Todd R. Golub

Bio: Todd R. Golub is an academic researcher from Harvard University. The author has contributed to research in topics: Cancer & Gene expression profiling. The author has an hindex of 164, co-authored 422 publications receiving 201457 citations. Previous affiliations of Todd R. Golub include Rush University Medical Center & Boston Children's Hospital.

MicroRNA-1 negatively regulates expression of the hypertrophy-associated calmodulin and Mef2a genes.

Abstract: Calcium signaling is a central regulator of cardiomyocyte growth and function. Calmodulin is a critical mediator of calcium signals. Because the amount of calmodulin within cardiomyocytes is limiting, the precise control of calmodulin expression is important for the regulation of calcium signaling. In this study, we show for the first time that calmodulin levels are regulated posttranscriptionally in heart failure. The cardiomyocyterestricted microRNA miR-1 inhibited the translation of calmodulin-encoding mRNAs via highly conserved target sites within their 3 untranslated regions. In keeping with its effect on calmodulin expression, miR-1 downregulated calcium-calmodulin signaling through calcineurin to NFAT. miR-1 also negatively regulated the expression of Mef2a and Gata4, key transcription factors that mediate calcium-dependent changes in gene expression. Consistent with the downregulation of these hypertrophy-associated genes, miR-1 attenuated cardiomyocyte hypertrophy in cultured neonatal rat cardiomyocytes and in the intact adult heart. Our data indicate that miR-1 regulates cardiomyocyte growth responses by negatively regulating the calcium signaling components calmodulin, Mef2a, and Gata4. In the face of biomechanical stress, the heart develops compensatory hypertrophy, the hallmark of which is an increase in the size of cardiomyocytes. Over time, these hypertrophic changes become maladaptive, resulting in decreased contractile performance, cardiomyocyte loss, and, ultimately, heart failure (16). These changes in cardiomyocyte phenotype are linked to pathological changes in cardiomyocyte gene expression (24). Calcium, in conjunction with the calcium-binding protein calmodulin (CaM), is a critical mediator of hypertrophy signaling. Ca/CaM activates the phosphatase calcineurin (CN), leading to nuclear accumulation and the activation of

Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies

Abstract: Using a genome-scale, lentivirally delivered shRNA library, we performed massively parallel pooled shRNA screens in 216 cancer cell lines to identify genes that are required for cell proliferation and/or viability. Cell line dependencies on 11,000 genes were interrogated by 5 shRNAs per gene. The proliferation effect of each shRNA in each cell line was assessed by transducing a population of 11M cells with one shRNA-virus per cell and determining the relative enrichment or depletion of each of the 54,000 shRNAs after 16 population doublings using Next Generation Sequencing. All the cell lines were screened using standardized conditions to best assess differential genetic dependencies across cell lines. When combined with genomic characterization of these cell lines, this dataset facilitates the linkage of genetic dependencies with specific cellular contexts (e.g., gene mutations or cell lineage). To enable such comparisons, we developed and provided a bioinformatics tool to identify linear and nonlinear correlations between these features.

Erratum: Addendum: The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity (Nature (2012) 483 7391 (603-607))

Abstract: Jordi Barretina, Giordano Caponigro, Nicolas Stransky, Kavitha Venkatesan, Adam A. Margolin, Sungjoon Kim, Christopher J. Wilson, Joseph Lehár, Gregory V. Kryukov, Dmitriy Sonkin, Anupama Reddy, Manway Liu, Lauren Murray, Michael F. Berger, John E. Monahan, Paula Morais, Jodi Meltzer, Adam Korejwa, Judit Jané-Valbuena, Felipa A. Mapa, Joseph Thibault, Eva Bric-Furlong, Pichai Raman, Aaron Shipway, Ingo H. Engels, Jill Cheng, Guoying K. Yu, Jianjun Yu, Peter Aspesi Jr, Melanie de Silva, Kalpana Jagtap, Michael D. Jones, Li Wang, Charles Hatton, Emanuele Palescandolo, Supriya Gupta, Scott Mahan, Carrie Sougnez, Robert C. Onofrio, Ted Liefeld, Laura MacConaill, Wendy Winckler, Michael Reich, Nanxin Li, Jill P. Mesirov, Stacey B. Gabriel, Gad Getz, Kristin Ardlie, Vivien Chan, Vic E. Myer, Barbara L. Weber, Jeff Porter, Markus Warmuth, Peter Finan, Jennifer L. Harris, Matthew Meyerson, Todd R. Golub, Michael P. Morrissey, William R. Sellers, Robert Schlegel & Levi A. Garraway

Oligomerization of the ABL tyrosine kinase by the Ets protein TEL in human leukemia.

Abstract: TEL is a member of the Ets family of transcription factors which are frequently rearranged in human leukemia. The mechanism of TEL-mediated transformation, however, is unknown. We report the cloning and characterization of a chromosomal translocation associated with acute myeloid leukemia which fuses TEL to the ABL tyrosine kinase. The TEL-ABL fusion confers growth factor-independent growth to the marine hematopoietic cell line Ba/F3 and transforms Rat-1 fibroblasts and primary murine bone marrow cells. TEL-ABL is constitutively tyrosine phosphorylated and localizes to the cytoskeleton. A TEL-ABL mutant containing an ABL kinase-inactivating mutation is not constitutively phosphorylated and is nontransforming but retains cytoskeletal localization. However, constitutive phosphorylation, cytoskeletal localization, and transformation are all dependent upon a highly conserved region of TEL termed the helix-loop-helix (HLH) domain. TEL-ABL formed HLH-dependent homo-oligomers in vitro, a process critical for tyrosine kinase activation. These experiments suggest that oligomerization of TEL-ABL mediated by the TEL HLH domain is required for tyrosine kinase activation, cytoskeletal localization, and transformation. These data also suggest that oligomerization of Ets proteins through the highly conserved HLH domain may represent a previously unrecognized phenomenon.

A remarkably simple genome underlies highly malignant pediatric rhabdoid cancers

Abstract: Cancer is principally considered a genetic disease, and numerous mutations are thought essential to drive its growth. However, the existence of genomically stable cancers and the emergence of mutations in genes that encode chromatin remodelers raise the possibility that perturbation of chromatin structure and epigenetic regulation are capable of driving cancer formation. Here we sequenced the exomes of 35 rhabdoid tumors, highly aggressive cancers of early childhood characterized by biallelic loss of SMARCB1, a subunit of the SWI/SNF chromatin remodeling complex. We identified an extremely low rate of mutation, with loss of SMARCB1 being essentially the sole recurrent event. Indeed, in 2 of the cancers there were no other identified mutations. Our results demonstrate that high mutation rates are dispensable for the genesis of cancers driven by mutation of a chromatin remodeling complex. Consequently, cancer can be a remarkably genetically simple disease.

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks

Abstract: Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

Abstract: DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.

limma powers differential expression analyses for RNA-sequencing and microarray studies

Abstract: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.