Supplemental Data


Abstract
Full Text
Integrated Genomic and Proteomic Analyses of a Systematically Perturbed Metabolic Network
T. Ideker, V. Thorsson, J. A. Ranish, R. Christmas, J. Buhler, J. K. Eng, R. Bumgarner, D. R. Goodlett, R. Aebersold, L. Hood

Supplementary Material

Supplemental Figure 1.
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Integrated physical-interaction network (PDF format, enlarged from Fig. 4A). Gene expression data from this study are shown in the context of a network of previously observed physical interactions. Nodes represent genes and are labeled with their corresponding gene names. Connections between nodes display physical interactions as recorded in the public databases, where a yellow arrow directed from one node to another represents a protein --> DNA interaction, and a blue line between nodes represents a protein-protein interaction. Global changes in mRNA expression (in this case, in response to a deletion of GAL4 in the presence of galactose) are visually superimposed on the network. The grayscale intensity of each node indicates the change in mRNA expression of the corresponding gene, where medium gray represents no change, darker or lighter shades represent an increase or decrease in expression, respectively (as in Fig. 2), and node diameter scales with the overall magnitude of change. GAL4 is colored in red to signify that its expression level has been perturbed by external means. When viewing this PDF in Acrobat Reader, use the magnifying glass to obtain more detail on network regions of interest and to enlarge the names of individual genes. Highly interconnected groups of genes tend to have common biological function and are annotated accordingly (rectangular labels).

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Supplemental Figure 2. Comparison of Northern vs. microarray analysis. Total RNA from yeast growing in each of eight perturbation conditions (column headings) was probed with radiolabeled GAL1 or GAL80 cDNA using a Northern blot assay. The actin gene (ACT1) was also labeled as a control expected to be expressed at constant levels over all conditions. Corresponding changes in gene expression measured with the yeast-genome microarray are depicted graphically beneath each blot. Microarray data are shown relative to wt yeast growing in galactose (column 5), with medium-gray representing no change, darker or lighter shades representing increasing or decreasing amounts of expression respectively, and spot size scaling with the magnitude of change. The quantitative log10 change in expression level is annotated in each spot center.


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Supplemental Table 1. mRNA- and protein-expression responses to galactose pathway perturbations (Microsoft Excel format). This table reports mRNA-expression ratios measured across all 23 experimental perturbations (20 initial and three follow-up perturbations) using yeast-genome microarrays, as well as protein-abundance ratios for each of 289 proteins measured between wt+gal and wt-gal conditions using ICAT technology.

To view Supplemental Table 1 in excel format click here


Supplemental Table 2. Physical interactions linking genes with strong expression-profile correlations [accompanies Step (iii) of the text]. Pearson correlations rAB were computed between the expression profiles of pairs of genes A and B, where each profile consists of log10 mRNA-expression ratios over our galactose-pathway perturbations (all 20 except the wt+gal versus wt+gal control). Strong correlations (|rAB| > 0.4) are reported for all cases in which [a] protein A is involved in a known proteinrarrowDNA interaction with gene B (ArarrowB); [b] protein A affects B through a signaling pathway involving a protein-protein interaction between A and a third protein C (A-CrarrowB); [c] genes A and B are regulated by proteinrarrowDNA interactions from a common transcription factor C (CrarrowA,B). These interactions are hypothesized to have transmitted expression changes from one gene to another in one or more perturbations. When protein C regulates more than two genes (A, B, ...), we list the range of all pairwise correlations among regulated genes.
[a] ArarrowB
ABrAB
Alpha2STE2-0.545
BAR1-0.469
Cbf1PGK1-0.574
MET160.471
Cup2CUP1B0.631
CUP1A0.573
Gcn4ARG10.422
Gcr1PGK10.579
TPI10.575
CDC190.544
ENO10.535
ENO20.493
Hap1CTT1-0.45
Hsf1SSA4-0.409
Mac1FRE10.476
Mcm1FAR10.817
MFA2-0.675
MFA1-0.526
PIS1-0.445
Met28MET160.471
Met4MET160.52
Mig1FBP10.633
HAP40.601
GAL1-0.549
GAL3-0.477
GAL10-0.465
Msn2SUC2-0.416
Pdr1HXT9-0.427
Put3PUT10.535
Rox1HEM13-0.431
Sip4PCK10.513
FBP10.5
ICL10.48
MLS10.429
Sko1SUC20.433
Ste12MFA2-0.456


[b] A-CrarrowB
ACBrAB
Gcr2Gcr1TPI1-0.859
PGK1-0.675
CDC19-0.463
ENO2-0.443
Hsp42Rap1PGK10.529
TPI10.48
CDC190.441
Mcm1Alpha2MFA1-0.526
MFA2-0.675
Ste12MFA1-0.526
MFA2-0.675
Rad52Rfa1CAR10.697
Rfa2CAR10.697
Tpo1Cup2CUP1A0.469
Ydr412WPdr1HXT9-0.451


[c] Crarrow(A, B, ...)
C(A, B, ...)rAB
Alpha2BAR1, MFA1, MFA2, STE20.427 to .772
Gal11GAL1, GAL2, GAL7, GAL100.807 to 0.919
Gal4GAL1, GAL2, GAL7, GAL10, GCY10.787 to 0.919
Gcn4ADE4, HIS3, HIS4, HIS70.489 to 0.893
Gcr1ENO1, ENO2, CDC19, PGK1, TPI10.563 to 0.891
Hap1CYB2, CYC1, CYC70.388 to 0.753
Hsf1HSP26, CUP10.555
Mcm1BAR1, MFA1, MFA2, PIS1, STE20.427 to 0.854
CLB1, FAR10.579
Mig1GAL1, GAL3, GAL100.726 to 0.898
FBP1, HAP40.665
Rap1CDC19, ENO1, ENO2, PDC1, PGK1, TPI10.502 to 0.891
HIS4, RPL16A, RPL18A, RPL18B, RPL25, RPS17A, RPS24A, RPS24B0.503 to 0.969
Sip4FBP1, ICL1, MLS1, PCK10.669 to 0.882
Ste12MFA1, MFA2, STE20.679 to 0.772


Supplemental Table 3.Gal4p binding-site predictions [accompanies Step (iii) of the text]. We looked for the well-characterized Gal4p-binding site (UASGAL) upstream of the 997 genes whose mRNA levels were significantly affected by our 20 GAL-pathway perturbations. Of the 41 genes in this set with UASGAL predictions, Gal4p-binding had been confirmed experimentally for seven of these (shown in bold) [R. J. Bram, N. F. Lue, R. D. Kornberg, EMBO J.5, 603-608 (1986); M. Angermayr, W. Bandlow, J. Biol. Chem.272, 31630-31635 (1997); W. Zheng, H. E. Xu, S. A. Johnston, J. Biol. Chem.272, 30350-30355 (1997)]. Since all experimentally-confirmed genes fell into expression clusters 1, 2, or 3, we suggest that other genes in these clusters with predicted UASGAL sites may be directly regulated by Gal4p. Note that the average expression profiles of clusters 1, 2, and 3 are also highly correlated with one another (r12 = 0.8; r13 = 0.8; r13 = 0.7).
ClusterGeneUpstream position
(+/- strand)
Core similarity scoreMatrix similarity scoreSequence
1GAL1
(GAL10shares regul. region)
456 (+)1.0000.840GTACGGATTAGAAGCCGCCGAGC
437 (+)1.0000.880GAGCGGGCGACAGCCCTCCGACG
419 (+)0.8750.920CGACGGAAGACTCTCCTCCGTGC
355 (+)1.0000.860CCTCGCGCCGCACTGCTCCGAAC
1GAL7336 (-)1.0000.860CCTCGCGCCGCACTGCTCCGAAC
272 (-)0.8750.920CGACGGAAGACTCTCCTCCGTGC
254 (-)1.0000.880GAGCGGGCGACAGCCCTCCGACG
2GCY1372 (-)1.0000.840CCCCGGAATAGTCTGCCCCGATT
2GAL6 (LAP3)67 (+)1.0000.870CGCCGGCTGACAAGTCGCCGACG
2MLF3169 (-)1.0000.924CCGCGGAGTGCTCTTCGCCGAGA
2PCL10235 (-)1.0000.848GATCGGTGCAATATACTCCGAGC
2YEL057C417 (+)1.0000.828GGACGGGCGGCTGCCGTCCGGGG
2YPL066W101 (+)1.0000.827TCACGGTCATCACTGCTCCGACA
2YPS3211 (+)1.0000.876GATCGGATTACTATTCGCGGAAA
3GAL2533 (-)1.0000.905TTCCGGAAGGAAGCTTTCCGAAT
419 (+)0.8000.829CACCGGCGGTCTTTCGTCCGTGC
400 (-)0.8750.811GAACGGCGCAGATATCTCCGCAC
336 (+)1.0000.856TATCGGGGCGGATCACTCCGAAC
331 (+)0.7250.806GGGCGGATCACTCCGAACCGAGA
3GAL3291 (-)1.0000.818GTTCGGCACACAGTGGACCGAAC
3GAL80175 (+)0.8750.903TACCGGCGCACTCTCGCCCGAAC
3RPA49249 (+)0.8000.825GACCGGACACCTAATCACCGACG
3YMR318C239 (+)1.0000.867GTCCGGTCCGTCCTTGACCGAAG
3YPR194C624 (-)1.0000.855CGTCGGACAGCAACCCCCCGATT
5YLR201C143 (-)1.0000.938CTTCCGCCTAATATAGTCCGAAA
7ADK1226 (-)1.0000.952CTGCTGCGGACAGTTCTCCGTGA
7RPL27A175 (+)1.0000.804TTGCTGCAGAGATTCGCCCGAAG
7RPL34B389 (+)1.0000.830TTTCGGAGGTCCCGCTTCCGACA
7YGR090W381 (-)1.0000.814CAACGGCATGCAGCGAGCCGTAG
8BAP3750 (+)1.0000.812GTGCGAAGTAGTATGATCCGAAG
8YLR042C276 (+)0.8750.813ATTTGGCCAAGAATGCCCCGAAC
10MTH1472 (-)1.0000.801GCACGGACTCCATTTCCCCGGAC
10NAR1460 (-)1.0000.823CGCCGGCTGACAAGTCGCCGACG
10YJL217W259 (-)1.0000.819GAACGGTACTTATTTCCCCGAAA
10YLR352W298 (-)0.8750.819GCGCGGGTAACATACCTCCGTGA
11ISU1306 (-)1.0000.805AAGCGGAGATAAAGCCTCCGAAC
12MRK1268 (-)1.0000.800CATCGGACGACTTTGCTCCCAGG
12POR1281 (+)0.8750.825GATCGGGGTTCAATTCCCCGTCG
12USV1251 (+)0.8000.834AACCGTTCAACAGTCTTCCGTAT
12YJL112W526 (+)1.0000.827GATCGGGGTTCAATTCCCCGTCG
12YMR031C305 (-)1.0000.840TTTTGGGTAACAGCGGACCGAAG
12YMR044W23 (+)0.8750.836GAACGGCGTGTCATTCTCCGATA
12YMR098C299 (+)1.0000.853AATGGGGTCACAATCATCCGAAC
13GAL5 (PGM2)337 (-)0.7250.828CTCCGCGCTTCTCTTCACCGAGC
175 (+)0.8750.817ATCTGGATGACTGCCGCCCGAAC
14MBR1313 (+)1.0000.804GAGCGGCTCCCCTTTCCCCGGAA
14YJL045W524 (+)0.8750.802GATCGGGGTTCAATTCCCCGTCG
14YLR164W243 (+)0.8750.817GATTGGAGTACCCTTATCCGAAG
15ICL1407 (+)1.0000.804CCCAGGTTTCCATTCATCCGAGC
16YIL057C192 (+)1.0000.801CGGCGGTTGGCAATCGTCCGTAT


Supplemental Table 4. New observations, hypotheses, and possible tests
section OBSERVATIONS EXAMPLE HYPOTHESESsection SYSTEMS-LEVEL TESTS
[1]Effect of gal80Δ-gal: slow growth and widespread changes in metabolic-gene expression Stress-related, caused by derepression of either the GAL enzymes or transporter ddag Examine EP of a gal4Δgal80Δ-gal double deletion, in which the GAL enzymes and transporter are not expressed
[2]Decrease in GAL-gene expression in response to gal7Δ+gal or gal10Δ+gal dag Dependent on levels of Gal-1-P or a derivative metabolite ddag Examine EP of a gal1Δgal10Δ+gal double deletion strain, in which Gal-1-P levels are reduced
[3]GAL5 and GAL6 mRNA levels are unaffected by galactose addition or by deletion of GAL3, 4, or 80Caused by differences in strains and/or media between this and previous studiesObtain EPs for identical strains and media as in previous studies
[4]GAL6 deletion does not affect mRNA levels of GAL enzymes
[5]Expression levels of genes in many other metabolic pathways respond to perturbations of the GAL pathwayEach affected pathway depends on galactose, specific GAL genes, or on the total amount of available energyExamine EPs of yeast growing in carbon sources other than galactose, e.g. 2% glucose
[6]In wt+gal vs. wt-gal, approx. 15 genes change in protein but not mRNA abundance (Fig. 3) These genes are regulated at the level of protein translation or degradationCompare global translation state of proteins between + vs. - gal, using method of [Q. Zong et al. Proc Natl Acad Sci U.S.A.96, 10632-6. (1999)]
[7]In wt+gal vs. wt-gal, most ribosomal subunits increase in mRNA but not protein abundance (Fig. 3)
[8]Nine genes w/ predicted Gal4p-binding sites have EPs that are similar to those of known GAL genesdag Gal4p regulates transcription of these genes via protein-DNA interactions Verify predicted interactions by global chromatin immuno-precipitation experiments [B. Ren et al., Science 290, 2306-9. (2000)]
dagPossible model refinements shown in Fig. 1.
ddagTests explored in Step (iv) of the paper.
sectionEP = Expression profile.