroxylation Fatty acid biosynthesis path 1 Androgen and estrogen metabolism Globoside metabolism Biosynthesis of steroids Glutathione metabolism Cyanoamino acid metabolism Vitamin B6 metabolism 1− and 2−Methylnaphthalene degradation Cysteine metabolism Selenoamino acid metabolism Riboflavin metabolism Prostaglandin and leukotriene metabolism Taurine and hypotaurine metabolism Synthesis and degradation of ketone bodies Thiamine metabolism Glycosphingolipid metabolism Galactose metabolism Inositol phosphate metabolism Pyruvate metabolism beta−Alanine metabolism Propanoate metabolism Limonene and pinene degradation Tryptophan metabolism Glyoxylate and dicarboxylate metabolism Reductive carboxylate cycle CO2 fixation Citrate cycle TCA cycle Carbon fixation Arginine and proline metabolism Urea cycle and metabolism of amino groups Glycine, serine and threonine metabolism Lysine biosynthesis Histidine metabolism Tyrosine metabolism

Phenylalanine, tyrosine and tryptophan biosynthesis Pantothenate and CoA biosynthesis Phenylalanine metabolism Glycolysis Gluconeogenesis Fatty acid metabolism Glycerolipid metabolism Bile acid biosynthesis Phosphatidylinositol signaling system Glycerophospholipid metabolism Benzoate degradation via CoA ligation Purine metabolism Alkaloid biosynthesis II gamma−Hexachlorocyclohexane degradation Aminophosphonate metabolism GlycosylphosphatidylinositolGPI−anchor biosynthesis Lysine degradation Butanoate metabolism Ascorbate and aldarate metabolism Valine, leucine and isoleucine degradation Valine, leucine and isoleucine biosynthesis Tetrachloroethene degradation Nucleotide sugars metabolism Terpenoid biosynthesis One carbon pool by folate Streptomycin biosynthesis Methane metabolism Pentose and glucuronate interconversions Starch and sucrose metabolism Alanine and aspartate metabolism Glutamate metabolism Pyrimidine metabolism Sulfur metabolism Methionine metabolism Aminoacyl−tRNA biosynthesis C21−Steroid hormone metabolism 1,4−Dichlorobenzene degradation Peptidoglycan biosynthesis Cell cycle MAPK signaling pathway N−Glycan biosynthesis Pentose phosphate pathway Fructose and mannose metabolism ATP synthesis Folate biosynthesis Nitrogen metabolism Aminosugars metabolism Nicotinate and nicotinamide metabolism Ubiquinone biosynthesis Biotin metabolism Porphyrin and chlorophyll metabolism Ubiquitin mediated proteolysis

O−Glycan_biosynthesis Blood_group_glycolipid_biosynthesis−neolactoseries Ganglioside_biosynthesis Globoside_metabolism Glycosylphosphatidylinositol(GPI)−anchor_biosynthesis N−Glycan_biosynthesis Glycosphingolipid_metabolism Thiamine_metabolism Phosphatidylinositol_signaling_system Inositol_phosphate_metabolism Nicotinate_and_nicotinamide_metabolism Ubiquitin_mediated_proteolysis MAPK_signaling_pathway Cell_cycle Folate_biosynthesis Purine_metabolism Pyrimidine_metabolism Oxidative_phosphorylation ATP_synthesis Aminosugars_metabolism Ubiquinone_biosynthesis Streptomycin_biosynthesis Starch_and_sucrose_metabolism Galactose_metabolism Fructose_and_mannose_metabolism Glycerophospholipid_metabolism Pentose_and_glucuronate_interconversions Glutathione_metabolism Prostaglandin_and_leukotriene_metabolism Taurine_and_hypotaurine_metabolism Lysine_degradation Butanoate_metabolism Bile_acid_biosynthesis Tetrachloroethene_degradation Ascorbate_and_aldarate_metabolism Nucleotide_sugars_metabolism Benzoate_degradation_via_CoA_ligation Alkaloid_biosynthesis_II Glyoxylate_and_dicarboxylate_metabolism Arginine_and_proline_metabolism Urea_cycle_and_metabolism_of_amino_groups Alanine_and_aspartate_metabolism Lysine_biosynthesis Phenylalanine,_tyrosine_and_tryptophan_biosynthesis Histidine_metabolism Cysteine_metabolism Pantothenate_and_CoA_biosynthesis Valine,_leucine_and_isoleucine_biosynthesis Novobiocin_biosynthesis Vitamin_B6_metabolism One_carbon_pool_by_folate Tyrosine_metabolism 1−_and_2−Methylnaphthalene_degradation Phenylalanine_metabolism Pyruvate_metabolism Citrate_cycle_(TCA_cycle) Reductive_carboxylate_cycle_(CO2_fixation) Glutamate_metabolism Glycine,_serine_and_threonine_metabolism Selenoamino_acid_metabolism Methionine_metabolism Sulfur_metabolism Biotin_metabolism Glycolysis_Gluconeogenesis Carbon_fixation Pentose_phosphate_pathway Fatty_acid_metabolism Glycerolipid_metabolism Porphyrin_and_chlorophyll_metabolism Aminoacyl−tRNA_biosynthesis 1,4−Dichlorobenzene_degradation Valine,_leucine_and_isoleucine_degradation Limonene_and_pinene_degradation beta−Alanine_metabolism Propanoate_metabolism Tryptophan_metabolism Cyanoamino_acid_metabolism Nitrogen_metabolism Methane_metabolism gamma−Hexachlorocyclohexane_degradation Riboflavin_metabolism Peptidoglycan_biosynthesis Biosynthesis_of_steroids Terpenoid_biosynthesis Synthesis_and_degradation_of_ketone_bodies C21−Steroid_hormone_metabolism Fatty_acid_biosynthesis_(path_1) Fatty_acid_biosynthesis_(path_2) Benzoate_degradation_via_hydroxylation Androgen_and_estrogen_metabolism Aminophosphonate_metabolism

Figure 6.1: Significance values for pathways. Top: Significance values using Fisher’s exact test. Bottom: Significance values using Wilcoxon-Mann-Whitney test. Red means up- regulated, green means down-regulated, more significant p-values get higher intensities.

104 6. Applications 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1

True Positives rate

False Positives rate Fisher’s exact test

0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1

True Positives rate

False Positives rate Wilcoxon rank test (6000 genes)

Figure 6.2: Receiver operator curve (ROC) for the threshold classifier based on FET (left) and WMWT (right).

visualized in an ROC plot. The ROC plot for both FET and WMWT is shown in Figure 6.2

The ROC plots seem to indicate that the classifier performance – although better than random guessing – is not very good. But the main reason is that the evaluation does not reflect what the method tries to identify. The tests simply find gene/pathway pairs where the knock-out of the gene affects the regulation of the pathway. This can happen, even if that the gene does not participate in that pathway. The effect can be indirect, for instance, if the knock-out results in a reduced production of some metabolite, another pathway that needs that metabolite may be down-regulated. These indirect effects can potentially lead to many false positives that are actually correct in a biological sense. Therefore, we manually validated some high-scoring hits that are considered false positives in the automatic validation. As such manual validation is usually very time-consuming, a method was desired that could ease the task of finding relevant literature. We use text mining networks generated by ProMiner (Hanisch et al., 2003) and ToPNet to navigate through the literature. Thus, we provide a complete workflow for identifying relevant effects of experiments on pre-defined pathways and validate the results with the help of text-mining tools.

6.1 Analysis of yeast compendium data 105

the WMWT. The reason is probably that the two tests are not based on exactly the same information. FET uses p-values for differential expression which have been calculated taking the fluctuations in control measurements into account. These control measurements were not used for calculating the expression ratios that we used for the WMWT.

We have examined four examples for pathways identified as affected by a knock-out. These examples should demonstrate the kind of information that can be gained by using the proposed tests on known pathways. Furthermore, they should explain why there can be significant differences between the results from the two tests and why pathways can be affected even if the knocked-out gene is not part of it.

The Purine metabolism in the hpt1 knockout is identified as affected in the FET

approach (p-value 4.910−12_{, while the WMWT only results in a p-value of 0.31. In}

SGD, Hpt1p is annotated with the GO term hypoxanthine phosphoribosyltransferase activity. The corresponding EC number is 2.4.2.8. That enzyme is part of the KEGG reference pathway for purine metabolism, but Hpt1p is not present in the corresponding yeast pathway in KEGG. Maybe the enzymatic function of Hpt1p was not known to the KEGG annotators when the yeast pathway was designed. Thus, in our automatic evaluation for FET, this pathway will be counted as a false positive due to a missing annotation in KEGG. Figure 6.3 shows the important parts of the purine metabolism pathway with expression data from the hpt1 knockout experiment. Hpt1p is an enzyme necessary for a step in the salvage pathway, which basically recycles GMP. Thus, the recycling of GMP fails in the mutant, and therefore the de novo synthesis must be up-regulated. The reason for the high p-value for WMWT is that the complete pathway is very large and some genes are down-regulated as well. Therefore the effects on the ranks cancel out.

The Urea cycle and amino group metabolism in the arg80 knockout appears af-

fected using both tests (p-value for FET is 2.010−12_{, the p-value for WMWT is 0.023).}

As in the previous example, the identified pathway does not contain the knocked out gene. But using our text mining approach, we could quickly identify the role of arg80 for that pathway from the network around Arg80p (Figure 6.4, right-hand side). Arg80p forms a complex with Arg81p and Mcm1p, and that complex is re- quired for the repression of arg1, arg3, arg5,6 and arg8, which take part in the argenine biosynthesis and for the induction of car1 and car2, which are necessary for argenine catabolism (Turner et al., 2002). Therefore, as Figure 6.4 (left-hand side) shows, arg1, arg3, arg5,6 and arg8 are up-regulated in the mutant and car1 and car2 are down-regulated.

The leucine biosynthesis in the gcn4 knockout gets a p-value of 2.310−6 _{using FET}

and 9.210−6 _{using WMWT. Again, Gcn4p is not contained in the affected pathway.}

A literature search using our text mining network reveals that Gcn4p induces Leu3p, which is a transcriptional activator of leu1, leu2, leu4, ilv2 and ilv5 (Natarajan et al., 2001). The corresponding proteins are all involved in the leucine biosynthesis path- way and down-regulated in the gcn4 knockout (Figure 6.5).

106 6. Applications

Figure 6.3: The purine metabolism with expression data from the hpt1 knockout experi- ment. Red color indicates up-regulation in the mutant, green down-regulation.

The MAPK signaling pathway in the ste4 knockout is our only example involving

a signaling pathway. Here, the knocked out gene is part of the pathway, the p-

values for FET and WMWT are 2.310−11 _{and 0.02, respectively. Therefore, both}

tests correctly identify the pathway as affected in the ste4 knockout. Figure 6.6 shows what is going on: The Ste4p protein is required for the signal flow from the receptor to the central transcription factor Ste12p. As a consequence, some genes participating in the pathway are down-regulated (the mechanism is not clear for all genes). Although the tests correctly identify the MAPK pathway in this case, it becomes clear that they are not quite appropriate to detect perturbations in signaling pathways. Even though the proteins in a signaling pathway may not be regulated on a transcriptional level, they can induce transcriptional changes in the target genes of the pathway. Therefore, looking at the target genes may often provide more insight into the activity of a pathway than looking at the proteins participating in the pathway. This approach will be used in 6.1.2, where we identify relevant transcription factors by looking at their target genes.

These examples demonstrate that the enrichment analysis on pre-defined pathways often delivers specific and biologically relevant results. With the help of visualization and text mining tools these results can be evaluated and interpreted by the user. On the other hand, the restriction to pre-defined pathways seems a bit strict. Some important links can easily be missed. Looking at metabolic pathways for instance, it is impossible to

6.1 Analysis of yeast compendium data 107

Figure 6.4: Left: The urea cycle and the metabolism of amino groups with expression data from the arg80 knockout experiment. Red color indicates up-regulation in the mutant, green down-regulation. Right: A text-mining network around the knocked out gene arg80.

Figure 6.5: The leucine biosynthesis pathway with expression data from the gcn4 knockout experiment. Red color indicates up-regulation in the mutant, green down-regulation.

108 6. Applications

Figure 6.6: The MAPK signaling pathway with expression data from the ste4 knockout experiment. Red color indicates up-regulation in the mutant, green down-regulation. find the regulatory relationships that are important for the measured expression data. In regulatory pathways, a transcriptional regulation of the participating genes will often not be present. Therefore, such an analysis is more suitable to characterize the effect of the observed expression pattern than to explain the causative regulatory mechanisms.