Aimée M. Dudley

Measuring absolute expression with microarrays with a calibrated reference sample and an extended signal intensity range.

Gene expression ratios derived from spotted-glass microarray experiments have become invaluable to researchers by providing sensitive and comprehensive indicators of the molecular underpinnings of cell behaviors and states. However, several drawbacks to this form of data have been noted, including the inability to relate ratios to absolute expression levels or to compare experimental conditions not measured with the same control. In this study we demonstrate a method for overcoming these obstacles. First, instead of cohybridizing labeled experimental and control samples, we hybridize each sample against labeled oligos complementary to every microarray feature. Ratios between sample intensities and intensities of the oligo reference measure sample RNA levels on a scale that relates to their absolute abundance, instead of to the variable and unknown abundances of a cDNA reference. We demonstrate that results from this type of hybridization are accurate and retain absolute abundance information far better than conventional microarray ratios. Next, to ensure the accurate measurement of sample and oligo reference intensities, which may differ by several orders of magnitude, we use a linear regression algorithm, implemented in a freely available perl script, to combine the linear ranges of multiple scans taken at different scanner sensitivity settings onto an extended linear scale. We discuss future applications of our method to measure RNA expression on the absolute scale of number of transcripts per cell from any organism for which oligo-based spotted-glass microarrays are available.

A global view of pleiotropy and phenotypically derived gene function in yeast

Pleiotropy, the ability of a single mutant gene to cause multiple mutant phenotypes, is a relatively common but poorly understood phenomenon in biology. Perhaps the greatest challenge in the analysis of pleiotropic genes is determining whether phenotypes associated with a mutation result from the loss of a single function or of multiple functions encoded by the same gene. Here we estimate the degree of pleiotropy in yeast by measuring the phenotypes of 4710 mutants under 21 environmental conditions, finding that it is significantly higher than predicted by chance. We use a biclustering algorithm to group pleiotropic genes by common phenotype profiles. Comparisons of these clusters to biological process classifications, synthetic lethal interactions, and protein complex data support the hypothesis that this method can be used to genetically define cellular functions. Applying these functional classifications to pleiotropic genes, we are able to dissect phenotypes into groups associated with specific gene functions.

Learning a prior on regulatory potential from eQTL data

Genome-wide RNA expression data provide a detailed view of an organisms biological state; hence, a dataset measuring expression variation between genetically diverse individuals (eQTL data) may provide important insights into the genetics of complex traits. However, with data from a relatively small number of individuals, it is difficult to distinguish true causal polymorphisms from the large number of possibilities. The problem is particularly challenging in populations with significant linkage disequilibrium, where traits are often linked to large chromosomal regions containing many genes. Here, we present a novel method, Lirnet, that automatically learns a regulatory potential for each sequence polymorphism, estimating how likely it is to have a significant effect on gene expression. This regulatory potential is defined in terms of “regulatory features”—including the function of the gene and the conservation, type, and position of genetic polymorphisms—that are available for any organism. The extent to which the different features influence the regulatory potential is learned automatically, making Lirnet readily applicable to different datasets, organisms, and feature sets. We apply Lirnet both to the human HapMap eQTL dataset and to a yeast eQTL dataset and provide statistical and biological results demonstrating that Lirnet produces significantly better regulatory programs than other recent approaches. We demonstrate in the yeast data that Lirnet can correctly suggest a specific causal sequence variation within a large, linked chromosomal region. In one example, Lirnet uncovered a novel, experimentally validated connection between Puf3—a sequence-specific RNA binding protein—and P-bodies—cytoplasmic structures that regulate translation and RNA stability—as well as the particular causative polymorphism, a SNP in Mkt1, that induces the variation in the pathway.

Surface Plasmon Resonance Biosensor for Rapid Label-Free Detection of Microribonucleic Acid at Subfemtomole Level

Microribonucleic acids (miRNAs) have been linked with various regulatory functions and disorders, such as cancers and heart diseases. They, therefore, present an important target for detection technologies for future medical diagnostics. We report here a novel method for rapid and sensitive miRNA detection and quantitation using surface plasmon resonance (SPR) sensor technology and a DNA*RNA antibody-based assay. The approach takes advantage of a novel high-performance portable SPR sensor instrument for spectroscopy of surface plasmons based on a special diffraction grating called a surface plasmon coupler and disperser (SPRCD). The surface of the grating is functionalized with thiolated DNA oligonucleotides which specifically capture miRNA from a liquid sample without amplification. Subsequently, an antibody that recognizes DNA*RNA hybrids is introduced to bind to the DNA*RNA complex and enhance sensor response to the captured miRNA. This approach allows detection of miRNA in less than 30 min at concentrations down to 2 pM with an absolute amount at high attomoles. The methodology is evaluated for analysis of miRNA from mouse liver tissues and is found to yield results which agree well with those provided by the quantitative polymerase chain reaction (qPCR).

Identifying regulatory mechanisms using individual variation reveals key role for chromatin modification

Sequence polymorphisms affect gene expression by perturbing the complex network of regulatory interactions. We propose a probabilistic method, called Geronemo, which directly aims to identify the mechanism by which genetic changes perturb the regulatory network. Geronemo automatically constructs a set of coregulated genes (modules), whose regulation can involve both sequence variations and expression of regulators. By exploiting the modularity of genetic regulatory systems, Geronemo reveals regulatory relationships that are indiscernible when genes are considered in isolation, allowing the recovery of intricate combinatorial regulation. By incorporating both expression and genotype of regulators, Geronemo captures cases where the effect of sequence variation on its targets is indirect. We applied Geronemo to a data set from the progeny generated by a cross between laboratory BY4716 (BY) and wild RM11-1a (RM) isolates of Saccharomyces cerevisiae. Geronemo produced previously undescribed hypotheses regarding genetic perturbations in the yeast regulatory network, including transcriptional regulation, signal transduction, and chromatin modification. In particular, we find a large number of modules that have both chromosomal characteristics and are regulated by chromatin modification proteins. Indeed, a large fraction of the variance in the expression can be explained by a small number of markers associated with chromatin modifiers. Additional analysis reveals positive selection for sequence evolution of elements in the Swi/Snf chromatin remodeling complex. Overall, our results suggest that a significant part of individual expression variation in yeast arises from evolution of a small number of chromatin structure modifiers.

Genomic Sequence Diversity and Population Structure of Saccharomyces cerevisiae Assessed by RAD-seq

The budding yeast Saccharomyces cerevisiae is important for human food production and as a model organism for biological research. The genetic diversity contained in the global population of yeast strains represents a valuable resource for a number of fields, including genetics, bioengineering, and studies of evolution and population structure. Here, we apply a multiplexed, reduced genome sequencing strategy (restriction site−associated sequencing or RAD-seq) to genotype a large collection of S. cerevisiae strains isolated from a wide range of geographical locations and environmental niches. The method permits the sequencing of the same 1% of all genomes, producing a multiple sequence alignment of 116,880 bases across 262 strains. We find diversity among these strains is principally organized by geography, with European, North American, Asian, and African/S. E. Asian populations defining the major axes of genetic variation. At a finer scale, small groups of strains from cacao, olives, and sake are defined by unique variants not present in other strains. One population, containing strains from a variety of fermentations, exhibits high levels of heterozygosity and a mixture of alleles from European and Asian populations, indicating an admixed origin for this group. We propose a model of geographic differentiation followed by human-associated admixture, primarily between European and Asian populations and more recently between European and North American populations. The large collection of genotyped yeast strains characterized here will provide a useful resource for the broad community of yeast researchers.

Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images

BackgroundSeveral algorithms have been proposed for detecting fluorescently labeled subcellular objects in microscope images. Many of these algorithms have been designed for specific tasks and validated with limited image data. But despite the potential of using extensive comparisons between algorithms to provide useful information to guide method selection and thus more accurate results, relatively few studies have been performed.ResultsTo better understand algorithm performance under different conditions, we have carried out a comparative study including eleven spot detection or segmentation algorithms from various application fields. We used microscope images from well plate experiments with a human osteosarcoma cell line and frames from image stacks of yeast cells in different focal planes. These experimentally derived images permit a comparison of method performance in realistic situations where the number of objects varies within image set. We also used simulated microscope images in order to compare the methods and validate them against a ground truth reference result. Our study finds major differences in the performance of different algorithms, in terms of both object counts and segmentation accuracies.ConclusionsThese results suggest that the selection of detection algorithms for image based screens should be done carefully and take into account different conditions, such as the possibility of acquiring empty images or images with very few spots. Our inclusion of methods that have not been used before in this context broadens the set of available detection methods and compares them against the current state-of-the-art methods for subcellular particle detection.

Model-driven analysis of experimentally determined growth phenotypes for 465 yeast gene deletion mutants under 16 different conditions

BackgroundUnderstanding the response of complex biochemical networks to genetic perturbations and environmental variability is a fundamental challenge in biology. Integration of high-throughput experimental assays and genome-scale computational methods is likely to produce insight otherwise unreachable, but specific examples of such integration have only begun to be explored.ResultsIn this study, we measured growth phenotypes of 465 Saccharomyces cerevisiae gene deletion mutants under 16 metabolically relevant conditions and integrated them with the corresponding flux balance model predictions. We first used discordance between experimental results and model predictions to guide a stage of experimental refinement, which resulted in a significant improvement in the quality of the experimental data. Next, we used discordance still present in the refined experimental data to assess the reliability of yeast metabolism models under different conditions. In addition to estimating predictive capacity based on growth phenotypes, we sought to explain these discordances by examining predicted flux distributions visualized through a new, freely available platform. This analysis led to insight into the glycerol utilization pathway and the potential effects of metabolic shortcuts on model results. Finally, we used model predictions and experimental data to discriminate between alternative raffinose catabolism routes.ConclusionsOur study demonstrates how a new level of integration between high throughput measurements and flux balance model predictions can improve understanding of both experimental and computational results. The added value of a joint analysis is a more reliable platform for specific testing of biological hypotheses, such as the catabolic routes of different carbon sources.

Genetics. Systems genetics.

Systems genetics is the next frontier in systems biology and medicine. From studies with peas over 150 years ago, Gregor Mendel deduced the laws that govern the inheritance of traits in most organisms. The brilliance, but also the limitation, of Mendels work was its focus on single-gene traits, such as flower color and plant height. However, phenotypic variation, including that which underlies health and disease in humans, often results from multiple interactions among numerous genetic and environmental factors. Systems genetics seeks to understand this complexity by integrating the questions and methods of systems biology with those of genetics to solve the fundamental problem of interrelating genotype and phenotype in complex traits and disease.

Aneuploidy underlies a multicellular phenotypic switch

Although microorganisms are traditionally used to investigate unicellular processes, the yeast Saccharomyces cerevisiae has the ability to form colonies with highly complex, multicellular structures. Colonies with the “fluffy” morphology have properties reminiscent of bacterial biofilms and are easily distinguished from the “smooth” colonies typically formed by laboratory strains. We have identified strains that are able to reversibly toggle between the fluffy and smooth colony-forming states. Using a combination of flow cytometry and high-throughput restriction-site associated DNA tag sequencing, we show that this switch is correlated with a change in chromosomal copy number. Furthermore, the gain of a single chromosome is sufficient to switch a strain from the fluffy to the smooth state, and its subsequent loss to revert the strain back to the fluffy state. Because copy number imbalance of six of the 16 S. cerevisiae chromosomes and even a single gene can modulate the switch, our results support the hypothesis that the state switch is produced by dosage-sensitive genes, rather than a general response to altered DNA content. These findings add a complex, multicellular phenotype to the list of molecular and cellular traits known to be altered by aneuploidy and suggest that chromosome missegregation can provide a quick, heritable, and reversible mechanism by which organisms can toggle between phenotypes.