Genome-wide coexpression dynamics: Theory and application

High-throughput expression profiling enables the global study of gene activities. Genes with positively correlated expression profiles are likely to encode functionally related proteins. However, all biological processes are interlocked, and each protein may play multiple cellular roles. Thus the coexpression of any two functionally related genes may depend on the constantly varying, yet often-unknown cellular state. To initiate a systematic study on this issue, a theory of coexpression dynamics is presented. This theory is used to rationalize a strategy of conducting a genome-wide search for the most critical cellular players that may affect the coexpression pattern of any two genes. In one example, using a yeast data set, our method reveals how the enzymes associated with the urea cycle are expressed to ensure proper mass flow of the involved metabolites. The correlation between ARG2 and CAR2 is found to change from positive to negative as the expression level of CPA2 increases. This delicate interplay in correlation signifies a remarkable control on the influx and efflux of ornithine and reflects well the intrinsic cellular demand for arginine. In addition to the urea cycle, our examples include SCH9 and CYR1 (both implicated in a recent longevity study), cytochrome c1 (mitochondrial electron transport), calmodulin (main calcium-binding protein), PFK1 and PFK2 (glycolysis), and two genes, ECM1 and YNL101W, the functions of which are newly revealed. The complexity in computation is eased by a new result from mathematical statistics.

[1]  J. Dahlberg,et al.  Molecular biology. , 1977, Science.

[2]  C. Stein.  Estimation of the Mean of a Multivariate Normal Distribution , 1981 .

[3]  T. Cooper,et al.  Combinatorial regulation of the Saccharomyces cerevisiae CAR1 (arginase) promoter in response to multiple environmental signals , 1996, Molecular and cellular biology.

[4]  C. Finch,et al.  Genetics of aging. , 1997, Science.

[5]  E. Dubois,et al.  Integration of the multiple controls regulating the expression of the arginase gene CAR1 of Saccharomyces cerevisiae in response to different nitrogen signals: role of Gln3p, ArgRp-Mcm1p, and Ume6p , 1997, Molecular and General Genetics MGG.

[6]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[7]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[8]  M. Saraste.  Oxidative phosphorylation at the fin de siècle. , 1999, Science.

[9]  U. Alon,et al.  Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[10]  D. Eisenberg,et al.  A combined algorithm for genome-wide prediction of protein function , 1999, Nature.

[11]  J. Mesirov,et al.  Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[12]  P. Defossez,et al.  Requirement of NAD and SIR2 for life-span extension by calorie restriction in Saccharomyces cerevisiae. , 2000, Science.

[13]  D. Botstein,et al.  Singular value decomposition for genome-wide expression data processing and modeling. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[14]  Xin Chen,et al.  TRANSFAC: an integrated system for gene expression regulation , 2000, Nucleic Acids Res..

[15]  D Haussler,et al.  Knowledge-based analysis of microarray gene expression data by using support vector machines. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[16]  D. Botstein,et al.  A gene expression database for the molecular pharmacology of cancer , 2000, Nature Genetics.

[17]  M. Cyert,et al.  Genetic analysis of calmodulin and its targets in Saccharomyces cerevisiae. , 2001, Annual review of genetics.

[18]  T. Cooper,et al.  Gln3p Nuclear Localization and Interaction with Ure2p inSaccharomyces cerevisiae * , 2001, The Journal of Biological Chemistry.

[19]  V. Longo,et al.  Regulation of Longevity and Stress Resistance by Sch9 in Yeast , 2001, Science.

[20]  G Rennert,et al.  Organ-specific molecular classification of primary lung, colon, and ovarian adenocarcinomas using gene expression profiles. , 2001, The American journal of pathology.

[21]  M. Crabeel,et al.  A New Yeast Metabolon Involving at Least the Two First Enzymes of Arginine Biosynthesis , 2001, The Journal of Biological Chemistry.

[22]  E. Lander,et al.  Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[23]  P. Grandi,et al.  Identification of a 60S preribosomal particle that is closely linked to nuclear export. , 2001, Molecular cell.

[24]  S. McIntire,et al.  A Family of Yeast Proteins Mediating Bidirectional Vacuolar Amino Acid Transport* , 2001, The Journal of Biological Chemistry.

[25]  T. Furuchi,et al.  Two nuclear proteins, Cin5 and Ydr259c, confer resistance to cisplatin in Saccharomyces cerevisiae. , 2001, Molecular pharmacology.