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.

Gamma Interferon Triggers Interaction between ICSBP (IRF-8) and TEL, Recruiting the Histone Deacetylase HDAC3 to the Interferon-Responsive Element

Abstract: ICSBP (IRF-8) is a transcription factor of the IRF family expressed only in the immune system. It is induced in macrophages by gamma interferon (IFN-γ) and contributes to macrophage functions. By interacting with Ets family protein PU.1, ICSBP binds to the IRF/Ets composite element and stimulates transcription. ICSBP binds to another DNA element, the IFN-stimulated response element (ISRE), a common target of the IRF family. Limited knowledge as to how ICSBP and other IRF proteins regulate ISRE-dependent transcription in IFN-γ-activated macrophages is available. By mass-spectrometric analysis of ISRE-bound proteins in macrophages, we identified TEL, another Ets member, as a factor recruited to the element in an IFN-γ-dependent manner. In vitro analysis with recombinant proteins indicated that this recruitment is due to a direct interaction between ICSBP and TEL, which is enhanced by the presence of ISRE. Significantly, the interaction with TEL in turn resulted in the recruitment of the histone deacetytase HDAC3 to the ISRE, causing increased repression of IFN-γ-mediated reporter activity through the ISRE. This repression may provide a negative-feedback mechanism operating after the initial transcriptional activation by IFN-γ. By associating with two different Ets family proteins, ICSBP exerts a dual function in IFN-γ-dependent gene regulation in an immune system-specific manner.

A high-throughput, multiplexed assay for superfamily-wide profiling of enzyme activity.

Abstract: The selectivity of an enzyme inhibitor is a key determinant of its usefulness as a tool compound or its safety as a drug. Yet selectivity is never assessed comprehensively in the early stages of the drug discovery process, and only rarely in the later stages, because technical limitations prohibit doing otherwise. Here, we report EnPlex, an efficient, high-throughput method for simultaneously assessing inhibitor potency and specificity, and pilot its application to 96 serine hydrolases. EnPlex analysis of widely used serine hydrolase inhibitors revealed numerous previously unrecognized off-target interactions, some of which may help to explain previously confounding adverse effects. In addition, EnPlex screening of a hydrolase-directed library of boronic acid- and nitrile-containing compounds provided structure-activity relationships in both potency and selectivity dimen - sions from which lead candidates could be more effectively prioritized. Follow-up of a series of dipeptidyl peptidase 4 inhibitors showed that EnPlex indeed predicted efficacy and safety in animal models. These results demonstrate the feasibility and value of high-throughput, superfamily-wide selectivity profiling and suggest that such profiling can be incorporated into the earliest stages of drug discovery.

Respiratory Failure Due to Differentiation Arrest and Expansion of Alveolar Cells following Lung-Specific Loss of the Transcription Factor C/EBPα in Mice

Abstract: The leucine zipper family transcription factor CCAAT enhancer binding protein alpha (C/EBPalpha) inhibits proliferation and promotes differentiation in various cell types. In this study, we show, using a lung-specific conditional mouse model of C/EBPalpha deletion, that loss of C/EBPalpha in the respiratory epithelium leads to respiratory failure at birth due to an arrest in the type II alveolar cell differentiation program. This differentiation arrest results in the lack of type I alveolar cells and differentiated surfactant-secreting type II alveolar cells. In addition to showing a block in type II cell differentiation, the neonatal lungs display increased numbers of proliferating cells and decreased numbers of apoptotic cells, leading to epithelial expansion and loss of airspace. Consistent with the phenotype observed, genes associated with alveolar maturation, survival, and proliferation were differentially expressed. Taken together, these results identify C/EBPalpha as a master regulator of airway epithelial maturation and suggest that the loss of C/EBPalpha could also be an important event in the multistep process of lung tumorigenesis. Furthermore, this study indicates that exploring the C/EBPalpha pathway might have therapeutic benefits for patients with respiratory distress syndromes.

A Library of Phosphoproteomic and Chromatin Signatures for Characterizing Cellular Responses to Drug Perturbations.

Abstract: Although the value of proteomics has been demonstrated, cost and scale are typically prohibitive, and gene expression profiling remains dominant for characterizing cellular responses to perturbations. However, high-throughput sentinel assays provide an opportunity for proteomics to contribute at a meaningful scale. We present a systematic library resource (90 drugs × 6 cell lines) of proteomic signatures that measure changes in the reduced-representation phosphoproteome (P100) and changes in epigenetic marks on histones (GCP). A majority of these drugs elicited reproducible signatures, but notable cell line- and assay-specific differences were observed. Using the "connectivity" framework, we compared signatures across cell types and integrated data across assays, including a transcriptional assay (L1000). Consistent connectivity among cell types revealed cellular responses that transcended lineage, and consistent connectivity among assays revealed unexpected associations between drugs. We further leveraged the resource against public data to formulate hypotheses for treatment of multiple myeloma and acute lymphocytic leukemia. This resource is publicly available at https://clue.io/proteomics.

WRN Helicase is a Synthetic Lethal Target in Microsatellite Unstable Cancers

Abstract: Summary paragraph Synthetic lethality, an interaction whereby the co-occurrence of two or more genetic events lead to cell death but one event alone does not, can be exploited to develop novel cancer therapeutics1. DNA repair processes represent attractive synthetic lethal targets since many cancers exhibit an impaired DNA repair pathway, which can lead these cells to become dependent on specific repair proteins2. The success of poly (ADP-ribose) polymerase 1 (PARP-1) inhibitors in homologous recombination-deficient cancers highlights the potential of this approach in clinical oncology3,4. Hypothesizing that other DNA repair defects would give rise to alternative synthetic lethal relationships, we asked if there are specific dependencies in cancers with microsatellite instability (MSI), which results from impaired DNA mismatch repair (MMR). Here we analyzed data from large-scale CRISPR/Cas9 knockout and RNA interference (RNAi) silencing screens and found that the RecQ DNA helicase WRN was selectively essential in MSI cell lines, yet dispensable in microsatellite stable (MSS) cell lines. WRN depletion induced double-strand DNA breaks and promoted apoptosis and cell cycle arrest selectively in MSI models. MSI cancer models specifically required the helicase activity, but not the exonuclease activity of WRN. These findings expose WRN as a synthetic lethal vulnerability and promising drug target in MSI cancers.

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.