Dissecting the calcium-induced differentiation of human primary keratinocytes stem cells by integrative and structural network analyses.

The molecular details underlying the time-dependent assembly of protein complexes in cellular networks, such as those that occur during differentiation, are largely unexplored. Focusing on the calcium-induced differentiation of primary human keratinocytes as a model system for a major cellular reorganization process, we look at the expression of genes whose products are involved in manually-annotated protein complexes. Clustering analyses revealed only moderate co-expression of functionally related proteins during differentiation. However, when we looked at protein complexes, we found that the majority (55%) are composed of non-dynamic and dynamic gene products ('di-chromatic'), 19% are non-dynamic, and 26% only dynamic. Considering three-dimensional protein structures to predict steric interactions, we found that proteins encoded by dynamic genes frequently interact with a common non-dynamic protein in a mutually exclusive fashion. This suggests that during differentiation, complex assemblies may also change through variation in the abundance of proteins that compete for binding to common proteins as found in some cases for paralogous proteins. Considering the example of the TNF-α/NFκB signaling complex, we suggest that the same core complex can guide signals into diverse context-specific outputs by addition of time specific expressed subunits, while keeping other cellular functions constant. Thus, our analysis provides evidence that complex assembly with stable core components and competition could contribute to cell differentiation.

Human epidermal stem cell function is regulated by circadian oscillations.

Human skin copes with harmful environmental factors that are circadian in nature, yet how circadian rhythms modulate the function of human epidermal stem cells is mostly unknown. Here we show that in human epidermal stem cells and their differentiated counterparts, core clock genes peak in a successive and phased manner, establishing distinct temporal intervals during the 24 hr day period. Each of these successive clock waves is associated with a peak in the expression of subsets of transcripts that temporally segregate the predisposition of epidermal stem cells to respond to cues that regulate their proliferation or differentiation, such as TGFß and calcium. Accordingly, circadian arrhythmia profoundly affects stem cell function in culture and in vivo. We hypothesize that this intricate mechanism ensures homeostasis by providing epidermal stem cells with environmentally relevant temporal functional cues during the course of the day and that its perturbation may contribute to aging and carcinogenesis.

Putting genetic interactions in context through a global modular decomposition.

Genetic interactions provide a powerful perspective into gene function, but our knowledge of the specific mechanisms that give rise to these interactions is still relatively limited. The availability of a global genetic interaction map in Saccharomyces cerevisiae, covering ∼30% of all possible double mutant combinations, provides an unprecedented opportunity for an unbiased assessment of the native structure within genetic interaction networks and how it relates to gene function and modular organization. Toward this end, we developed a data mining approach to exhaustively discover all block structures within this network, which allowed for its complete modular decomposition. The resulting modular structures revealed the importance of the context of individual genetic interactions in their interpretation and revealed distinct trends among genetic interaction hubs as well as insights into the evolution of duplicate genes. Block membership also revealed a surprising degree of multifunctionality across the yeast genome and enabled a novel association of VIP1 and IPK1 with DNA replication and repair, which is supported by experimental evidence. Our modular decomposition also provided a basis for testing the between-pathway model of negative genetic interactions and within-pathway model of positive genetic interactions. While we find that most modular structures involving negative genetic interactions fit the between-pathway model, we found that current models for positive genetic interactions fail to explain 80% of the modular structures detected. We also find differences between the modular structures of essential and nonessential genes.

Quantitative analysis of fitness and genetic interactions in yeast on a genome scale.

Global quantitative analysis of genetic interactions is a powerful approach for deciphering the roles of genes and mapping functional relationships among pathways. Using colony size as a proxy for fitness, we developed a method for measuring fitness-based genetic interactions from high-density arrays of yeast double mutants generated by synthetic genetic array (SGA) analysis. We identified several experimental sources of systematic variation and developed normalization strategies to obtain accurate single- and double-mutant fitness measurements, which rival the accuracy of other high-resolution studies. We applied the SGA score to examine the relationship between physical and genetic interaction networks, and we found that positive genetic interactions connect across functionally distinct protein complexes revealing a network of genetic suppression among loss-of-function alleles.

The genetic landscape of a cell.

A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for approximately 75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.

DRYGIN: a database of quantitative genetic interaction networks in yeast.

Genetic interactions are highly informative for deciphering the underlying functional principles that govern how genes control cell processes. Recent developments in Synthetic Genetic Array (SGA) analysis enable the mapping of quantitative genetic interactions on a genome-wide scale. To facilitate access to this resource, which will ultimately represent a complete genetic interaction network for a eukaryotic cell, we developed DRYGIN (Data Repository of Yeast Genetic Interactions)-a web database system that aims at providing a central platform for yeast genetic network analysis and visualization. In addition to providing an interface for searching the SGA genetic interactions, DRYGIN also integrates other data sources, in order to associate the genetic interactions with pathway information, protein complexes, other binary genetic and physical interactions, and Gene Ontology functional annotation. DRYGIN version 1.0 currently holds more than 5.4 million measurements of genetic interacting pairs involving approximately 4500 genes, and is available at http://drygin.ccbr.utoronto.ca.

Significant conservation of synthetic lethal genetic interaction networks between distantly related eukaryotes.

Synthetic lethal genetic interaction networks define genes that work together to control essential functions and have been studied extensively in Saccharomyces cerevisiae using the synthetic genetic array (SGA) analysis technique (ScSGA). The extent to which synthetic lethal or other genetic interaction networks are conserved between species remains uncertain. To address this question, we compared literature-curated and experimentally derived genetic interaction networks for two distantly related yeasts, Schizosaccharomyces pombe and S. cerevisiae. We find that 23% of interactions in a novel, high-quality S. pombe literature-curated network are conserved in the existing S. cerevisiae network. Next, we developed a method, called S. pombe SGA analysis (SpSGA), enabling rapid, high-throughput isolation of genetic interactions in this species. Direct comparison by SpSGA and ScSGA of approximately 220 genes involved in DNA replication, the DNA damage response, chromatin remodeling, intracellular transport, and other processes revealed that approximately 29% of genetic interactions are common to both species, with the remainder exhibiting unique, species-specific patterns of genetic connectivity. We define a conserved yeast network (CYN) composed of 106 genes and 144 interactions and suggest that this network may help understand the shared biology of diverse eukaryotic species.

The Botany Array Resource: e-Northerns, Expression Angling, and promoter analyses.

The Botany Array Resource provides the means for obtaining and archiving microarray data for Arabidopsis thaliana as well as biologist-friendly tools for viewing and mining both our own and other's data, for example, from the AtGenExpress Consortium. All the data produced are publicly available through the web interface of the database at http://bbc.botany.utoronto.ca. The database has been designed in accordance with the Minimum Information About a Microarray Experiment convention -- all expression data are associated with the corresponding experimental details. The database is searchable and it also provides a set of useful and easy-to-use web-based data-mining tools for researchers with sophisticated yet understandable output graphics. These include Expression Browser for performing 'electronic Northerns', Expression Angler for identifying genes that are co-regulated with a gene of interest, and Promomer for identifying potential cis-elements in the promoters of individual or co-regulated genes.