PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes

DNA microarrays can be used to identify gene expression changes characteristic of human disease. This is challenging, however, when relevant differences are subtle at the level of individual genes. We introduce an analytical strategy, Gene Set Enrichment Analysis, designed to detect modest but coordinate changes in the expression of groups of functionally related genes. Using this approach, we identify a set of genes involved in oxidative phosphorylation whose expression is coordinately decreased in human diabetic muscle. Expression of these genes is high at sites of insulin-mediated glucose disposal, activated by PGC-1alpha and correlated with total-body aerobic capacity. Our results associate this gene set with clinically important variation in human metabolism and illustrate the value of pathway relationships in the analysis of genomic profiling experiments.     

Systematic variation in gene expression patterns in human cancer cell lines

We used cDNA microarrays to explore the variation in expression of approximately 8,000 unique genes among the 60 cell lines used in the National Cancer Institute's screen for anti-cancer drugs. Classification of the cell lines based solely on the observed patterns of gene expression revealed a correspondence to the ostensible origins of the tumours from which the cell lines were derived. The consistent relationship between the gene expression patterns and the tissue of origin allowed us to recognize outliers whose previous classification appeared incorrect. Specific features of the gene expression patterns appeared to be related to physiological properties of the cell lines, such as their doubling time in culture, drug metabolism or the interferon response. Comparison of gene expression patterns in the cell lines to those observed in normal breast tissue or in breast tumour specimens revealed features of the expression patterns in the tumours that had recognizable counterparts in specific cell lines, reflecting the tumour, stromal and inflammatory components of the tumour tissue. These results provided a novel molecular characterization of this important group of human cell lines and their relationships to tumours in vivo.     

Biochemical networking contributes more to genetic buffering in human and mouse metabolic pathways than does gene duplication

During evolution different genes evolve at unequal rates, reflecting the varying functional constraints on phenotype. An important contributor to this variation is genetic buffering, which reduces the potential detrimental effects of mutations. We studied whether gene duplication and redundant metabolic networks affect genetic buffering by comparing the evolutionary rate of 242 human and mouse orthologous genes involved in metabolic pathways. A gene with a redundant network is defined as one for which the structural layout of metabolic pathways provides an alternative metabolic route that can, in principle, compensate for the loss of a protein function encoded by the gene. We found that genes with redundant networks evolve at similar rates as did genes without redundant networks, [corrected] but no significant difference was detected between single-copy genes and gene families. This implies that redundancy in metabolic networks provides significantly more genetic buffering than do gene families. We also found that genes encoding proteins involved in glycolysis and gluconeogenesis showed as a group a distinct pattern of variation, in contrast to genes involved in other pathways. These results suggest that redundant networks provide genetic buffering and contribute to the functional diversification of metabolic pathways.     

Common genetic variants account for differences in gene expression among ethnic groups

Variation in DNA sequence contributes to individual differences in quantitative traits, but in humans the specific sequence variants are known for very few traits. We characterized variation in gene expression in cells from individuals belonging to three major population groups. This quantitative phenotype differs significantly between European-derived and Asian-derived populations for 1,097 of 4,197 genes tested. For the phenotypes with the strongest evidence of cis determinants, most of the variation is due to allele frequency differences at cis-linked regulators. The results show that specific genetic variation among populations contributes appreciably to differences in gene expression phenotypes. Populations differ in prevalence of many complex genetic diseases, such as diabetes and cardiovascular disease. As some of these are probably influenced by the level of gene expression, our results suggest that allele frequency differences at regulatory polymorphisms also account for some population differences in prevalence of complex diseases.     

Population genomics of human gene expression

Genetic variation influences gene expression, and this variation in gene expression can be efficiently mapped to specific genomic regions and variants. Here we have used gene expression profiling of Epstein-Barr virus-transformed lymphoblastoid cell lines of all 270 individuals genotyped in the HapMap Consortium to elucidate the detailed features of genetic variation underlying gene expression variation. We find that gene expression is heritable and that differentiation between populations is in agreement with earlier small-scale studies. A detailed association analysis of over 2.2 million common SNPs per population (5% frequency in HapMap) with gene expression identified at least 1,348 genes with association signals in cis and at least 180 in trans. Replication in at least one independent population was achieved for 37% of cis signals and 15% of trans signals, respectively. Our results strongly support an abundance of cis-regulatory variation in the human genome. Detection of trans effects is limited but suggests that regulatory variation may be the key primary effect contributing to phenotypic variation in humans. We also explore several methodologies that improve the current state of analysis of gene expression variation.     

Treatment-specific changes in gene expression discriminate in vivo drug response in human leukemia cells

To elucidate the genomics of cellular responses to cancer treatment, we analyzed the expression of over 9,600 human genes in acute lymphoblastic leukemia cells before and after in vivo treatment with methotrexate and mercaptopurine given alone or in combination. Based on changes in gene expression, we identified 124 genes that accurately discriminated among the four treatments. Discriminating genes included those involved in apoptosis, mismatch repair, cell cycle control and stress response. Only 14% of genes that changed when these medications were given as single agents also changed when they were given together. These data indicate that lymphoid leukemia cells of different molecular subtypes share common pathways of genomic response to the same treatment, that changes in gene expression are treatment-specific and that gene expression can illuminate differences in cellular response to drug combinations versus single agents.     

Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease

Integration of genome-wide expression profiling with linkage analysis is a new approach to identifying genes underlying complex traits. We applied this approach to the regulation of gene expression in the BXH/HXB panel of rat recombinant inbred strains, one of the largest available rodent recombinant inbred panels and a leading resource for genetic analysis of the highly prevalent metabolic syndrome. In two tissues important to the pathogenesis of the metabolic syndrome, we mapped cis- and trans-regulatory control elements for expression of thousands of genes across the genome. Many of the most highly linked expression quantitative trait loci are regulated in cis, are inherited essentially as monogenic traits and are good candidate genes for previously mapped physiological quantitative trait loci in the rat. By comparative mapping we generated a data set of 73 candidate genes for hypertension that merit testing in human populations. Mining of this publicly available data set is expected to lead to new insights into the genes and regulatory pathways underlying the extensive range of metabolic and cardiovascular disease phenotypes that segregate in these recombinant inbred strains.     

Digenic inheritance of severe insulin resistance in a human pedigree

Impaired insulin action is a key feature of type 2 diabetes and is also found, to a more extreme degree, in familial syndromes of insulin resistance. Although inherited susceptibility to insulin resistance may involve the interplay of several genetic loci, no clear examples of interactions among genes have yet been reported. Here we describe a family in which five individuals with severe insulin resistance, but no unaffected family members, were doubly [corrected] heterozygous with respect to frameshift/premature stop mutations in two unlinked genes, PPARG and PPP1R3A these encode peroxisome proliferator activated receptor gamma, which is highly expressed in adipocytes, and protein phosphatase 1, regulatory subunit 3, the muscle-specific regulatory subunit of protein phosphatase 1, which are centrally involved in the regulation of carbohydrate and lipid metabolism, respectively. That mutant molecules primarily involved in either carbohydrate or lipid metabolism can combine to produce a phenotype of extreme insulin resistance provides a model of interactions among genes that may underlie common human metabolic disorders such as type 2 diabetes.     

Detection of regulatory variation in mouse genes

Functional polymorphism in genes can be classified as coding variation, altering the amino-acid sequence of the encoded protein, or regulatory variation, affecting the level or pattern of expression of the gene. Coding variation can be recognized directly from DNA sequence, and consequently its frequency and characteristics have been extensively described. By contrast, virtually nothing is known about the extent to which gene regulation varies in populations. Yet it is likely that regulatory variants are important in modulating gene function: alterations in gene regulation have been proposed to influence disease susceptibility and to have been the primary substrate for the evolution of species. Here, we report a systematic study to assess the extent of cis-acting regulatory variation in 69 genes across four inbred mouse strains. We find that at least four of these genes show allelic differences in expression level of 1.5-fold or greater, and that some of these differences are tissue specific. The results show that the impact of regulatory variants can be detected at a significant frequency in a genomic survey and suggest that such variation may have important consequences for organismal phenotype and evolution. The results indicate that larger-scale surveys in both mouse and human could identify a substantial number of genes with common regulatory variation.     

Regulatory changes underlying expression differences within and between Drosophila species

Differences in gene expression are an important source of phenotypic variation, and can be caused by changes in cis and/or trans regulation. cis-regulatory variants alter allele-specific expression, whereas trans-regulatory variants influence expression of both alleles in a diploid cell. Because of this difference, we hypothesize that natural selection may favor one type of change over the other. Here, we investigate contributions of cis- and trans-regulatory changes to variable intra- and interspecific gene expression using four strains of Drosophila melanogaster, three strains of D. simulans and a total of 78 genes. We show that cis-regulatory changes account for a greater proportion of the expression differences observed between rather than within species. These data are inconsistent with a neutral model assuming equal probabilities of fixation for cis- and trans-regulatory polymorphisms, suggesting that natural selection influences the molecular mechanisms underlying divergent gene expression. Specifically, cis-regulatory changes seem to accumulate preferentially over time.