Exome sequencing reveals a thrombopoietin ligand mutation in a Micronesian family with autosomal recessive aplastic anemia.

We recently identified 2 siblings afflicted with idiopathic, autosomal recessive aplastic anemia. Whole-exome sequencing identified a novel homozygous missense mutation in thrombopoietin (THPO, c.112C>T) in both affected siblings. This mutation encodes an arginine to cysteine substitution at residue 38 or residue 17 excluding the 21-amino acid signal peptide of THPO receptor binding domain (RBD). THPO has 4 conserved cysteines in its RBD that form 2 disulfide bonds. Our in silico modeling predicts that introduction of a fifth cysteine may disrupt normal disulfide bonding to cause poor receptor binding. In functional assays, the mutant-THPO-containing media shows two- to threefold reduced ability to sustain UT7-TPO cells, which require THPO for proliferation. Both parents and a sibling with heterozygous R17C change have reduced platelet counts, whereas a sibling with wild-type sequence has normal platelet count. Thus, the R17C partial loss-of-function allele results in aplastic anemia in the homozygous state and mild thrombocytopenia in the heterozygous state in our family. Together with the recent identification of THPO receptor (MPL) mutations and the effects of THPO agonists in aplastic anemia, our results have clinical implications in the diagnosis and treatment of patients with aplastic anemia and highlight a role for the THPO-MPL pathway in hematopoiesis in vivo.

Emerging applications of network and pathway analysis in drug discovery and development.

Molecular pathway and network analysis tools that have been developed over the past decade provide an emerging systems-wide perspective for the analysis of gene expression, proteomic and metabolomic data. These tools link relevant extracted literature information available from structured knowledge bases with user-friendly features that enable analysis and interpretation of the global impact of a disease stage or drug treatment. Pathway and network analysis tools have been found to have broad applicability within the biomedical community, most specifically in cancer research to elucidate mechanisms of tumor progression and for biomarker discovery, and more recently in the area of drug discovery and development. This review discusses recent applications of pathway and network analysis for predictive in silico modeling in the area of drug discovery and development, including theoretical and practical advances and the implications for their application.

Pathway analysis tools and toxicogenomics reference databases for risk assessment.

The pharmaceutical industry has begun to leverage a range of new technologies (proteomics, pharmacogenomics, metabolomics and molecular toxicology [e.g., toxicogenomics]) and analysis tools that are becoming increasingly integrated in the area of drug discovery and development. The approach of analyzing the vast amount of toxicogenomics data generated using molecular pathway and networks analysis tools in combination with analysis of reference data will be the main focus of this review. We will demonstrate how this combined approach can increase the understanding of the molecular mechanisms that lead to chemical-induced toxicity and application of this knowledge to compound risk assessment. We will provide an example of the insights achieved through a molecular toxicology analysis based on the well-known hepatotoxicant lipopolysaccharide to illustrate the utility of these new tools in the analysis of complex data sets, both in vivo and in vitro. The ultimate objective is a better lead selection process that improves the chances for success across the different stages of drug discovery and development.

Use of a mixed tissue RNA design for performance assessments on multiple microarray formats.

The comparability and reliability of data generated using microarray technology would be enhanced by use of a common set of standards that allow accuracy, reproducibility and dynamic range assessments on multiple formats. We designed and tested a complex biological reagent for performance measurements on three commercial oligonucleotide array formats that differ in probe design and signal measurement methodology. The reagent is a set of two mixtures with different proportions of RNA for each of four rat tissues (brain, liver, kidney and testes). The design provides four known ratio measurements of >200 reference probes, which were chosen for their tissue-selectivity, dynamic range coverage and alignment to the same exemplar transcript sequence across all three platforms. The data generated from testing three biological replicates of the reagent at eight laboratories on three array formats provides a benchmark set for both laboratory and data processing performance assessments. Close agreement with target ratios adjusted for sample complexity was achieved on all platforms and low variance was observed among platforms, replicates and sites. The mixed tissue design produces a reagent with known gene expression changes within a complex sample and can serve as a paradigm for performance standards for microarrays that target other species.

Myb proteins inhibit fibroblast transformation by v-Rel.

Genes that cause cancer have been divided into two general classes--oncogenes that act in a dominant fashion to transform normal cells into a malignant state, and tumor suppressor genes that act in a dominant fashion to prevent such transformation. In this report, we demonstrate that both the v-myb retroviral oncogene, which causes leukemic transformation of hematopoietic cells, and the c-myb proto-oncogene can also function as inhibitors of fibroblast transformation by the v-rel oncogene. These results imply that the myb genes can function either as oncogenes or as tumor suppressors in different cellular contexts.

Toxicogenomics in drug discovery and development: mechanistic analysis of compound/class-dependent effects using the DrugMatrix database.

A range of genomics technologies are increasingly becoming integrated with existing scientific disciplines to broaden and strengthen existing capabilities and open new avenues of research in drug discovery and development. Examples of these new research fields are proteomics, pharmacogenomics, metabolomics and toxicogenomics. Here we review the application of toxicogenomics to improve the evaluation of drug safety, mechanism of action and toxicity in the drug discovery and development process.

Development of a large-scale chemogenomics database to improve drug candidate selection and to understand mechanisms of chemical toxicity and action.

Successful drug discovery requires accurate decision making in order to advance the best candidates from initial lead identification to final approval. Chemogenomics, the use of genomic tools in pharmacology and toxicology, offers a promising enhancement to traditional methods of target identification/validation, lead identification, efficacy evaluation, and toxicity assessment. To realize the value of chemogenomics information, a contextual database is needed to relate the physiological outcomes induced by diverse compounds to the gene expression patterns measured in the same animals. Massively parallel gene expression characterization coupled with traditional assessments of drug candidates provides additional, important mechanistic information, and therefore a means to increase the accuracy of critical decisions. A large-scale chemogenomics database developed from in vivo treated rats provides the context and supporting data to enhance and accelerate accurate interpretation of mechanisms of toxicity and pharmacology of chemicals and drugs. To date, approximately 600 different compounds, including more than 400 FDA approved drugs, 60 drugs approved in Europe and Japan, 25 withdrawn drugs, and 100 toxicants, have been profiled in up to 7 different tissues of rats (representing over 3200 different drug-dose-time-tissue combinations). Accomplishing this task required evaluating and improving a number of in vivo and microarray protocols, including over 80 rigorous quality control steps. The utility of pairing clinical pathology assessments with gene expression data is illustrated using three anti-neoplastic drugs: carmustine, methotrexate, and thioguanine, which had similar effects on the blood compartment, but diverse effects on hepatotoxicity. We will demonstrate that gene expression events monitored in the liver can be used to predict pathological events occurring in that tissue as well as in hematopoietic tissues.