Roland Stoughton

Genetics of gene expression surveyed in maize, mouse and man

Treating messenger RNA transcript abundances as quantitative traits and mapping gene expression quantitative trait loci for these traits has been pursued in gene-specific ways. Transcript abundances often serve as a surrogate for classical quantitative traits in that the levels of expression are significantly correlated with the classical traits across members of a segregating population. The correlation structure between transcript abundances and classical traits has been used to identify susceptibility loci for complex diseases such as diabetes and allergic asthma. One study recently completed the first comprehensive dissection of transcriptional regulation in budding yeast, giving a detailed glimpse of a genome-wide survey of the genetics of gene expression. Unlike classical quantitative traits, which often represent gross clinical measurements that may be far removed from the biological processes giving rise to them, the genetic linkages associated with transcript abundance affords a closer look at cellular biochemical processes. Here we describe comprehensive genetic screens of mouse, plant and human transcriptomes by considering gene expression values as quantitative traits. We identify a gene expression pattern strongly associated with obesity in a murine cross, and observe two distinct obesity subtypes. Furthermore, we find that these obesity subtypes are under the control of different loci.

Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer

We describe a flexible system for gene expression profiling using arrays of tens of thousands of oligonucleotides synthesized in situ by an ink-jet printing method employing standard phosphoramidite chemistry. We have characterized the dependence of hybridization specificity and sensitivity on parameters including oligonucleotide length, hybridization stringency, sequence identity, sample abundance, and sample preparation method. We find that 60-mer oligonucleotides reliably detect transcript ratios at one copy per cell in complex biological samples, and that ink-jet arrays are compatible with several different sample amplification and labeling techniques. Furthermore, results using only a single carefully selected oligonucleotide per gene correlate closely with those obtained using complementary DNA (cDNA) arrays. Most of the genes for which measurements differ are members of gene families that can only be distinguished by oligonucleotides. Because different oligonucleotide sequences can be specified for each array, we anticipate that ink-jet oligonucleotide array technology will be useful in a wide variety of DNA microarray applications.

Integrated Genomic and Proteomic Analyses of Gene Expression in Mammalian Cells

Using DNA microarrays together with quantitative proteomic techniques (ICAT reagents, two-dimensional DIGE, and MS), we evaluated the correlation of mRNA and protein levels in two hematopoietic cell lines representing distinct stages of myeloid differentiation, as well as in the livers of mice treated for different periods of time with three different peroxisome proliferative activated receptor agonists. We observe that the differential expression of mRNA (up or down) can capture at most 40% of the variation of protein expression. Although the overall pattern of protein expression is similar to that of mRNA expression, the incongruent expression between mRNAs and proteins emphasize the importance of posttranscriptional regulatory mechanisms in cellular development or perturbation that can be unveiled only through integrated analyses of both proteins and mRNAs.

Experimental annotation of the human genome using microarray technology.

The most important product of the sequencing of a genome is a complete, accurate catalogue of genes and their products, primarily messenger RNA transcripts and their cognate proteins. Such a catalogue cannot be constructed by computational annotation alone; it requires experimental validation on a genome scale. Using ‘exon’ and ‘tiling’ arrays fabricated by ink-jet oligonucleotide synthesis, we devised an experimental approach to validate and refine computational gene predictions and define full-length transcripts on the basis of co-regulated expression of their exons. These methods can provide more accurate gene numbers and allow the detection of mRNA splice variants and identification of the tissue- and disease-specific conditions under which genes are expressed. We apply our technique to chromosome 22q under 69 experimental condition pairs, and to the entire human genome under two experimental conditions. We discuss implications for more comprehensive, consistent and reliable genome annotation, more efficient, full-length complementary DNA cloning strategies and application to complex diseases.

Large-scale prediction of Saccharomyces cerevisiae gene function using overlapping transcriptional clusters

Genome sequencing has led to the discovery of tens of thousands of potential new genes. Six years after the sequencing of the well-studied yeast Saccharomyces cerevisiae and the discovery that its genome encodes ∼6,000 predicted proteins, more than 2,000 have not yet been characterized experimentally, and determining their functions seems far from a trivial task. One crucial constraint is the generation of useful hypotheses about protein function. Using a new approach to interpret microarray data, we assign likely cellular functions with confidence values to these new yeast proteins. We perform extensive genome-wide validations of our predictions and offer visualization methods for exploration of the large numbers of functional predictions. We identify potential new members of many existing functional categories including 285 candidate proteins involved in transcription, processing and transport of non-coding RNA molecules. We present experimental validation confirming the involvement of several of these proteins in ribosomal RNA processing. Our methodology can be applied to a variety of genomics data types and organisms.

A Cell Proliferation Signature Is a Marker of Extremely Poor Outcome in a Subpopulation of Breast Cancer Patients

Breast cancer comprises a group of distinct subtypes that despite having similar histologic appearances, have very different metastatic potentials. Being able to identify the biological driving force, even for a subset of patients, is crucially important given the large population of women diagnosed with breast cancer. Here, we show that within a subset of patients characterized by relatively high estrogen receptor expression for their age, the occurrence of metastases is strongly predicted by a homogeneous gene expression pattern almost entirely consisting of cell cycle genes (5-year odds ratio of metastasis, 24.0; 95% confidence interval, 6.0-95.5). Overexpression of this set of genes is clearly associated with an extremely poor outcome, with the 10-year metastasis-free probability being only 24% for the poor group, compared with 85% for the good group. In contrast, this gene expression pattern is much less correlated with the outcome in other patient subpopulations. The methods described here also illustrate the value of combining clinical variables, biological insight, and machine-learning to dissect biological complexity. Our work presented here may contribute a crucial step towards rational design of personalized treatment.

Microarray standard data set and figures of merit for comparing data processing methods and experiment designs.

MOTIVATION There is a very large and growing level of effort toward improving the platforms, experiment designs, and data analysis methods for microarray expression profiling. Along with a growing richness in the approaches there is a growing confusion among most scientists as to how to make objective comparisons and choices between them for different applications. There is a need for a standard framework for the microarray community to compare and improve analytical and statistical methods. RESULTS We report on a microarray data set comprising 204 in-situ synthesized oligonucleotide arrays, each hybridized with two-color cDNA samples derived from 20 different human tissues and cell lines. Design of the approximately 24 000 60mer oligonucleotides that report approximately 2500 known genes on the arrays, and design of the hybridization experiments, were carried out in a way that supports the performance assessment of alternative data processing approaches and of alternative experiment and array designs. We also propose standard figures of merit for success in detecting individual differential expression changes or expression levels, and for detecting similarities and differences in expression patterns across genes and experiments. We expect this data set and the proposed figures of merit will provide a standard framework for much of the microarray community to compare and improve many analytical and statistical methods relevant to microarray data analysis, including image processing, normalization, error modeling, combining of multiple reporters per gene, use of replicate experiments, and sample referencing schemes in measurements based on expression change. AVAILABILITY/SUPPLEMENTARY INFORMATION Expression data and supplementary information are available at http://www.rii.com/publications/2003/HE_SDS.htm

Optimization of oligonucleotide arrays and RNA amplification protocols for analysis of transcript structure and alternative splicing.

Microarrays offer a high-resolution means for monitoring pre-mRNA splicing on a genomic scale. We have developed a novel, unbiased amplification protocol that permits labeling of entire transcripts. Also, hybridization conditions, probe characteristics, and analysis algorithms were optimized for detection of exons, exon-intron edges, and exon junctions. These optimized protocols can be used to detect small variations and isoform mixtures, map the tissue specificity of known human alternative isoforms, and provide a robust, scalable platform for high-throughput discovery of alternative splicing.