Integration of Murine and Human Studies for Mapping Periodontitis Susceptibility.

Periodontitis is one of the most common inflammatory human diseases with a strong genetic component. Due to the limited sample size of available periodontitis cohorts and the underlying trait heterogeneity, genome-wide association studies (GWASs) of chronic periodontitis (CP) have largely been unsuccessful in identifying common susceptibility factors. A combination of quantitative trait loci (QTL) mapping in mice with association studies in humans has the potential to discover novel risk loci. To this end, we assessed alveolar bone loss in response to experimental periodontal infection in 25 lines (286 mice) from the Collaborative Cross (CC) mouse population using micro-computed tomography (µCT) analysis. The orthologous human chromosomal regions of the significant QTL were analyzed for association using imputed genotype data (OmniExpress BeadChip arrays) derived from case-control samples of aggressive periodontitis (AgP; 896 cases, 7,104 controls) and chronic periodontitis (CP; 2,746 cases, 1,864 controls) of northwest European and European American descent, respectively. In the mouse genome, QTL mapping revealed 2 significant loci (-log P = 5.3; false discovery rate = 0.06) on chromosomes 1 ( Perio3) and 14 ( Perio4). The mapping resolution ranged from ~1.5 to 3 Mb. Perio3 overlaps with a previously reported QTL associated with residual bone volume in F2 cross and includes the murine gene Ccdc121. Its human orthologue showed previously a nominal significant association with CP in humans. Use of variation data from the genomes of the CC founder strains further refined the QTL and suggested 7 candidate genes ( CAPN8, DUSP23, PCDH17, SNORA17, PCDH9, LECT1, and LECT2). We found no evidence of association of these candidates with the human orthologues. In conclusion, the CC populations enabled mapping of confined QTL that confer susceptibility to alveolar bone loss in mice and larger human phenotype-genotype samples and additional expression data from gingival tissues are likely required to identify true positive signals.

EZH2 promotes a bi-lineage identity in basal-like breast cancer cells.

The mechanisms regulating breast cancer differentiation state are poorly understood. Of particular interest are molecular regulators controlling the highly aggressive and poorly differentiated traits of basal-like breast carcinomas. Here we show that the Polycomb factor EZH2 maintains the differentiation state of basal-like breast cancer cells, and promotes the expression of progenitor associated and basal-lineage genes. Specifically, EZH2 regulates the composition of basal-like breast cancer cell populations by promoting a 'bi-lineage' differentiation state, in which cells co-express basal- and luminal-lineage markers. We show that human basal-like breast cancers contain a subpopulation of bi-lineage cells, and that EZH2-deficient cells give rise to tumors with a decreased proportion of such cells. Bi-lineage cells express genes that are active in normal luminal progenitors, and possess increased colony-formation capacity, consistent with a primitive differentiation state. We found that GATA3, a driver of luminal differentiation, performs a function opposite to EZH2, acting to suppress bi-lineage identity and luminal-progenitor gene expression. GATA3 levels increase upon EZH2 silencing, mediating a decrease in bi-lineage cell numbers. Our findings reveal a novel role for EZH2 in controlling basal-like breast cancer differentiation state and intra-tumoral cell composition.

Reconstructing chain functions in genetic networks.

Deciphering the mechanisms that control gene expression in the cell is a fundamental question in molecular biology. This task is complicated by the large number of possible regulation relations in the cell, and the relatively small amount of available experimental data. Recently, a new class of regulation functions called chain functions was suggested. Many signal transduction pathways can be accurately modeled by chain functions, and the restriction to chain functions greatly reduces the vast search space of regulation relations. In this paper we study the computational problem of reconstructing a chain function using a minimum number of experiments, in each of which only few genes are perturbed. We give optimal reconstruction schemes for several scenarios and show their application in reconstructing the regulation of galactose utilization in yeast.

Scoring clustering solutions by their biological relevance.

MOTIVATION: A central step in the analysis of gene expression data is the identification of groups of genes that exhibit similar expression patterns. Clustering gene expression data into homogeneous groups was shown to be instrumental in functional annotation, tissue classification, regulatory motif identification, and other applications. Although there is a rich literature on clustering algorithms for gene expression analysis, very few works addressed the systematic comparison and evaluation of clustering results. Typically, different clustering algorithms yield different clustering solutions on the same data, and there is no agreed upon guideline for choosing among them. RESULTS: We developed a novel statistically based method for assessing a clustering solution according to prior biological knowledge. Our method can be used to compare different clustering solutions or to optimize the parameters of a clustering algorithm. The method is based on projecting vectors of biological attributes of the clustered elements onto the real line, such that the ratio of between-groups and within-group variance estimators is maximized. The projected data are then scored using a non-parametric analysis of variance test, and the score's confidence is evaluated. We validate our approach using simulated data and show that our scoring method outperforms several extant methods, including the separation to homogeneity ratio and the silhouette measure. We apply our method to evaluate results of several clustering methods on yeast cell-cycle gene expression data. AVAILABILITY: The software is available from the authors upon request.

Chain functions and scoring functions in genetic networks.

One of the grand challenges of system biology is to reconstruct the network of regulatory control among genes and proteins. High throughput data, particularly from expression experiments, may gradually make this possible in the future. Here we address two key ingredients in any such 'reverse engineering' effort: The choice of a biologically relevant, yet restricted, set of potential regulation functions, and the appropriate score to evaluate candidate regulatory relations. We propose a set of regulation functions which we call chain functions, and argue for their ubiquity in biological networks. We analyze their complexity and show that their number is exponentially smaller than all boolean functions of the same dimension. We define two new scores: one evaluating the fitness of a candidate set of regulators of a particular gene, and the other evaluating a candidate function. Both scores use established statistical methods. Finally, we test our methods on experimental gene expression data from the yeast galactose pathway. We show the utility of using chain functions and the improved inference using our scores in comparison to several extant scores. We demonstrate that the combined use of the two scores gives an extra advantage. We expect both chain functions and the new scores to be helpful in future attempts to infer regulatory networks.