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Biomedical subjects

Yuval Kluger

Publications and source records attributed to Yuval Kluger.

17 recordsLinked to original sources
PubMed2026

Sharp cell type boundaries emerge from coordinated morphogen signaling.

Classic models of the French flag problem depict sharp cell type boundaries emerging from threshold responses to morphogen gradients. How discrete cell type boundaries arise from morphogen signals that vary continuously across developing tissues remains incompletely understood. We use hair follicle dermal condensate (DC) formation to study a sharp developmental transition in which proliferative progenitors undergo cell cycle exit concurrent with molecular differentiation. Using genetic and genomic approaches, we show that Wnt and Hedgehog signaling coordinate separable cellular events during DC commitment. Elevated Wnt signaling promotes cell cycle exit through reduced chromatin binding of the Hedgehog mediator GLI3, while Hedgehog signaling induces differentiation genes in a Wnt-dependent manner and simultaneously elevates Wnt activity. When these responses coincide, differentiation and cell cycle exit occur together, limiting the duration and abundance of intermediate states and producing a sharp boundary. When they do not, intermediate states persist and expand, producing a graded boundary. Thus, a sharp boundary can emerge from a continuous transition that is compressed in time and space.

Hedgehog↗
PubMed2006

Characterizing disease states from topological properties of transcriptional regulatory networks.

BACKGROUND: High throughput gene expression experiments yield large amounts of data that can augment our understanding of disease processes, in addition to classifying samples. Here we present new paradigms of data Separation based on construction of transcriptional regulatory networks for normal and abnormal cells using sequence predictions, literature based data and gene expression studies. We analyzed expression datasets from a number of diseased and normal cells, including different types of acute leukemia, and breast cancer with variable clinical outcome. RESULTS: We constructed sample-specific regulatory networks to identify links between transcription factors (TFs) and regulated genes that differentiate between healthy and diseased states. This approach carries the advantage of identifying key transcription factor-gene pairs with differential activity between healthy and diseased states rather than merely using gene expression profiles, thus alluding to processes that may be involved in gene deregulation. We then generalized this approach by studying simultaneous changes in functionality of multiple regulatory links pointing to a regulated gene or emanating from one TF (or changes in gene centrality defined by its in-degree or out-degree measures, respectively). We found that samples can often be separated based on these measures of gene centrality more robustly than using individual links. We examined distributions of distances (the number of links needed to traverse the path between each pair of genes) in the transcriptional networks for gene subsets whose collective expression profiles could best separate each dataset into predefined groups. We found that genes that optimally classify samples are concentrated in neighborhoods in the gene regulatory networks. This suggests that genes that are deregulated in diseased states exhibit a remarkable degree of connectivity. CONCLUSION: Transcription factor-regulated gene links and centrality of genes on transcriptional networks can be used to differentiate between cell types. Transcriptional network blueprints can be used as a basis for further research into gene deregulation in diseased states.

Algorithms↗
PubMed2006

Unraveling condition specific gene transcriptional regulatory networks in Saccharomyces cerevisiae.

BACKGROUND: Gene expression and transcription factor (TF) binding data have been used to reveal gene transcriptional regulatory networks. Existing knowledge of gene regulation can be presented using gene connectivity networks. However, these composite connectivity networks do not specify the range of biological conditions of the activity of each link in the network. RESULTS: We present a novel method that utilizes the expression and binding patterns of the neighboring nodes of each link in existing experimentally-based, literature-derived gene transcriptional regulatory networks and extend them in silico using TF-gene binding motifs and a compendium of large expression data from Saccharomyces cerevisiae. Using this method, we predict several hundreds of new transcriptional regulatory TF-gene links, along with experimental conditions in which known and predicted links become active. This approach unravels new links in the yeast gene transcriptional regulatory network by utilizing the known transcriptional regulatory interactions, and is particularly useful for breaking down the composite transcriptional regulatory network to condition specific networks. CONCLUSION: Our methods can facilitate future binding experiments, as they can considerably help focus on the TFs that must be surveyed to understand gene regulation.(Supplemental material and the latest version of the MATLAB implementation of the United Signature Algorithm is available online at 1 or [see Additional files 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).

Algorithms↗
PubMed2005

Two types of precursor cells in a multipotential hematopoietic cell line.

The biochemistry of early stages of hematopoietic differentiation is difficult to study because only relatively small numbers of precursor cells are available. The murine EML cell line is a multipotential cell line that can be used to model some of these steps. We found that the lineage- EML precursor cells can be separated into two populations based on cell surface markers including CD34. Both populations contain similar levels of stem cell factor (SCF) receptor (c-Kit) but only the CD34+ population shows a growth response when treated with SCF. Conversely, the CD34- population will grow in the presence of the cytokine IL-3. The human beta-globin locus control region hypersensitive site 2 plays different roles on beta-globin transcription in the CD34+ and CD34- populations. The two populations are present in about equal amounts in culture, and the CD34+ population rapidly regenerates the mixed population when grown in the presence of SCF. We suggest that this system may mimic a normal developmental transition in hematopoiesis.

Animals↗
PubMed2005

Using a xenograft model of human breast cancer metastasis to find genes associated with clinically aggressive disease.

Metastasis is the primary cause of death from breast cancer. A xenograft model was used to identify genes potentially involved with metastasis, comparing expression in the poorly metastatic GI101A human breast cancer cell line and a highly metastatic variant, GILM2. cDNA microarray analyses of these isogenic variants were done using 16K Operon 70-mer oligonucleotide microarray slides. Differentially expressed genes were identified by ANOVA, and differences of > or =2.5-fold were found for 106 genes. Changes in protein or RNA expression were confirmed for 10 of 12 genes. Three markers, heat shock protein 70 (HSP-70), chemokine (C-X-C motif) ligand 1 (CXCL-1), and secreted leukocyte protease inhibitor (SLPI), were studied further with breast cancer tissue microarrays using a novel method of automated quantitative analysis. This uses cytokeratin to define pixels as breast cancer (tumor mask) within the tissue array spot and then measures intensity of marker expression using a cyanine 5-conjugated antibody within the mask. Scores were correlated with clinicopathologic variables. High HSP-70 expression and high nuclear CXCL-1 expression in primary tumors were both associated with decreased survival (P = 0.05 and 0.027, respectively). Expression of each marker was strongly associated with lymph node involvement (P = 0.0002, 0.008, 0.0012, and 0.012 for HSP-70, nuclear CXCL-1, cytoplasmic CXCL-1, and SLPI, respectively). Identification of genes associated with metastasis in experimental models may have clinical implications for the management of breast cancer, because some of these are associated with lymph node metastasis and survival and might be useful as prognostic markers or molecular targets for novel therapies.

Animals↗
PubMed2005

An initial blueprint for myogenic differentiation.

We have combined genome-wide transcription factor binding and expression profiling to assemble a regulatory network controlling the myogenic differentiation program in mammalian cells. We identified a cadre of overlapping and distinct targets of the key myogenic regulatory factors (MRFs)--MyoD and myogenin--and Myocyte Enhancer Factor 2 (MEF2). We discovered that MRFs and MEF2 regulate a remarkably extensive array of transcription factor genes that propagate and amplify the signals initiated by MRFs. We found that MRFs play an unexpectedly wide-ranging role in directing the assembly and usage of the neuromuscular junction. Interestingly, these factors also prepare myoblasts to respond to diverse types of stress. Computational analyses identified novel combinations of factors that, depending on the differentiation state, might collaborate with MRFs. Our studies suggest unanticipated biological insights into muscle development and highlight new directions for further studies of genes involved in muscle repair and responses to stress and damage.

Cell Differentiation↗
PubMed2004

A common set of gene regulatory networks links metabolism and growth inhibition.

Using genome-wide analysis of transcription factor occupancy, we investigated the mechanisms underlying three mammalian growth arrest pathways that require the pRB tumor suppressor family. We found that p130 and E2F4 cooperatively repress a common set of genes under each growth arrest condition and showed that growth arrest is achieved through repression of a core set of genes involved not only in cell cycle control but also mitochondrial biogenesis and metabolism. Motif-finding algorithms predicted the existence of nuclear respiratory factor-1 (NRF1) binding sites in E2F target promoters, and genome-wide factor binding analysis confirmed our predictions. We showed that NRF1, a factor known to regulate expression of genes involved in mitochondrial function, is a coregulator of a large number of E2F target genes. Our studies provide insights into E2F regulatory circuitry, suggest how factor occupancy can predict the expression signature of a given target gene, and reveal pathways deregulated in human tumors.

Amino Acid Motifs↗
PubMed2004

Lineage specificity of gene expression patterns.

The hematopoietic system offers many advantages as a model for understanding general aspects of lineage choice and specification. Using oligonucleotide microarrays, we compared gene expression patterns of multiple purified hematopoietic cell populations, including neutrophils, monocytes, macrophages, resting, centrocytic, and centroblastic B lymphocytes, dendritic cells, and hematopoietic stem cells. Some of these cells were studied under both resting and stimulated conditions. We studied the collective behavior of subsets of genes derived from the Biocarta database of functional pathways, hand-tuned groupings of genes into broad functional categories based on the Gene Ontology database, and the metabolic pathways in the Kyoto Encyclopedia of Genes and Genomes database. Principal component analysis revealed strikingly pervasive differences in relative levels of gene expression among cell lineages that involve most of the subsets examined. These results indicate that many processes in these cells behave differently in different lineages. Much of the variation among lineages was captured by the first few principal components. Principal components biplots were found to provide a convenient visual display of the contributions of the various genes within the subsets in lineage discrimination. Moreover, by applying tree-constructing methodologies borrowed from phylogenetics to the expression data from differentiated cells and stem cells, we reconstructed a tree of relationships that resembled the established hematopoietic program of lineage development. Thus, the mRNA expression data implicitly contained information about developmental relationships among cell types.

Cell Lineage↗
PubMed2004

A panorama of lineage-specific transcription in hematopoiesis.

The hematopoietic system consists of more than ten differentiated cell types, all of which are derived from a single type of hematopoietic stem cell. The accessibility and interest of this system have made it a model for understanding normal and abnormal differentiation of mammalian cells. Newer techniques have generated a mass of data that requires integrative approaches for analysis and interpretation. The traditional view of the differentiation program holds that a small number of regulators are involved in each stage of cell specification. However, this may not be the case. Recent analyses have shown that almost all substantial subsets of genes, including the set of broadly expressed transcription factors, are expressed in patterns that are unique for each lineage. Further, much of this difference between lineages can be captured in two-dimensional graphs. Understanding the biologic significance, mechanisms and constraints underlying these differences is a challenge for experimentalists and computational biologists alike.

Animals↗
PubMed2004

cDNA microarray analysis of invasive and tumorigenic phenotypes in a breast cancer model.

The fms oncogene encodes the macrophage colony-stimulating factor receptor (CSF1R), a transmembrane tyrosine kinase receptor, which is abnormally expressed in breast cancer. Transfection of wild-type CSF1R into HC11 mammary epithelial cells (HC11-CSF1R) renders the transfectants capable of in vitro local invasion and in vivo tumorigenesis. Transfection with CSF1R mutated to express phe at the tyr-721 autophosphorylation site (HC11-CSF1R-721) creates a phenotype that lacks metastastic competence but maintains local invasiveness. Conversely, HC11 cells transfected with CSF1R mutated at tyr-807 (HC11-CSF1R-807) retain their metastatic competence, but are not locally invasive. Our aims were to determine which genes were differentially expressed with transfection of HC11 with wild-type CSF1R, and to determine the effect of mutation at the autophosphorylation sites on gene expression, using 4.6 K cDNA microarrays. Complementary DNA from HC11, HC11-CSF1R-721 and HC11-CSF1R-807 were each hybridized together with HC11-CSF1R on individual arrays. A principal component spectral method combined with prenormalization procedures was used for sample clustering. Differentially expressed genes were identified by the analysis of variance. Confirmation by Northern blotting was performed for MAP kinase phosphatase-1, WDNM1 (extracellular proteinase inhibitor), Trop 2 (tumor-associated calcium signal transducer-2), procollagen type IV alpha, secretory leukoprotease inhibitor, prenylated snare protein Ykt6, ceruloplasmin and chaperonin 10. Many of these genes have not previously been associated with tumor invasion and metastasis. We have successfully identified genes that can be linked to the invasive phenotypes or to tumorigenesis. These genes provide a basis for further studies of metastatic progression and local invasiveness, and can be evaluated as therapeutic targets.

Animals↗
PubMed2003

Relationship between gene co-expression and probe localization on microarray slides.

BACKGROUND: Microarray technology allows simultaneous measurement of thousands of genes in a single experiment. This is a potentially useful tool for evaluating co-expression of genes and extraction of useful functional and chromosomal structural information about genes. RESULTS: In this work we studied the association between the co-expression of genes, their location on the chromosome and their location on the microarray slides by analyzing a number of eukaryotic expression datasets, derived from the S. cerevisiae, C. elegans, and D. melanogaster. We find that in several different yeast microarray experiments the distribution of the number of gene pairs with correlated expression profiles as a function of chromosomal spacing is peaked at short separations and has two superimposed periodicities. The longer periodicity has a spacing of 22 genes (approximately 42 Kb), and the shorter periodicity is 2 genes (approximately 4 Kb). CONCLUSION: The relative positioning of DNA probes on microarray slides and source plates introduces subtle but significant correlations between pairs of genes. Careful consideration of this spatial artifact is important for analysis of microarray expression data. It is particularly relevant to recent microarray analyses that suggest that co-expressed genes cluster along chromosomes or are spaced by multiples of a fixed number of genes along the chromosome.

Animals↗
PubMed2003

Gene expression in mature neutrophils: early responses to inflammatory stimuli.

Neutrophils provide an essential defense against bacterial and fungal infection and play a major role in tissue damage during inflammation. Using oligonucleotide microarrays, we have examined the time course of changes in gene expression induced by stimulation with live, opsonized Escherichia coli, soluble lipopolysaccharide, and the chemoattractant formyl-methionyl-leucyl-phenylalanine. The results indicate that activated neutrophils generate a broad and vigorous set of alterations in gene expression. The responses included changes in the levels of transcripts encoding 148 transcription factors and chromatin-remodeling genes and 95 regulators of protein synthesis or stability. Clustering analysis showed distinct temporal patterns with many rapid changes in gene expression within the first hour of exposure. In addition to the temporal clustering of genes, we also observed rather different profiles associated with each stimulus, suggesting that even a nonvirulent organism such as E. coli is able to play a dynamic role in shaping the inflammatory response. Principal component analysis of transcription factor genes demonstrated clear separation of the neutrophil-response clusters from those of resting and stimulated human monocytes. The present study indicates that combinatorial transcriptional regulation including alterations of chromatin structure may play a role in the rapid changes in gene expression that occur in these terminally differentiated cells.

Chromatin Assembly and Disassembly↗
PubMed2003

A Bayesian networks approach for predicting protein-protein interactions from genomic data.

We have developed an approach using Bayesian networks to predict protein-protein interactions genome-wide in yeast. Our method naturally weights and combines into reliable predictions genomic features only weakly associated with interaction (e.g., messenger RNAcoexpression, coessentiality, and colocalization). In addition to de novo predictions, it can integrate often noisy, experimental interaction data sets. We observe that at given levels of sensitivity, our predictions are more accurate than the existing high-throughput experimental data sets. We validate our predictions with TAP (tandem affinity purification) tagging experiments. Our analysis, which gives a comprehensive view of yeast interactions, is available at genecensus.org/intint.

Bayes Theorem↗
PubMed2003

Gene expression in human neutrophils during activation and priming by bacterial lipopolysaccharide.

Circulating neutrophils play a key role both in the systemic inflammatory response and in complications of bacterial infection such as septic shock and septic multiple organ dysfunction syndrome. We have analyzed gene expression patterns in human neutrophils stimulated by E. coli lipopolysaccharide (LPS), with or without prior exposure to LPS, using differential display and oligonucleotide chip techniques. We identified 307 genes that were activated or repressed after treatment with LPS at 10 ng/ml and 385 genes after LPS at 100 ng/ml, compared with untreated neutrophils. The two sets included many transcription factors, cytokines, chemokines, interleukins, and surface antigens, as well as members of the toll-like receptor, Rel/NF-kappaB, and immune mediator gene families. Time course analysis showed that the early and late neutrophil responses to LPS share some common mechanisms, but many changes in gene expression are transient or late to develop. Neutrophils also showed a priming response to LPS, in which 97 genes significantly changed expression on re-exposure to lower dose LPS and were analyzed by unsupervised hierarchical clustering. These findings indicate that the neutrophil is a transcriptionally active cell responsive to environmental stimuli and capable of a complex series of both early and late changes in gene expression. Supplementary material for this article can be found on the Journal of Cellular Biochemistry website (http://jws-edci.interscience.wiley.com:8998/jpages/0730-2312/suppmat/89/v89.page.html).

Cluster Analysis↗
PubMed2003

Spectral biclustering of microarray data: coclustering genes and conditions.

Global analyses of RNA expression levels are useful for classifying genes and overall phenotypes. Often these classification problems are linked, and one wants to find "marker genes" that are differentially expressed in particular sets of "conditions." We have developed a method that simultaneously clusters genes and conditions, finding distinctive "checkerboard" patterns in matrices of gene expression data, if they exist. In a cancer context, these checkerboards correspond to genes that are markedly up- or downregulated in patients with particular types of tumors. Our method, spectral biclustering, is based on the observation that checkerboard structures in matrices of expression data can be found in eigenvectors corresponding to characteristic expression patterns across genes or conditions. In addition, these eigenvectors can be readily identified by commonly used linear algebra approaches, in particular the singular value decomposition (SVD), coupled with closely integrated normalization steps. We present a number of variants of the approach, depending on whether the normalization over genes and conditions is done independently or in a coupled fashion. We then apply spectral biclustering to a selection of publicly available cancer expression data sets, and examine the degree to which the approach is able to identify checkerboard structures. Furthermore, we compare the performance of our biclustering methods against a number of reasonable benchmarks (e.g., direct application of SVD or normalized cuts to raw data).

Breast Neoplasms↗
PubMed2002

Genomic and proteomic analysis of the myeloid differentiation program: global analysis of gene expression during induced differentiation in the MPRO cell line.

We have used an approach using 2-dimensional gel electrophoresis with mass spectrometry analysis combined with oligonucleotide chip hybridization for a comprehensive and quantitative study of the temporal patterns of protein and mRNA expression during myeloid development in the MPRO murine cell line. This global analysis detected 123 known proteins and 29 "new" proteins out of 220 protein spots identified by tandem mass spectroscopy, including proteins in 12 functional categories such as transcription factors and cytokines. Bioinformatic analysis of these proteins revealed clusters with functional importance to myeloid differentiation. Previous analyses have found that for a substantial number of genes the absolute amount of protein in the cell is not strongly correlated to the amount of mRNA. These conclusions were based on simultaneous measurement of mRNA and protein at just a single time point. Here, however, we are able to investigate the relationship between mRNA and protein in terms of simultaneous changes in their levels over multiple time points. This is the first time such a relationship has been studied, and we find that it gives a much stronger correlation, consistent with the hypothesis that a substantial proportion of protein change is a consequence of changed mRNA levels, rather than posttranscriptional effects. Cycloheximide inhibition also showed that most of the proteins detected by gel electrophoresis were relatively stable. Specific investigation of transcription factor mRNA representation showed considerable similarity to those of mature human neutrophils and highlighted several transcription factors and other functional nuclear proteins whose mRNA levels change prominently during MPRO differentiation but which have not been investigated previously in the context of myeloid development. Data are available online at http://bioinfo.mbb.yale.edu/expression/myelopoiesis.

Animals↗