Biomedical subjects

Chad L Myers

Publications and source records attributed to Chad L Myers.

9 recordsLinked to original sources

PubMed2023

RNF4 and USP7 cooperate in ubiquitin-regulated steps of DNA replication.

DNA replication requires precise regulation achieved through post-translational modifications, including ubiquitination and SUMOylation. These modifications are linked by the SUMO-targeted E3 ubiquitin ligases (STUbLs). Ring finger protein 4 (RNF4), one of only two mammalian STUbLs, participates in double-strand break repair and resolving DNA-protein cross-links. However, its role in DNA replication has been poorly understood. Using CRISPR/Cas9 genetic screens, we discovered an unexpected dependency of RNF4 mutants on ubiquitin specific peptidase 7 (USP7) for survival in TP53-null retinal pigment epithelial cells. TP53-/-/RNF4-/-/USP7-/- triple knockout (TKO) cells displayed defects in DNA replication that cause genomic instability. These defects were exacerbated by the proteasome inhibitor bortezomib, which limited the nuclear ubiquitin pool. A shortage of free ubiquitin suppressed the ataxia telangiectasia and Rad3-related (ATR)-mediated checkpoint response, leading to increased cell death. In conclusion, RNF4 and USP7 work cooperatively to sustain a functional level of nuclear ubiquitin to maintain the integrity of the genome.

Animals↗

PubMed2017

A New Natural Product Analog of Blasticidin S Reveals Cellular Uptake Facilitated by the NorA Multidrug Transporter.

The permeation of antibiotics through bacterial membranes to their target site is a crucial determinant of drug activity but in many cases remains poorly understood. During screening efforts to discover new broad-spectrum antibiotic compounds from marine sponge samples, we identified a new analog of the peptidyl nucleoside antibiotic blasticidin S that exhibited up to 16-fold-improved potency against a range of laboratory and clinical bacterial strains which we named P10. Whole-genome sequencing of laboratory-evolved strains of Staphylococcus aureus resistant to blasticidin S and P10, combined with genome-wide assessment of the fitness of barcoded Escherichia coli knockout strains in the presence of the antibiotics, revealed that restriction of cellular access was a key feature in the development of resistance to this class of drug. In particular, the gene encoding the well-characterized multidrug efflux pump NorA was found to be mutated in 69% of all S. aureus isolates resistant to blasticidin S or P10. Unexpectedly, resistance was associated with inactivation of norA, suggesting that the NorA transporter facilitates cellular entry of peptidyl nucleosides in addition to its known role in the efflux of diverse compounds, including fluoroquinolone antibiotics.

Bacterial Proteins↗

PubMed2006

GOLEM: an interactive graph-based gene-ontology navigation and analysis tool.

BACKGROUND: The Gene Ontology has become an extremely useful tool for the analysis of genomic data and structuring of biological knowledge. Several excellent software tools for navigating the gene ontology have been developed. However, no existing system provides an interactively expandable graph-based view of the gene ontology hierarchy. Furthermore, most existing tools are web-based or require an Internet connection, will not load local annotations files, and provide either analysis or visualization functionality, but not both. RESULTS: To address the above limitations, we have developed GOLEM (Gene Ontology Local Exploration Map), a visualization and analysis tool for focused exploration of the gene ontology graph. GOLEM allows the user to dynamically expand and focus the local graph structure of the gene ontology hierarchy in the neighborhood of any chosen term. It also supports rapid analysis of an input list of genes to find enriched gene ontology terms. The GOLEM application permits the user either to utilize local gene ontology and annotations files in the absence of an Internet connection, or to access the most recent ontology and annotation information from the gene ontology webpage. GOLEM supports global and organism-specific searches by gene ontology term name, gene ontology id and gene name. CONCLUSION: GOLEM is a useful software tool for biologists interested in visualizing the local directed acyclic graph structure of the gene ontology hierarchy and searching for gene ontology terms enriched in genes of interest. It is freely available both as an application and as an applet at http://function.princeton.edu/GOLEM.

Computer Graphics↗

PubMed2006

Finding function: evaluation methods for functional genomic data.

BACKGROUND: Accurate evaluation of the quality of genomic or proteomic data and computational methods is vital to our ability to use them for formulating novel biological hypotheses and directing further experiments. There is currently no standard approach to evaluation in functional genomics. Our analysis of existing approaches shows that they are inconsistent and contain substantial functional biases that render the resulting evaluations misleading both quantitatively and qualitatively. These problems make it essentially impossible to compare computational methods or large-scale experimental datasets and also result in conclusions that generalize poorly in most biological applications. RESULTS: We reveal issues with current evaluation methods here and suggest new approaches to evaluation that facilitate accurate and representative characterization of genomic methods and data. Specifically, we describe a functional genomics gold standard based on curation by expert biologists and demonstrate its use as an effective means of evaluation of genomic approaches. Our evaluation framework and gold standard are freely available to the community through our website. CONCLUSION: Proper methods for evaluating genomic data and computational approaches will determine how much we, as a community, are able to learn from the wealth of available data. We propose one possible solution to this problem here but emphasize that this topic warrants broader community discussion.

Algorithms↗

PubMed2006

Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae.

BACKGROUND: The study of complex biological networks and prediction of gene function has been enabled by high-throughput (HTP) methods for detection of genetic and protein interactions. Sparse coverage in HTP datasets may, however, distort network properties and confound predictions. Although a vast number of well substantiated interactions are recorded in the scientific literature, these data have not yet been distilled into networks that enable system-level inference. RESULTS: We describe here a comprehensive database of genetic and protein interactions, and associated experimental evidence, for the budding yeast Saccharomyces cerevisiae, as manually curated from over 31,793 abstracts and online publications. This literature-curated (LC) dataset contains 33,311 interactions, on the order of all extant HTP datasets combined. Surprisingly, HTP protein-interaction datasets currently achieve only around 14% coverage of the interactions in the literature. The LC network nevertheless shares attributes with HTP networks, including scale-free connectivity and correlations between interactions, abundance, localization, and expression. We find that essential genes or proteins are enriched for interactions with other essential genes or proteins, suggesting that the global network may be functionally unified. This interconnectivity is supported by a substantial overlap of protein and genetic interactions in the LC dataset. We show that the LC dataset considerably improves the predictive power of network-analysis approaches. The full LC dataset is available at the BioGRID (http://www.thebiogrid.org) and SGD (http://www.yeastgenome.org/) databases. CONCLUSION: Comprehensive datasets of biological interactions derived from the primary literature provide critical benchmarks for HTP methods, augment functional prediction, and reveal system-level attributes of biological networks.

Computational Biology↗

PubMed2006

Global analysis of gene function in yeast by quantitative phenotypic profiling.

We present a method for the global analysis of the function of genes in budding yeast based on hierarchical clustering of the quantitative sensitivity profiles of the 4756 strains with individual homozygous deletion of nonessential genes to a broad range of cytotoxic or cytostatic agents. This method is superior to other global methods of identifying the function of genes involved in the various DNA repair and damage checkpoint pathways as well as other interrogated functions. Analysis of the phenotypic profiles of the 51 diverse treatments places a total of 860 genes of unknown function in clusters with genes of known function. We demonstrate that this can not only identify the function of unknown genes but can also suggest the mechanism of action of the agents used. This method will be useful when used alone and in conjunction with other global approaches to identify gene function in yeast.

Cluster Analysis↗

PubMed2005

Discovery of biological networks from diverse functional genomic data.

We have developed a general probabilistic system for query-based discovery of pathway-specific networks through integration of diverse genome-wide data. This framework was validated by accurately recovering known networks for 31 biological processes in Saccharomyces cerevisiae and experimentally verifying predictions for the process of chromosomal segregation. Our system, bioPIXIE, a public, comprehensive system for integration, analysis, and visualization of biological network predictions for S. cerevisiae, is freely accessible over the worldwide web.

Bayes Theorem↗

PubMed2005

Visualization-based discovery and analysis of genomic aberrations in microarray data.

BACKGROUND: Chromosomal copy number changes (aneuploidies) play a key role in cancer progression and molecular evolution. These copy number changes can be studied using microarray-based comparative genomic hybridization (array CGH) or gene expression microarrays. However, accurate identification of amplified or deleted regions requires a combination of visual and computational analysis of these microarray data. RESULTS: We have developed ChARMView, a visualization and analysis system for guided discovery of chromosomal abnormalities from microarray data. Our system facilitates manual or automated discovery of aneuploidies through dynamic visualization and integrated statistical analysis. ChARMView can be used with array CGH and gene expression microarray data, and multiple experiments can be viewed and analyzed simultaneously. CONCLUSION: ChARMView is an effective and accurate visualization and analysis system for recognizing even small aneuploidies or subtle expression biases, identifying recurring aberrations in sets of experiments, and pinpointing functionally relevant copy number changes. ChARMView is freely available under the GNU GPL at http://function.princeton.edu/ChARMView.

Algorithms↗

PubMed2004

Accurate detection of aneuploidies in array CGH and gene expression microarray data.

MOTIVATION: Chromosomal copy number changes (aneuploidies) are common in cell populations that undergo multiple cell divisions including yeast strains, cell lines and tumor cells. Identification of aneuploidies is critical in evolutionary studies, where changes in copy number serve an adaptive purpose, as well as in cancer studies, where amplifications and deletions of chromosomal regions have been identified as a major pathogenetic mechanism. Aneuploidies can be studied on whole-genome level using array CGH (a microarray-based method that measures the DNA content), but their presence also affects gene expression. In gene expression microarray analysis, identification of copy number changes is especially important in preventing aberrant biological conclusions based on spurious gene expression correlation or masked phenotypes that arise due to aneuploidies. Previously suggested approaches for aneuploidy detection from microarray data mostly focus on array CGH, address only whole-chromosome or whole-arm copy number changes, and rely on thresholds or other heuristics, making them unsuitable for fully automated general application to gene expression datasets. There is a need for a general and robust method for identification of aneuploidies of any size from both array CGH and gene expression microarray data. RESULTS: We present ChARM (Chromosomal Aberration Region Miner), a robust and accurate expectation-maximization based method for identification of segmental aneuploidies (partial chromosome changes) from gene expression and array CGH microarray data. Systematic evaluation of the algorithm on synthetic and biological data shows that the method is robust to noise, aneuploidal segment size and P-value cutoff. Using our approach, we identify known chromosomal changes and predict novel potential segmental aneuploidies in commonly used yeast deletion strains and in breast cancer. ChARM can be routinely used to identify aneuploidies in array CGH datasets and to screen gene expression data for aneuploidies or array biases. Our methodology is sensitive enough to detect statistically significant and biologically relevant aneuploidies even when expression or DNA content changes are subtle as in mixed populations of cells. AVAILABILITY: Code available by request from the authors and on Web supplement at http://function.cs.princeton.edu/ChARM/

Algorithms↗