Optimization of a high-throughput whole blood expression profiling methodology and its application to assess the pharmacodynamics of interferon (IFN) beta-1a or polyethylene glycol-conjugated IFN beta-1a in healthy clinical trial subjects.

BACKGROUND: Clinical trials offer a unique opportunity to study human disease and response to therapy in a highly controlled setting. The application of high-throughput expression profiling to peripheral blood from clinical trial subjects could facilitate the identification of transcripts that function as prognostic or diagnostic markers of disease or treatment. The paramount issue for these methods is the ability to produce robust, reproducible, and timely mRNA expression profiles from peripheral blood. Single-stranded complementary DNA (sscDNA) targets derived from whole blood exhibit improved detection of transcripts and reduced variance as compared to their complementary RNA counterparts and therefore provide a better option for interrogation of peripheral blood on oligonucleotide arrays. High-throughput microarray technologies such as the high-throughput plate array platform offer several advantages compared with slide- or cartridge-based arrays; however, manufacturer's protocols do not support the use of sscDNA targets. RESULTS: We have developed a highly reproducible, high-through put, whole blood expression profiling methodology based on sscDNA and used it to analyze human brain reference RNA and universal human reference RNA samples to identify experimental conditions that most highly correlated with a gold standard quantitative polymerase chain reaction reference dataset. We then utilized the optimized method to analyze whole blood samples from healthy clinical trial subjects treated with different versions of interferon (IFN) beta-1a. Analysis of whole blood samples before and after treatment with intramuscular [IM] IFN beta-1a or polyethylene glycol-conjugated IFN (PEG-IFN) beta-1a under optimized experimental conditions demonstrated that PEG-IFN beta-1a induced a more sustained and prolonged pharmacodynamic response than unmodified IM IFN beta-1a. These results provide validation of the utility of this new methodology and suggest the potential therapeutic benefit of a sustained pharmacodynamic response to PEG-IFN beta-1a. CONCLUSIONS: This novel microarray methodology is ideally suited for utilization in large clinical studies to identify expressed transcripts for the elucidation of disease mechanisms of action and as prognostic, diagnostic, or toxicity markers.

Sequencing and analysis of JC virus DNA from natalizumab-treated PML patients.

BACKGROUND: Progressive multifocal leukoencephalopathy (PML) in natalizumab-treated MS patients is linked to JC virus (JCV) infection. JCV sequence variation and rearrangements influence viral pathogenicity and tropism. To better understand PML development, we analyzed viral DNA sequences in blood, CSF and/or urine of natalizumab-treated PML patients. METHODS: Using biofluid samples from 17 natalizumab-treated PML patients, we sequenced multiple isolates of the JCV noncoding control region (NCCR), VP1 capsid coding region, and the entire 5 kb viral genome. RESULTS: Analysis of JCV from multiple biofluids revealed that individuals were infected with a single genotype. Across our patient cohort, multiple PML-associated NCCR rearrangements and VP1 mutations were present in CSF and blood, but absent from urine-derived virus. NCCR rearrangements occurred in CSF of 100% of our cohort. VP1 mutations were observed in blood or CSF in 81% of patients. Sequencing of complete JCV genomes demonstrated that NCCR rearrangements could occur without VP1 mutations, but VP1 mutations were not observed without NCCR rearrangement. CONCLUSIONS: These data confirm that JCV in natalizumab-PML patients is similar to that observed in other PML patient groups, multiple genotypes are associated with PML, individual patients appear to be infected with a single genotype, and PML-associated mutations arise in patients during PML development.

GECKO: a complete large-scale gene expression analysis platform.

BACKGROUND: Gecko (Gene Expression: Computation and Knowledge Organization) is a complete, high-capacity centralized gene expression analysis system, developed in response to the needs of a distributed user community. RESULTS: Based on a client-server architecture, with a centralized repository of typically many tens of thousands of Affymetrix scans, Gecko includes automatic processing pipelines for uploading data from remote sites, a data base, a computational engine implementing approximately 50 different analysis tools, and a client application. Among available analysis tools are clustering methods, principal component analysis, supervised classification including feature selection and cross-validation, multi-factorial ANOVA, statistical contrast calculations, and various post-processing tools for extracting data at given error rates or significance levels. On account of its open architecture, Gecko also allows for the integration of new algorithms. The Gecko framework is very general: non-Affymetrix and non-gene expression data can be analyzed as well. A unique feature of the Gecko architecture is the concept of the Analysis Tree (actually, a directed acyclic graph), in which all successive results in ongoing analyses are saved. This approach has proven invaluable in allowing a large (approximately 100 users) and distributed community to share results, and to repeatedly return over a span of years to older and potentially very complex analyses of gene expression data. CONCLUSIONS: The Gecko system is being made publicly available as free software http://sourceforge.net/projects/geckoe. In totality or in parts, the Gecko framework should prove useful to users and system developers with a broad range of analysis needs.

Finding genes in the C2C12 osteogenic pathway by k-nearest-neighbor classification of expression data.

A supervised classification scheme for analyzing microarray expression data, based on the k-nearest-neighbor method coupled to noise-reduction filters, has been used to find genes involved in the osteogenic pathway of the mouse C2C12 cell line studied here as a model for in vivo osteogenesis. The scheme uses as input a training set embodying expert biological knowledge, and provides internal estimates of its own misclassification errors, which furthermore enables systematic optimization of the classifier parameters. On the basis of the C2C12-generated expression data set with 34,130 expression profiles across 2 time courses, each comprised of 6 points, and a training set containing known members of the osteogenic, myoblastic, and adipocytic pathways, 176 new genes in addition to 28 originally in the training set are selected as relevant to osteogenesis. For this selection, the estimated sensitivity is 42% and the posterior false-positive rate (fraction of candidates that are spurious) is 12%. The corresponding sensitivity and false-positive rate for detection of myoblastic genes are 9% and 31%, respectively, and only 4% and approximately 100%, respectively, for adipocytic genes, in accordance with an experimental design that predominantly stimulated the osteogenic pathway. Validation of this selection is provided by examining expression of the genes in an independent biological assay involving mouse calvaria (skull bone) primary cell cultures, in which a large fraction of the 176 genes are seen to be strongly regulated, as well as by case-by-case analysis of the genes on the basis of expert domain knowledge. The methodology should be generalizable to any situation in which enough a priori biological knowledge exists to define a training set.