Daniel F. Jaramillo

Bio: Daniel F. Jaramillo is an academic researcher from Stanford University. The author has contributed to research in topics: Saccharomyces cerevisiae & Gene. The author has an hindex of 6, co-authored 6 publications receiving 5216 citations.

Functional profiling of the Saccharomyces cerevisiae genome.

Abstract: Determining the effect of gene deletion is a fundamental approach to understanding gene function. Conventional genetic screens exhibit biases, and genes contributing to a phenotype are often missed. We systematically constructed a nearly complete collection of gene-deletion mutants (96% of annotated open reading frames, or ORFs) of the yeast Saccharomyces cerevisiae. DNA sequences dubbed 'molecular bar codes' uniquely identify each strain, enabling their growth to be analysed in parallel and the fitness contribution of each gene to be quantitatively assessed by hybridization to high-density oligonucleotide arrays. We show that previously known and new genes are necessary for optimal growth under six well-studied conditions: high salt, sorbitol, galactose, pH 8, minimal medium and nystatin treatment. Less than 7% of genes that exhibit a significant increase in messenger RNA expression are also required for optimal growth in four of the tested conditions. Our results validate the yeast gene-deletion collection as a valuable resource for functional genomics.

Chemogenomic profiling: Identifying the functional interactions of small molecules in yeast

Abstract: We demonstrate the efficacy of a genome-wide protocol in yeast that allows the identification of those gene products that functionally interact with small molecules and result in the inhibition of cellular proliferation. Here we present results from screening 10 diverse compounds in 80 genome-wide experiments against the complete collection of heterozygous yeast deletion strains. These compounds include anticancer and antifungal agents, statins, alverine citrate, and dyclonine. In several cases, we identified previously known interactions; furthermore, in each case, our analysis revealed novel cellular interactions, even when the relationship between a compound and its cellular target had been well established. In addition, we identified a chemical core structure shared among three therapeutically distinct compounds that inhibit the ERG24 heterozygous deletion strain, demonstrating that cells may respond similarly to compounds of related structure. The ability to identify on-and-off target effects in vivo is fundamental to understanding the cellular response to small-molecule perturbants.

Mechanisms of haploinsufficiency revealed by genome-wide profiling in yeast.

Abstract: Haploinsufficiency is defined as a dominant phenotype in diploid organisms that are heterozygous for a loss-of-function allele. Despite its relevance to human disease, neither the extent of haploinsufficiency nor its precise molecular mechanisms are well understood. We used the complete set of Saccharomyces cerevisiae heterozygous deletion strains to survey the genome for haploinsufficiency via fitness profiling in rich (YPD) and minimal media to identify all genes that confer a haploinsufficient growth defect. This assay revealed that ∼3% of all ∼5900 genes tested are haploinsufficient for growth in YPD. This class of genes is functionally enriched for metabolic processes carried out by molecular complexes such as the ribosome. Much of the haploinsufficiency in YPD is alleviated by slowing the growth rate of each strain in minimal media, suggesting that certain gene products are rate limiting for growth only in YPD. Overall, our results suggest that the primary mechanism of haploinsufficiency in yeast is due to insufficient protein production. We discuss the relevance of our findings in yeast to human haploinsufficiency disorders.

A unique and universal molecular barcode array

Abstract: Molecular barcode arrays allow the analysis of thousands of biological samples in parallel through the use of unique 20-base-pair (bp) DNA tags. Here we present a new barcode array, which is unique among microarrays in that it includes at least five replicates of every tag feature. The use of smaller dispersed replicate features dramatically improves performance versus a single larger feature and allows the correction of previously undetectable hybridization defects.

Novel insights into iron metabolism by integrating deletome and transcriptome analysis in an iron deficiency model of the yeast Saccharomyces cerevisiae.

Abstract: Iron-deficiency anemia is the most prevalent form of anemia world-wide. The yeast Saccharomyces cerevisiae has been used as a model of cellular iron deficiency, in part because many of its cellular pathways are conserved. To better understand how cells respond to changes in iron availability, we profiled the yeast genome with a parallel analysis of homozygous deletion mutants to identify essential components and cellular processes required for optimal growth under iron-limited conditions. To complement this analysis, we compared those genes identified as important for fitness to those that were differentially-expressed in the same conditions. The resulting analysis provides a global perspective on the cellular processes involved in iron metabolism. Using functional profiling, we identified several genes known to be involved in high affinity iron uptake, in addition to novel genes that may play a role in iron metabolism. Our results provide support for the primary involvement in iron homeostasis of vacuolar and endosomal compartments, as well as vesicular transport to and from these compartments. We also observed an unexpected importance of the peroxisome for growth in iron-limited media. Although these components were essential for growth in low-iron conditions, most of them were not differentially-expressed. Genes with altered expression in iron deficiency were mainly associated with iron uptake and transport mechanisms, with little overlap with those that were functionally required. To better understand this relationship, we used expression-profiling of selected mutants that exhibited slow growth in iron-deficient conditions, and as a result, obtained additional insight into the roles of CTI6, DAP1, MRS4 and YHR045W in iron metabolism. Comparison between functional and gene expression data in iron deficiency highlighted the complementary utility of these two approaches to identify important functional components. This should be taken into consideration when designing and analyzing data from these type of studies. We used this and other published data to develop a molecular interaction network of iron metabolism in yeast.

Network biology: understanding the cell's functional organization

Abstract: A key aim of postgenomic biomedical research is to systematically catalogue all molecules and their interactions within a living cell. There is a clear need to understand how these molecules and the interactions between them determine the function of this enormously complex machinery, both in isolation and when surrounded by other cells. Rapid advances in network biology indicate that cellular networks are governed by universal laws and offer a new conceptual framework that could potentially revolutionize our view of biology and disease pathologies in the twenty-first century.

Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection.

Abstract: We have systematically made a set of precisely defined, single-gene deletions of all nonessential genes in Escherichia coli K-12. Open-reading frame coding regions were replaced with a kanamycin cassette flanked by FLP recognition target sites by using a one-step method for inactivation of chromosomal genes and primers designed to create in-frame deletions upon excision of the resistance cassette. Of 4288 genes targeted, mutants were obtained for 3985. To alleviate problems encountered in high-throughput studies, two independent mutants were saved for every deleted gene. These mutants-the 'Keio collection'-provide a new resource not only for systematic analyses of unknown gene functions and gene regulatory networks but also for genome-wide testing of mutational effects in a common strain background, E. coli K-12 BW25113. We were unable to disrupt 303 genes, including 37 of unknown function, which are candidates for essential genes. Distribution is being handled via GenoBase (http://ecoli.aist-nara.ac.jp/).

Protein Misfolding, Functional Amyloid, and Human Disease

Abstract: Peptides or proteins convert under some conditions from their soluble forms into highly ordered fibrillar aggregates. Such transitions can give rise to pathological conditions ranging from neurodegenerative disorders to systemic amyloidoses. In this review, we identify the diseases known to be associated with formation of fibrillar aggregates and the specific peptides and proteins involved in each case. We describe, in addition, that living organisms can take advantage of the inherent ability of proteins to form such structures to generate novel and diverse biological functions. We review recent advances toward the elucidation of the structures of amyloid fibrils and the mechanisms of their formation at a molecular level. Finally, we discuss the relative importance of the common main-chain and side-chain interactions in determining the propensities of proteins to aggregate and describe some of the evidence that the oligomeric fibril precursors are the primary origins of pathological behavior.

Genome-Wide Insertional Mutagenesis of Arabidopsis thaliana

Abstract: Over 225,000 independent Agrobacterium transferred DNA (T-DNA) insertion events in the genome of the reference plant Arabidopsis thaliana have been created that represent near saturation of the gene space. The precise locations were determined for more than 88,000 T-DNA insertions, which resulted in the identification of mutations in more than 21,700 of the approximately 29,454 predicted Arabidopsis genes. Genome-wide analysis of the distribution of integration events revealed the existence of a large integration site bias at both the chromosome and gene levels. Insertion mutations were identified in genes that are regulated in response to the plant hormone ethylene.

A General Framework for Weighted Gene Co-Expression Network Analysis

Abstract: Gene co-expression networks are increasingly used to explore the system-level functionality of genes. The network construction is conceptually straightforward: nodes represent genes and nodes are connected if the corresponding genes are significantly co-expressed across appropriately chosen tissue samples. In reality, it is tricky to define the connections between the nodes in such networks. An important question is whether it is biologically meaningful to encode gene co-expression using binary information (connected=1, unconnected=0). We describe a general framework for ;soft' thresholding that assigns a connection weight to each gene pair. This leads us to define the notion of a weighted gene co-expression network. For soft thresholding we propose several adjacency functions that convert the co-expression measure to a connection weight. For determining the parameters of the adjacency function, we propose a biologically motivated criterion (referred to as the scale-free topology criterion). We generalize the following important network concepts to the case of weighted networks. First, we introduce several node connectivity measures and provide empirical evidence that they can be important for predicting the biological significance of a gene. Second, we provide theoretical and empirical evidence that the ;weighted' topological overlap measure (used to define gene modules) leads to more cohesive modules than its ;unweighted' counterpart. Third, we generalize the clustering coefficient to weighted networks. Unlike the unweighted clustering coefficient, the weighted clustering coefficient is not inversely related to the connectivity. We provide a model that shows how an inverse relationship between clustering coefficient and connectivity arises from hard thresholding. We apply our methods to simulated data, a cancer microarray data set, and a yeast microarray data set.