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Biomedical subjects

David Botstein

Publications and source records attributed to David Botstein.

At least 19 recordsLinked to original sources
PubMed2006

Identification of alterations in DNA copy number in host stromal cells during tumor progression.

The interactions between cancer cells and the surrounding host stromal tissue play a critical role in tumor progression and metastasis, but the molecular nature of this relationship remains largely uncharacterized. Furthermore, although genetic changes of neoplastic cells in tumors contribute significantly to tumor progression, it is not known whether similar changes occur in the adjacent host stromal microenvironment and whether they contribute to or inhibit tumorigenesis. To address this question in an unbiased and genome-wide manner, we applied high-resolution DNA copy number analysis to murine stromal DNA isolated from human xenograft tumors that were formed in immunodeficient mice. We show that numerous amplifications and deletions are found within the host stromal microenvironment, suggesting that alterations in host DNA copy number can occur and may play a significant role in modifying tumor-stromal interactions.

Animals↗
PubMed2006

Conservation of the metabolomic response to starvation across two divergent microbes.

We followed 68 cellular metabolites after carbon or nitrogen starvation of Escherichia coli and Saccharomyces cerevisiae, using a filter-culture methodology that allows exponential growth, nondisruptive nutrient removal, and fast quenching of metabolism. Dynamic concentration changes were measured by liquid chromatography-tandem mass spectrometry and viewed in clustered heat-map format. The major metabolic responses anticipated from metabolite-specific experiments in the literature were observed as well as a number of novel responses. When the data were analyzed by singular value decomposition, two dominant characteristic vectors were found, one corresponding to a generic starvation response and another to a nutrient-specific starvation response that is similar in both organisms. Together these captured a remarkable 72% of the metabolite concentration changes in the full data set. The responses described by the generic starvation response vector (42%) included, for example, depletion of most biosynthetic intermediates. The nutrient-specific vector (30%) included key responses such as increased phosphoenolpyruvate signaling glucose deprivation and increased alpha-ketoglutarate signaling ammonia deprivation. Metabolic similarity across organisms extends from the covalent reaction network of metabolism to include many elements of metabolome response to nutrient deprivation as well.

Carbon↗
PubMed2006

Expanded protein information at SGD: new pages and proteome browser.

The recent explosion in protein data generated from both directed small-scale studies and large-scale proteomics efforts has greatly expanded the quantity of available protein information and has prompted the Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) to enhance the depth and accessibility of protein annotations. In particular, we have expanded ongoing efforts to improve the integration of experimental information and sequence-based predictions and have redesigned the protein information web pages. A key feature of this redesign is the development of a GBrowse-derived interactive Proteome Browser customized to improve the visualization of sequence-based protein information. This Proteome Browser has enabled SGD to unify the display of hidden Markov model (HMM) domains, protein family HMMs, motifs, transmembrane regions, signal peptides, hydropathy plots and profile hits using several popular prediction algorithms. In addition, a physico-chemical properties page has been introduced to provide easy access to basic protein information. Improvements to the layout of the Protein Information page and integration of the Proteome Browser will facilitate the ongoing expansion of sequence-specific experimental information captured in SGD, including post-translational modifications and other user-defined annotations. Finally, SGD continues to improve upon the availability of genetic and physical interaction data in an ongoing collaboration with BioGRID by providing direct access to more than 82,000 manually-curated interactions.

Computer Graphics↗
PubMed2006

Back to the future: education for systems-level biologists.

We describe a graduate course in quantitative biology that is based on original path-breaking papers in diverse areas of biology; each of these papers depends on quantitative reasoning and theory as well as experiment. Close reading and discussion of these papers allows students with backgrounds in physics, computational sciences or biology to learn essential ideas and to communicate in the languages of disciplines other than their own.

Biology↗
PubMed2006

Accumulation of recessive lethal mutations in Saccharomyces cerevisiae mlh1 mismatch repair mutants is not associated with gross chromosomal rearrangements.

We examined mismatch repair (MMR)-defective diploid strains of budding yeast grown for approximately 160 generations to determine whether decreases in spore viability due to the uncovering of recessive lethal mutations correlated with an increase in gross chromosomal rearrangements (GCRs). No GCRs were detected despite dramatic decreases in spore viability, suggesting that frameshift and/or other unrepaired DNA replication lesions play a greater role than chromosomal instability in decreasing viability in MMR-defective strains.

Adaptor Proteins, Signal Transducing↗
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

Genome-wide detection of polymorphisms at nucleotide resolution with a single DNA microarray.

A central challenge of genomics is to detect, simply and inexpensively, all differences in sequence among the genomes of individual members of a species. We devised a system to detect all single-nucleotide differences between genomes with the use of data from a single hybridization to a whole-genome DNA microarray. This allowed us to detect a variety of spontaneous single-base pair substitutions, insertions, and deletions, and most (>90%) of the approximately 30,000 known single-nucleotide polymorphisms between two Saccharomyces cerevisiae strains. We applied this approach to elucidate the genetic basis of phenotypic variants and to identify the small number of single-base pair changes accumulated during experimental evolution of yeast.

Directed Molecular Evolution↗
PubMed2006

Genome-scale identification of membrane-associated human mRNAs.

The subcellular localization of proteins is critical to their biological roles. Moreover, whether a protein is membrane-bound, secreted, or intracellular affects the usefulness of, and the strategies for, using a protein as a diagnostic marker or a target for therapy. We employed a rapid and efficient experimental approach to classify thousands of human gene products as either "membrane-associated/secreted" (MS) or "cytosolic/nuclear" (CN). Using subcellular fractionation methods, we separated mRNAs associated with membranes from those associated with the soluble cytosolic fraction and analyzed these two pools by comparative hybridization to DNA microarrays. Analysis of 11 different human cell lines, representing lymphoid, myeloid, breast, ovarian, hepatic, colon, and prostate tissues, identified more than 5,000 previously uncharacterized MS and more than 6,400 putative CN genes at high confidence levels. The experimentally determined localizations correlated well with in silico predictions of signal peptides and transmembrane domains, but also significantly increased the number of human genes that could be cataloged as encoding either MS or CN proteins. Using gene expression data from a variety of primary human malignancies and normal tissues, we rationally identified hundreds of MS gene products that are significantly overexpressed in tumors compared to normal tissues and thus represent candidates for serum diagnostic tests or monoclonal antibody-based therapies. Finally, we used the catalog of CN gene products to generate sets of candidate markers of organ-specific tissue injury. The large-scale annotation of subcellular localization reported here will serve as a reference database and will aid in the rational design of diagnostic tests and molecular therapies for diverse diseases.

Biomarkers, Tumor↗
PubMed2006

Genome Snapshot: a new resource at the Saccharomyces Genome Database (SGD) presenting an overview of the Saccharomyces cerevisiae genome.

Sequencing and annotation of the entire Saccharomyces cerevisiae genome has made it possible to gain a genome-wide perspective on yeast genes and gene products. To make this information available on an ongoing basis, the Saccharomyces Genome Database (SGD) (http://www.yeastgenome.org/) has created the Genome Snapshot (http://db.yeastgenome.org/cgi-bin/genomeSnapShot.pl). The Genome Snapshot summarizes the current state of knowledge about the genes and chromosomal features of S.cerevisiae. The information is organized into two categories: (i) number of each type of chromosomal feature annotated in the genome and (ii) number and distribution of genes annotated to Gene Ontology terms. Detailed lists are accessible through SGD's Advanced Search tool (http://db.yeastgenome.org/cgi-bin/search/featureSearch), and all the data presented on this page are available from the SGD ftp site (ftp://ftp.yeastgenome.org/yeast/).

Chromosomes, Fungal↗
PubMed2006

Saccharomyces cerevisiae S288C genome annotation: a working hypothesis.

The S. cerevisiae genome is the most well-characterized eukaryotic genome and one of the simplest in terms of identifying open reading frames (ORFs), yet its primary annotation has been updated continually in the decade since its initial release in 1996 (Goffeau et al., 1996). The Saccharomyces Genome Database (SGD; www.yeastgenome.org) (Hirschman et al., 2006), the community-designated repository for this reference genome, strives to ensure that the S. cerevisiae annotation is as accurate and useful as possible. At SGD, the S. cerevisiae genome sequence and annotation are treated as a working hypothesis, which must be repeatedly tested and refined. In this paper, in celebration of the tenth anniversary of the completion of the S. cerevisiae genome sequence, we discuss the ways in which the S. cerevisiae sequence and annotation have changed, consider the multiple sources of experimental and comparative data on which these changes are based, and describe our methods for evaluating, incorporating and documenting these new data.

Base Sequence↗
PubMed2006

Gene expression profiles do not consistently predict the clinical treatment response in locally advanced breast cancer.

Neoadjuvant treatment offers an opportunity to correlate molecular variables to treatment response and to explore mechanisms of drug resistance in vivo. Here, we present a statistical analysis of large-scale gene expression patterns and their relationship to response following neoadjuvant chemotherapy in locally advanced breast cancers. We analyzed cDNA expression data from 81 tumors from two patient series, one treated with doxorubicin alone (51) and the other treated with 5-fluorouracil and mitomycin (30), and both were previously studied for correlations between TP53 status and response to therapy. We observed a low frequency of progressive disease within the luminal A subtype from both series (2 of 36 versus 13 of 45 patients; P = 0.0089) and a high frequency of progressive disease among patients with luminal B type tumors treated with doxorubicin (5 of 8 patients; P = 0.0078); however, aside from these two observations, no other consistent associations between response to chemotherapy and tumor subtype were observed. These specific associations could possibly be explained by covariance with TP53 mutation status, which also correlated with tumor subtype. Using supervised analysis, we could not uncover a gene profile that could reliably (>70% accuracy and specificity) predict response to either treatment regimen.

Adult↗
PubMed2006

Ammonium toxicity and potassium limitation in yeast.

DNA microarray analysis of gene expression in steady-state chemostat cultures limited for potassium revealed a surprising connection between potassium and ammonium: potassium limits growth only when ammonium is the nitrogen source. Under potassium limitation, ammonium appears to be toxic for Saccharomyces cerevisiae. This ammonium toxicity, which appears to occur by leakage of ammonium through potassium channels, is recapitulated under high-potassium conditions by over-expression of ammonium transporters. Although ammonium toxicity is well established in metazoans, it has never been reported for yeast. To characterize the response to ammonium toxicity, we examined the filtrates of these cultures for compounds whose excretion might serve to detoxify the ammonium (such as urea in mammals). Using liquid chromatography-tandem mass spectrometry to assay for a wide array of metabolites, we detected excreted amino acids. The amounts of amino acids excreted increased in relation to the severity of growth impairment by ammonium, suggesting that amino acid excretion is used by yeast for ammonium detoxification.

Amino Acids↗
PubMed2005

Transcriptional response of steady-state yeast cultures to transient perturbations in carbon source.

To understand the dynamics of transcriptional response to changing environments, well defined, easily controlled, and short-term perturbation experiments were undertaken. We subjected steady-state cultures of Saccharomyces cerevisiae in chemostats growing on limiting galactose to two different size pulses of glucose, well known to be a preferred carbon source. Although these pulses were not large enough to change growth rates or cell size, approximately 25% of the genes changed their expression at least 2-fold. Using DNA microarrays to estimate mRNA abundance, we found a number of distinguishable patterns of transcriptional response among the many genes whose expression changed. Many of these genes were already known to be regulated by particular transcription factors; we estimated five potentially relevant transcription factor activities from the observed changes in gene expression (i.e., Mig1p, Gal4p, Cat8p, Rgt1p, Adr1p, and Rcs1p). With these estimates, for two regulatory circuits involving interaction among multiple regulators we could generate dynamical models that quantitatively account for the observed transcriptional responses to the transient perturbations.

Carbon↗
PubMed2005

Gene expression profiling reveals molecularly and clinically distinct subtypes of glioblastoma multiforme.

Glioblastoma multiforme (GBM) is the most common form of malignant glioma, characterized by genetic instability, intratumoral histopathological variability, and unpredictable clinical behavior. We investigated global gene expression in surgical samples of brain tumors. Gene expression profiling revealed large differences between normal brain samples and tumor tissues and between GBMs and lower-grade oligodendroglial tumors. Extensive differences in gene expression were found among GBMs, particularly in genes involved in angiogenesis, immune cell infiltration, and extracellular matrix remodeling. We found that the gene expression patterns in paired specimens from the same GBM invariably were more closely related to each other than to any other tumor, even when the paired specimens had strikingly divergent histologies. Survival analyses revealed a set of approximately 70 genes more highly expressed in rapidly progressing tumors that stratified GBMs into two groups that differed by >4-fold in median duration of survival. We further investigated one gene from the group, FABP7, and confirmed its association with survival in two unrelated cohorts totaling 105 patients. Expression of FABP7 enhanced the motility of glioma-derived cells in vitro. Our analyses thus identify and validate a prognostic marker of both biologic and clinical significance and provide a series of putative markers for additional evaluation.

Brain Neoplasms↗
PubMed2005

Homeostatic adjustment and metabolic remodeling in glucose-limited yeast cultures.

We studied the physiological response to glucose limitation in batch and steady-state (chemostat) cultures of Saccharomyces cerevisiae by following global patterns of gene expression. Glucose-limited batch cultures of yeast go through two sequential exponential growth phases, beginning with a largely fermentative phase, followed by an essentially completely aerobic use of residual glucose and evolved ethanol. Judging from the patterns of gene expression, the state of the cells growing at steady state in glucose-limited chemostats corresponds most closely with the state of cells in batch cultures just before they undergo this "diauxic shift." Essentially the same pattern was found between chemostats having a fivefold difference in steady-state growth rate (the lower rate approximating that of the second phase respiratory growth rate in batch cultures). Although in both cases the cells in the chemostat consumed most of the glucose, in neither case did they seem to be metabolizing it primarily through respiration. Although there was some indication of a modest oxidative stress response, the chemostat cultures did not exhibit the massive environmental stress response associated with starvation that also is observed, at least in part, during the diauxic shift in batch cultures. We conclude that despite the theoretical possibility of a switch to fully aerobic metabolism of glucose in the chemostat under conditions of glucose scarcity, homeostatic mechanisms are able to carry out metabolic adjustment as if fermentation of the glucose is the preferred option until the glucose is entirely depleted. These results suggest that some aspect of actual starvation, possibly a component of the stress response, may be required for triggering the metabolic remodeling associated with the diauxic shift.

Aerobiosis↗
PubMed2005

A DNA microarray survey of gene expression in normal human tissues.

BACKGROUND: Numerous studies have used DNA microarrays to survey gene expression in cancer and other disease states. Comparatively little is known about the genes expressed across the gamut of normal human tissues. Systematic studies of global gene-expression patterns, by linking variation in the expression of specific genes to phenotypic variation in the cells or tissues in which they are expressed, provide clues to the molecular organization of diverse cells and to the potential roles of the genes. RESULTS: Here we describe a systematic survey of gene expression in 115 human tissue samples representing 35 different tissue types, using cDNA microarrays representing approximately 26,000 different human genes. Unsupervised hierarchical cluster analysis of the gene-expression patterns in these tissues identified clusters of genes with related biological functions and grouped the tissue specimens in a pattern that reflected their anatomic locations, cellular compositions or physiologic functions. In unsupervised and supervised analyses, tissue-specific patterns of gene expression were readily discernable. By comparative hybridization to normal genomic DNA, we were also able to estimate transcript abundances for expressed genes. CONCLUSIONS: Our dataset provides a baseline for comparison to diseased tissues, and will aid in the identification of tissue-specific functions. In addition, our analysis identifies potential molecular markers for detection of injury to specific organs and tissues, and provides a foundation for selection of potential targets for selective anticancer therapy.

Cluster Analysis↗
PubMed2005

Inference of combinatorial regulation in yeast transcriptional networks: a case study of sporulation.

Decomposing transcriptional regulatory networks into functional modules and determining logical relations between them is the first step toward understanding transcriptional regulation at the system level. Modules based on analysis of genome-scale data can serve as the basis for inferring combinatorial regulation and for building mathematical models to quantitatively describe the behavior of the networks. We present here an algorithm called modem to identify target genes of a transcription factor (TF) from a single expression experiment, based on a joint probabilistic model for promoter sequence and gene expression data. We show how this method can facilitate the discovery of specific instances of combinatorial regulation and illustrate this for a specific case of transcriptional networks that regulate sporulation in the yeast Saccharomyces cerevisiae. Applying this method to analyze two crucial TFs in sporulation, Ndt80p and Sum1p, we were able to delineate their overlapping binding sites. We proposed a mechanistic model for the competitive regulation by the two TFs on a defined subset of sporulation genes. We show that this model accounts for the temporal control of the "middle" sporulation genes and suggest a similar regulatory arrangement can be found in developmental programs in higher organisms.

Algorithms↗