Deregulated expression of c-Myc depletes epidermal stem cells

The beta-catenin/TCF signaling pathway is essential for the maintenance of epithelial stem cells in the small intestine. c-Myc a downstream target of beta-catenin/TCF (ref. 2), can induce differentiation of epidermal stem cells in vitro. To determine the role of c-Myc in epidermal stem cells in vivo, we have targeted expression of human MYC2 to the hair follicles and the basal layer of mouse epidermis using a keratin 14 vector (K14.MYC2). Adult K14.MYC2 mice gradually lose their hair and develop spontaneous ulcerated lesions due to a severe impairment in wound healing; their keratinocytes show impaired migration in response to wounding. The expression of beta1 integrin, which is preferentially expressed in epidermal stem cells is unusually low in the epidermis of K14.MYC2 mice. Label-retaining analysis to identify epidermal stem cells reveals a 75% reduction in the number of stem cells in 3-month-old K14.MYC2 mice, compared with wildtype mice. We conclude that deregulated expression of c-Myc in stem cells reduces beta1 integrin expression, which is essential to both keratinocyte migration and stem cell maintenance.     

Adaptive evolution of bacterial metabolic networks by horizontal gene transfer

Numerous studies have considered the emergence of metabolic pathways, but the modes of recent evolution of metabolic networks are poorly understood. Here, we integrate comparative genomics with flux balance analysis to examine (i) the contribution of different genetic mechanisms to network growth in bacteria, (ii) the selective forces driving network evolution and (iii) the integration of new nodes into the network. Most changes to the metabolic network of Escherichia coli in the past 100 million years are due to horizontal gene transfer, with little contribution from gene duplicates. Networks grow by acquiring genes involved in the transport and catalysis of external nutrients, driven by adaptations to changing environments. Accordingly, horizontally transferred genes are integrated at the periphery of the network, whereas central parts remain evolutionarily stable. Genes encoding physiologically coupled reactions are often transferred together, frequently in operons. Thus, bacterial metabolic networks evolve by direct uptake of peripheral reactions in response to changed environments.     

Network modeling links breast cancer susceptibility and centrosome dysfunction

Many cancer-associated genes remain to be identified to clarify the underlying molecular mechanisms of cancer susceptibility and progression. Better understanding is also required of how mutations in cancer genes affect their products in the context of complex cellular networks. Here we have used a network modeling strategy to identify genes potentially associated with higher risk of breast cancer. Starting with four known genes encoding tumor suppressors of breast cancer, we combined gene expression profiling with functional genomic and proteomic (or 'omic') data from various species to generate a network containing 118 genes linked by 866 potential functional associations. This network shows higher connectivity than expected by chance, suggesting that its components function in biologically related pathways. One of the components of the network is HMMR, encoding a centrosome subunit, for which we demonstrate previously unknown functional associations with the breast cancer-associated gene BRCA1. Two case-control studies of incident breast cancer indicate that the HMMR locus is associated with higher risk of breast cancer in humans. Our network modeling strategy should be useful for the discovery of additional cancer-associated genes.     

Modular epistasis in yeast metabolism

Epistatic interactions, manifested in the effects of mutations on the phenotypes caused by other mutations, may help uncover the functional organization of complex biological networks. Here, we studied system-level epistatic interactions by computing growth phenotypes of all single and double knockouts of 890 metabolic genes in Saccharomyces cerevisiae, using the framework of flux balance analysis. A new scale for epistasis identified a distinctive trimodal distribution of these epistatic effects, allowing gene pairs to be classified as buffering, aggravating or noninteracting. We found that the ensuing epistatic interaction network could be organized hierarchically into function-enriched modules that interact with each other 'monochromatically' (i.e., with purely aggravating or purely buffering epistatic links). This property extends the concept of epistasis from single genes to functional units and provides a new definition of biological modularity, which emphasizes interactions between, rather than within, functional modules. Our approach can be used to infer functional gene modules from purely phenotypic epistasis measurements.     

Exome sequencing identifies MAX mutations as a cause of hereditary pheochromocytoma

Hereditary pheochromocytoma (PCC) is often caused by germline mutations in one of nine susceptibility genes described to date, but there are familial cases without mutations in these known genes. We sequenced the exomes of three unrelated individuals with hereditary PCC (cases) and identified mutations in MAX, the MYC associated factor X gene. Absence of MAX protein in the tumors and loss of heterozygosity caused by uniparental disomy supported the involvement of MAX alterations in the disease. A follow-up study of a selected series of 59 cases with PCC identified five additional MAX mutations and suggested an association with malignant outcome and preferential paternal transmission of MAX mutations. The involvement of the MYC-MAX-MXD1 network in the development and progression of neural crest cell tumors is further supported by the lack of functional MAX in rat PCC (PC12) cells and by the amplification of MYCN in neuroblastoma and suggests that loss of MAX function is correlated with metastatic potential.     

Identification of renal Cd36 as a determinant of blood pressure and risk for hypertension

To identify renally expressed genes that influence risk for hypertension, we integrated expression quantitative trait locus (QTL) analysis of the kidney with genome-wide correlation analysis of renal expression profiles and blood pressure in recombinant inbred strains derived from the spontaneously hypertensive rat (SHR). This strategy, together with renal transplantation studies in SHR progenitor, transgenic and congenic strains, identified deficient renal expression of Cd36 encoding fatty acid translocase as a genetically determined risk factor for spontaneous hypertension.     

Modularity and evolutionary constraint on proteins

Modularity, which has been found in the functional and physical protein interaction networks of many organisms, has been postulated to affect both the mode and tempo of evolution. Here I show that in the yeast Saccharomyces cerevisiae, protein interaction hubs situated in single modules are highly constrained, whereas those connecting different modules are more plastic. This pattern of change could reflect a tendency for evolutionary innovations to occur by altering the proteins and interactions between rather than within modules, in a manner somewhat similar to the evolution of new proteins through the shuffling of conserved protein domains.     

Epistasis analysis with global transcriptional phenotypes

Classical epistasis analysis can determine the order of function of genes in pathways using morphological, biochemical and other phenotypes. It requires knowledge of the pathway's phenotypic output and a variety of experimental expertise and so is unsuitable for genome-scale analysis. Here we used microarray profiles of mutants as phenotypes for epistasis analysis. Considering genes that regulate activity of protein kinase A in Dictyostelium, we identified known and unknown epistatic relationships and reconstructed a genetic network with microarray phenotypes alone. This work shows that microarray data can provide a uniform, quantitative tool for large-scale genetic network analysis.     

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.     

Widespread aneuploidy revealed by DNA microarray expression profiling

Expression profiling using DNA microarrays holds great promise for a variety of research applications, including the systematic characterization of genes discovered by sequencing projects. To demonstrate the general usefulness of this approach, we recently obtained expression profiles for nearly 300 Saccharomyces cerevisiae deletion mutants. Approximately 8% of the mutants profiled exhibited chromosome-wide expression biases, leading to spurious correlations among profiles. Competitive hybridization of genomic DNA from the mutant strains and their isogenic parental wild-type strains showed they were aneuploid for whole chromosomes or chromosomal segments. Expression profile data published by several other laboratories also suggest the use of aneuploid strains. In five separate cases, the extra chromosome harboured a close homologue of the deleted gene; in two cases, a clear growth advantage for cells acquiring the extra chromosome was demonstrated. Our results have implications for interpreting whole-genome expression data, particularly from cells known to suffer genomic instability, such as malignant or immortalized cells.