Assif Yitzhaky

Automated long-term tracking and social behavioural phenotyping of animal colonies within a semi-natural environment

Social behaviour has a key role in animal survival across species, ranging from insects to primates and humans. However, the biological mechanisms driving natural interactions between multiple animals, over long-term periods, are poorly studied and remain elusive. Rigorous and objective quantification of behavioural parameters within a group poses a major challenge as it requires simultaneous monitoring of the positions of several individuals and comprehensive consideration of many complex factors. Automatic tracking and phenotyping of interacting animals could thus overcome the limitations of manual tracking methods. Here we report a broadly applicable system that automatically tracks the locations of multiple, uniquely identified animals, such as mice, within a semi-natural setting. The system combines video and radio frequency identified tracking data to obtain detailed behavioural profiles of both individuals and groups. We demonstrate the usefulness of these data in characterizing individual phenotypes, interactions between pairs and the collective social organization of groups.

CD133‐Positive Hematopoietic Stem Cell “Stemness” Genes Contain Many Genes Mutated or Abnormally Expressed in Leukemia

Affymetrix human Hu133A oligonucleotide arrays were used to study the expression profile of CD133+ cord blood (CB) and peripheral blood (PB) using CD133 cell‐surface marker. An unsupervised hierarchical clustering of 14,025 valid probe sets showed a clear distinction between the CD133+ cells representing the hematopoietic stem cell (HSC) population and CD133‐differentiated cells. Two hundred forty‐four genes were found to be upregulated by at least twofold in the CD133‐positive cells of both CB and PB compared with the CD133‐negative cells. These genes represent the hematopoietic “stemness,” whereas the 218 and 304 upregulated genes exclusively in PB and CB, respectively, represent tissue specificity. Some of the stemness genes were also common to HSC genes found to be upregulated in several recently published studies. Among these common stemness genes, we identified several groups of genes that have an important role in hematopoiesis: growth factor receptors, transcription factors, genes that have an important role in development, and genes involved in cell growth. Sixteen selected stemness genes are known to be mutated or abnormally regulated in acute leukemias. It can be suggested that key hematopoietic stemness machinery genes may lead to abnormal proliferation and leukemia upon mutation or change of their expression.

The promoters of human cell cycle genes integrate signals from two tumor suppressive pathways during cellular transformation.

Deciphering regulatory events that drive malignant transformation represents a major challenge for systems biology. Here, we analyzed genome‐wide transcription profiling of an in vitro cancerous transformation process. We focused on a cluster of genes whose expression levels increased as a function of p53 and p16INK4A tumor suppressors inactivation. This cluster predominantly consists of cell cycle genes and constitutes a signature of a diversity of cancers. By linking expression profiles of the genes in the cluster with the dynamic behavior of p53 and p16INK4A, we identified a promoter architecture that integrates signals from the two tumor suppressive channels and that maps their activity onto distinct levels of expression of the cell cycle genes, which, in turn, correspond to different cellular proliferation rates. Taking components of the mitotic spindle as an example, we experimentally verified our predictions that p53‐mediated transcriptional repression of several of these novel targets is dependent on the activities of p21, NFY, and E2F. Our study demonstrates how a well‐controlled transformation process allows linking between gene expression, promoter architecture, and activity of upstream signaling molecules.

Gene expression analysis reveals a strong signature of an interferon-induced pathway in childhood lymphoblastic leukemia as well as in breast and ovarian cancer

On the basis of epidemiological studies, infection was suggested to play a role in the etiology of human cancer. While for some cancers such a role was indeed demonstrated, there is no direct biological support for the role of viral pathogens in the pathogenesis of childhood leukemia. Using a novel bioinformatic tool that alternates between clustering and standard statistical methods of analysis, we performed a ‘double-blind’ search of published gene expression data of subjects with different childhood acute lymphoblastic leukemia (ALL) subtypes, looking for unanticipated partitions of patients, induced by unexpected groups of genes with correlated expression. We discovered a group of about 30 genes, related to the interferon response pathway, whose expression levels divide the ALL samples into two subgroups; high in 50, low in 285 patients. Leukemic subclasses prevalent in early childhood (the age most susceptible to infection) are over-represented in the high-expression subgroup. Similar partitions, induced by the same genes, were found also in breast and ovarian cancer but not in lung cancer, prostate cancer and lymphoma. About 40% of breast cancer samples expressed the ‘interferon-related’ signature. It is of interest that several studies demonstrated mouse mammary tumor virus-like sequences in about 40% of breast cancer samples. Our discovery of an unanticipated strong signature of an interferon-induced pathway provides molecular support for a role for either inflammation or viral infection in the pathogenesis of childhood leukemia as well as breast and ovarian cancer.

Wide-Scale Analysis of Human Functional Transcription Factor Binding Reveals a Strong Bias towards the Transcription Start Site

Background Transcription factors (TF) regulate expression by binding to specific DNA sequences. A binding event is functional when it affects gene expression. Functionality of a binding site is reflected in conservation of the binding sequence during evolution and in over represented binding in gene groups with coherent biological functions. Functionality is governed by several parameters such as the TF-DNA binding strength, distance of the binding site from the transcription start site (TSS), DNA packing, and more. Understanding how these parameters control functionality of different TFs in different biological contexts is a must for identifying functional TF binding sites and for understanding regulation of transcription. Methodology/Principal Findings We introduce a novel method to screen the promoters of a set of genes with shared biological function (obtained from the functional Gene Ontology (GO) classification) against a precompiled library of motifs, and find those motifs which are statistically over-represented in the gene set. More than 8000 human (and 23,000 mouse) genes, were assigned to one of 134 GO sets. Their promoters were searched (from 200 bp downstream to 1000 bp upstream the TSS) for 414 known DNA motifs. We optimized the sequence similarity score threshold, independently for every location window, taking into account nucleotide heterogeneity along the promoters of the target genes. The method, combined with binding sequence and location conservation between human and mouse, identifies with high probability functional binding sites for groups of functionally-related genes. We found many location-sensitive functional binding events and showed that they clustered close to the TSS. Our method and findings were tested experimentally. Conclusions/Significance We identified reliably functional TF binding sites. This is an essential step towards constructing regulatory networks. The promoter region proximal to the TSS is of central importance for regulation of transcription in human and mouse, just as it is in bacteria and yeast.

Context-specific microRNA analysis: identification of functional microRNAs and their mRNA targets

MicroRNAs (miRs) function primarily as post-transcriptional negative regulators of gene expression through binding to their mRNA targets. Reliable prediction of a miR’s targets is a considerable bioinformatic challenge of great importance for inferring the miR’s function. Sequence-based prediction algorithms have high false-positive rates, are not in agreement, and are not biological context specific. Here we introduce CoSMic (Context-Specific MicroRNA analysis), an algorithm that combines sequence-based prediction with miR and mRNA expression data. CoSMic differs from existing methods—it identifies miRs that play active roles in the specific biological system of interest and predicts with less false positives their functional targets. We applied CoSMic to search for miRs that regulate the migratory response of human mammary cells to epidermal growth factor (EGF) stimulation. Several such miRs, whose putative targets were significantly enriched by migration processes were identified. We tested three of these miRs experimentally, and showed that they indeed affected the migratory phenotype; we also tested three negative controls. In comparison to other algorithms CoSMic indeed filters out false positives and allows improved identification of context-specific targets. CoSMic can greatly facilitate miR research in general and, in particular, advance our understanding of individual miRs’ function in a specific context.

Cluster conservation as a novel tool for studying protein-protein interactions evolution.

Protein–protein interactions networks has come to be a buzzword associated with nets containing edges that represent a pair of interacting proteins (e.g. hormone‐receptor, enzyme‐inhibitor, antigen‐antibody, and a subset of multichain biological machines). Yet, each such interaction composes its own unique network, in which vertices represent amino acid residues, and edges represent atomic contacts. Recent studies have shown that analyses of the data encapsulated in these detailed networks may impact predictions of structure–function correlation. Here, we study homologous families of protein–protein interfaces, which share the same fold but vary in sequence. In this context, we address what properties of the network are shared among relatives with different sequences (and hence different atomic interactions) and which are not. Herein, we develop the general mathematical framework needed to compare the modularity of homologous networks. We then apply this analysis to the structural data of a few interface families, including hemoglobin α–β, growth hormone‐receptor, and Serine protease‐inhibitor. Our results suggest that interface modularity is an evolutionarily conserved property. Hence, protein–protein interfaces can be clustered down to a few modules, with the boundaries being evolutionarily conserved along homologous complexes. This suggests that protein engineering of protein–protein binding sites may be simplified by varying each module, but retaining the overall modularity of the interface. Proteins 2008.

Epidermal Growth-Factor – Induced Transcript Isoform Variation Drives Mammary Cell Migration

Signal-induced transcript isoform variation (TIV) includes alternative promoter usage as well as alternative splicing and alternative polyadenylation of mRNA. To assess the phenotypic relevance of signal-induced TIV, we employed exon arrays and breast epithelial cells, which migrate in response to the epidermal growth factor (EGF). We show that EGF rapidly – within one hour – induces widespread TIV in a significant fraction of the transcriptome. Importantly, TIV characterizes many genes that display no differential expression upon stimulus. In addition, similar EGF-dependent changes are shared by a panel of mammary cell lines. A functional screen, which utilized isoform-specific siRNA oligonucleotides, indicated that several isoforms play essential, non-redundant roles in EGF-induced mammary cell migration. Taken together, our findings highlight the importance of TIV in the rapid evolvement of a phenotypic response to extracellular signals.

Navigator‐3, a modulator of cell migration, may act as a suppressor of breast cancer progression

Dissemination of primary tumor cells depends on migratory and invasive attributes. Here, we identify Navigator‐3 (NAV3), a gene frequently mutated or deleted in human tumors, as a regulator of epithelial migration and invasion. Following induction by growth factors, NAV3 localizes to the plus ends of microtubules and enhances their polarized growth. Accordingly, NAV3 depletion trimmed microtubule growth, prolonged growth factor signaling, prevented apoptosis and enhanced random cell migration. Mathematical modeling suggested that NAV3‐depleted cells acquire an advantage in terms of the way they explore their environment. In animal models, silencing NAV3 increased metastasis, whereas ectopic expression of the wild‐type form, unlike expression of two, relatively unstable oncogenic mutants from human tumors, inhibited metastasis. Congruently, analyses of > 2,500 breast and lung cancer patients associated low NAV3 with shorter survival. We propose that NAV3 inhibits breast cancer progression by regulating microtubule dynamics, biasing directionally persistent rather than random migration, and inhibiting locomotion of initiated cells.