A role for APETALA1/fruitfull transcription factors in tomato leaf development.

Flexible maturation rates underlie part of the diversity of leaf shape, and tomato (Solanum lycopersicum) leaves are compound due to prolonged organogenic activity of the leaf margin. The CINCINNATA-teosinte branched1, cycloidea, PCF (CIN-TCP) transcription factor lanceolate (LA) restricts this organogenic activity and promotes maturation. Here, we show that tomato APETALA1/fruitfull (AP1/FUL) MADS box genes are involved in tomato leaf development and are repressed by LA. AP1/FUL expression is correlated negatively with LA activity and positively with the organogenic activity of the leaf margin. LA binds to the promoters of the AP1/FUL genes MBP20 and TM4. Overexpression of MBP20 suppressed the simple-leaf phenotype resulting from upregulation of LA activity or from downregulation of class I knotted like homeobox (KNOXI) activity. Overexpression of a dominant-negative form of MBP20 led to leaf simplification and partly suppressed the increased leaf complexity of plants with reduced LA activity or increased KNOXI activity. Tomato plants overexpressing miR319, a negative regulator of several CIN-TCP genes including LA, flower with fewer leaves via an SFT-dependent pathway, suggesting that miR319-sensitive CIN-TCPs delay flowering in tomato. These results identify a role for AP1/FUL genes in vegetative development and show that leaf and plant maturation are regulated via partially independent mechanisms.

Hierarchy in somatic mutations arising during genomic evolution and progression of follicular lymphoma.

Follicular lymphoma (FL) is currently incurable using conventional chemotherapy or immunotherapy regimes, compelling new strategies. Advances in high-throughput sequencing technologies that can reveal oncogenic pathways have stimulated interest in tailoring therapies toward actionable somatic mutations. However, for mutation-directed therapies to be most effective, the mutations must be uniformly present in evolved tumor cells as well as in the self-renewing tumor-cell precursors. Here, we show striking intratumoral clonal diversity within FL tumors in the representation of mutations in the majority of genes as revealed by whole exome sequencing of subpopulations. This diversity captures a clonal hierarchy, resolved using immunoglobulin somatic mutations and IGH-BCL2 translocations as a frame of reference and by comparing diagnosis and relapse tumor pairs, allowing us to distinguish early versus late genetic eventsduring lymphomagenesis. We provide evidence that IGH-BCL2 translocations and CREBBP mutations are early events, whereas MLL2 and TNFRSF14 mutations probably represent late events during disease evolution. These observations provide insight into which of the genetic lesions represent suitable candidates for targeted therapies.

A cross-sample statistical model for SNP detection in short-read sequencing data.

Highly multiplex DNA sequencers have greatly expanded our ability to survey human genomes for previously unknown single nucleotide polymorphisms (SNPs). However, sequencing and mapping errors, though rare, contribute substantially to the number of false discoveries in current SNP callers. We demonstrate that we can significantly reduce the number of false positive SNP calls by pooling information across samples. Although many studies prepare and sequence multiple samples with the same protocol, most existing SNP callers ignore cross-sample information. In contrast, we propose an empirical Bayes method that uses cross-sample information to learn the error properties of the data. This error information lets us call SNPs with a lower false discovery rate than existing methods.

Interspecies comparison of prostate cancer gene-expression profiles reveals genes associated with aggressive tumors.

Prostate cancer (PC) is a heterogeneous disease whose aggressive phenotype is the second leading cause of cancer-related death in men. The identification of key molecules and pathways that play a pivotal role in PC progression towards an aggressive form is crucial. A major effort towards this end has been taken by global analyses of gene expression profiles. However, the large body of data did not provide a definitive idea about the genes which are associated with the aggressive growth of PC. In order to identify such genes, we performed an interspecies comparison between several human data sets and high quality microarray data that we generated from the transgenic adenocarcinoma of mouse prostate (TRAMP) strain. The TRAMP PC mimics the histological and pathological appearance as well as the aggressive phenotype of human PC (huPC). Analysis of the microarray data, derived from microdissected TRAMP specimens removed at different stages of the disease yielded genetic signatures delineating the TRAMP PC development and progression. Comparison of the TRAMP data with a set of genes representing the core expression signature of huPC yielded a limited set genes. Some of these genes are known predictors of poor prognosis in huPC. Interestingly, the modulation of genes responsible for the invasive phenotype of huPC occurs in TRAMP already during the transition to prostate intraepithelial neoplasia (PIN) and onwards to localized tumors. We therefore suggest that critical oncogenic events leading to an aggressive phenotype of huPC can be studied in the PIN stage of TRAMP.

Outcome signature genes in breast cancer: is there a unique set?

MOTIVATION: Predicting the metastatic potential of primary malignant tissues has direct bearing on the choice of therapy. Several microarray studies yielded gene sets whose expression profiles successfully predicted survival. Nevertheless, the overlap between these gene sets is almost zero. Such small overlaps were observed also in other complex diseases, and the variables that could account for the differences had evoked a wide interest. One of the main open questions in this context is whether the disparity can be attributed only to trivial reasons such as different technologies, different patients and different types of analyses. RESULTS: To answer this question, we concentrated on a single breast cancer dataset, and analyzed it by a single method, the one which was used by van't Veer et al. to produce a set of outcome-predictive genes. We showed that, in fact, the resulting set of genes is not unique; it is strongly influenced by the subset of patients used for gene selection. Many equally predictive lists could have been produced from the same analysis. Three main properties of the data explain this sensitivity: (1) many genes are correlated with survival; (2) the differences between these correlations are small; (3) the correlations fluctuate strongly when measured over different subsets of patients. A possible biological explanation for these properties is discussed. CONTACT: eytan.domany@weizmann.ac.il SUPPLEMENTARY INFORMATION: http://www.weizmann.ac.il/physics/complex/compphys/downloads/liate/

Coupled two-way clustering analysis of breast cancer and colon cancer gene expression data.

UNLABELLED: We present and review coupled two-way clustering, a method designed to mine gene expression data. The method identifies submatrices of the total expression matrix, whose clustering analysis reveals partitions of samples (and genes) into biologically relevant classes. We demonstrate, on data from colon and breast cancer, that we are able to identify partitions that elude standard clustering analysis. AVAILABILITY: Free, at http://ctwc.weizmann.ac.il.. SUPPLEMENTARY INFORMATION: http://www.weizmann.ac.il/physics/complex/compphys/bioinfo2/

Identification of direct p73 target genes combining DNA microarray and chromatin immunoprecipitation analyses.

The newly discovered p53 family member, p73, has a striking homology to p53 in both sequence and modular structure. Ectopic expression of p73 promotes transcription of p53 target genes and recapitulates the most characterized p53 biological effects such as growth arrest, apoptosis, and differentiation. Unlike p53-deficient mice that develop normally but are subject to spontaneous tumor formation, p73-deficient mice exhibit severe defects in the development of central nervous system and suffer from inflammation but are not prone to tumor development. These phenotypes suggest different biological activities mediated by p53 and p73 that might reflect activation of specific sets of target genes. Here, we have analyzed the gene expression profile of H1299 cells after p73alpha or p53 activation using oligonucleotide microarrays capable of detecting approximately 11,000 mRNA species. Our results indicate that p73alpha and p53 activate both common and distinct groups of genes. We found 141 and 320 genes whose expression is modulated by p73alpha and p53, respectively. p73alpha up-regulates 85 genes, whereas p53 induces 153 genes, of which 27 are in common with p73alpha. Functional classification of these genes reveals that they are involved in many aspects of cell function ranging from cell cycle and apoptosis to DNA repair. Furthermore, we report that some of the up-regulated genes are directly activated by p73alpha or p53.