Current Role at Stanford
Associate Director, PharmGKB
Education & Certifications
PhD, Stanford University, Biophysics (2002)
SB, Massachusetts Institute of Technology, Biology (1993)
Associate Director, PharmGKB
My work focuses on pharmacogenomics, the study of the impact of genetics on drug response, and its application to personalized medicine and personal genomics. My interests range from basic research studying gene-variant-drug associations to translation of pharmacogenomics information into the clinical setting, including reducing barriers to implementing PGx prescribing guidelines in the clinical electronic environment (CDS/EHR). I am particularly interested in translation of human genome sequencing data to pharmacogenomic-based therapeutic recommendations that are actionable in the clinic.
Reporting and sharing pharmacogenetic test results across clinical laboratories and electronic health records is a crucial step toward the implementation of clinical pharmacogenetics, but allele function and phenotype terms are not standardized. Our goal was to develop terms that can be broadly applied to characterize pharmacogenetic allele function and inferred phenotypes.Terms currently used by genetic testing laboratories and in the literature were identified. The Clinical Pharmacogenetics Implementation Consortium (CPIC) used the Delphi method to obtain a consensus and agree on uniform terms among pharmacogenetic experts.Experts with diverse involvement in at least one area of pharmacogenetics (clinicians, researchers, genetic testing laboratorians, pharmacogenetics implementers, and clinical informaticians; n = 58) participated. After completion of five surveys, a consensus (>70%) was reached with 90% of experts agreeing to the final sets of pharmacogenetic terms.The proposed standardized pharmacogenetic terms will improve the understanding and interpretation of pharmacogenetic tests and reduce confusion by maintaining consistent nomenclature. These standard terms can also facilitate pharmacogenetic data sharing across diverse electronic health care record systems with clinical decision support.Genet Med advance online publication 21 July 2016Genetics in Medicine (2016); doi:10.1038/gim.2016.87.
View details for DOI 10.1038/gim.2016.87
View details for PubMedID 27441996
As pharmacogenomics becomes integrated into clinical practice, curation of published studies becomes increasingly important. At the Pharmacogenomics Knowledgebase (PharmGKB; www.pharmgkb.org), pharmacogenetic associations reported in published articles are manually curated and evaluated. Standard terminologies are used, making findings uniform and unambiguous. Lack of information, clarity, or standards in the original report can make it difficult or impossible to curate. We provide 10 rules to help authors ensure that their results are accurately captured and integrated.
View details for DOI 10.1002/cpt.15
View details for PubMedID 25670512
The Pharmacogenomics Knowledgebase (PharmGKB) is a resource that collects, curates, and disseminates information about the impact of human genetic variation on drug responses. It provides clinically relevant information, including dosing guidelines, annotated drug labels, and potentially actionable gene-drug associations and genotype-phenotype relationships. Curators assign levels of evidence to variant-drug associations using well-defined criteria based on careful literature review. Thus, PharmGKB is a useful source of high-quality information supporting personalized medicine-implementation projects.
View details for DOI 10.1038/clpt.2012.96
View details for Web of Science ID 000309017000009
View details for PubMedID 22992668
View details for PubMedCentralID PMC3660037
Whole-genome sequencing harbors unprecedented potential for characterization of individual and family genetic variation. Here, we develop a novel synthetic human reference sequence that is ethnically concordant and use it for the analysis of genomes from a nuclear family with history of familial thrombophilia. We demonstrate that the use of the major allele reference sequence results in improved genotype accuracy for disease-associated variant loci. We infer recombination sites to the lowest median resolution demonstrated to date (< 1,000 base pairs). We use family inheritance state analysis to control sequencing error and inform family-wide haplotype phasing, allowing quantification of genome-wide compound heterozygosity. We develop a sequence-based methodology for Human Leukocyte Antigen typing that contributes to disease risk prediction. Finally, we advance methods for analysis of disease and pharmacogenomic risk across the coding and non-coding genome that incorporate phased variant data. We show these methods are capable of identifying multigenic risk for inherited thrombophilia and informing the appropriate pharmacological therapy. These ethnicity-specific, family-based approaches to interpretation of genetic variation are emblematic of the next generation of genetic risk assessment using whole-genome sequencing.
View details for DOI 10.1371/journal.pgen.1002280
View details for Web of Science ID 000295419100031
View details for PubMedID 21935354
View details for PubMedCentralID PMC3174201
Recent advances in high-throughput genotyping and phenotyping have accelerated the creation of pharmacogenomic data. Consequently, the community requires standard formats to exchange large amounts of diverse information. To facilitate the transfer of pharmacogenomics data between databases and analysis packages, we have created a standard XML (eXtensible Markup Language) schema that describes both genotype and phenotype data as well as associated metadata. The schema accommodates information regarding genes, drugs, diseases, experimental methods, genomic/RNA/protein sequences, subjects, subject groups, and literature. The Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB; www.pharmgkb.org) has used this XML schema for more than 5 years to accept and process submissions containing more than 1,814,139 SNPs on 20,797 subjects using 8,975 assays. Although developed in the context of pharmacogenomics, the schema is of general utility for exchange of genotype and phenotype data. We have written syntactic and semantic validators to check documents using this format. The schema and code for validation is available to the community at http://www.pharmgkb.org/schema/index.html (last accessed: 8 October 2007).
View details for DOI 10.1002/humu.20662
View details for Web of Science ID 000253033000002
View details for PubMedID 17994540
The publication of the crystal structures of the ribosome offers an opportunity to retrospectively evaluate the information content of hundreds of qualitative biochemical and biophysical studies of these structures. We assessed the correspondence between more than 2,500 experimental proximity measurements and the distances observed in the ribosomal crystals. Although detailed experimental procedures and protocols are unique in almost each analyzed paper, the data can be grouped into subsets with similar patterns and analyzed in an integrative fashion. We found that, for crosslinking, footprinting, and cleavage data, the corresponding distances observed in crystal structures generally did not exceed the maximum values expected (from the estimated length of the agent and maximal anticipated deviations from the conformations found in crystals). However, the distribution of distances had heavier tails than those typically assumed when building three-dimensional models, and the fraction of incompatible distances was greater than expected. Some of these incompatibilities can be attributed to the experimental methods used. In addition, the accuracy of these procedures appears to be sensitive to the different reactivities, flexibilities, and interactions among the components. These findings demonstrate the necessity of a very careful analysis of data used for structural modeling and consideration of all possible parameters that could potentially influence the quality of measurements. We conclude that experimental proximity measurements can provide useful distance information for structural modeling, but with a broad distribution of inferred distance ranges. We also conclude that development of automated modeling approaches would benefit from better annotations of experimental data for detection and interpretation of their significance.
View details for DOI 10.1017/S135583820202407X
View details for Web of Science ID 000175155500002
View details for PubMedID 12003488
View details for PubMedCentralID PMC1370250
This document is an update to the 2011 Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for CYP2C9 and VKORC1 genotypes and warfarin dosing. Evidence from the published literature is presented for CYP2C9, VKORC1, CYP4F2, and rs12777823 genotype-guided warfarin dosing to achieve a target international normalized ratio of 2-3 when clinical genotype results are available. In addition, this updated guideline incorporates recommendations for adult and pediatric patients that are specific to continental ancestry.
View details for DOI 10.1002/cpt.668
View details for PubMedID 28198005
Numerous pharmacogenetic clinical guidelines and recommendations have been published, but barriers have hindered the clinical implementation of pharmacogenetics. The Translational Pharmacogenetics Program (TPP) of the NIH Pharmacogenomics Research Network was established in 2011 to catalog and contribute to the development of pharmacogenetic implementations at eight US healthcare systems, with the goal to disseminate real-world solutions for the barriers to clinical pharmacogenetic implementation. The TPP collected and normalized pharmacogenetic implementation metrics through June 2015, including gene-drug pairs implemented, interpretations of alleles and diplotypes, numbers of tests performed and actionable results, and workflow diagrams. TPP participant institutions developed diverse solutions to overcome many barriers, but the use of Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines provided some consistency among the institutions. The TPP also collected some pharmacogenetic implementation outcomes (scientific, educational, financial, and informatics), which may inform healthcare systems seeking to implement their own pharmacogenetic testing programs. This article is protected by copyright. All rights reserved.
View details for DOI 10.1002/cpt.630
View details for PubMedID 28090649
The current state of pharmacogenetic data curation and dissemination is described, and evidence-based resources for applying pharmacogenetic data in clinical practice are reviewed.Implementation of pharmacogenetics in clinical practice has been relatively slow despite substantial scientific progress in understanding linkages between genetic variation and variability of drug response and effect. One factor that has inhibited the adoption of genetic data to guide medication use is a lack of knowledge of how to translate genetic test results into clinical action based on currently available evidence. Other implementation challenges include controversy over selection of appropriate evidentiary thresholds for routine clinical implementation of pharmacogenetic data and the difficulty of compiling scientific data to support clinical recommendations given that large randomized controlled trials to demonstrate the utility of pharmacogenetic testing are not feasible or are not considered necessary to establish clinical utility. Organizations such as the Clinical Pharmacogenetics Implementation Consortium (CPIC) and the Pharmacogenomics Knowledgebase (PharmGKB) systematically evaluate emerging evidence of pharmacogenomic linkages and publish evidence-based prescribing recommendations to inform clinical practice. Both CPIC and PharmGKB provide online resources that facilitate the interpretation of genetic test results and provide prescribing recommendations for specific gene-drug pairs.Resources provided by organizations such as CPIC and PharmGKB, which use standardized approaches to evaluate the literature and provide clinical guidance for a growing number of gene-drug pairs, are essential for the implementation of pharmacogenetics into routine clinical practice.
View details for PubMedID 27864205
View details for PubMedCentralID PMC5117674
Owing to its highly polymorphic nature and major contribution to the metabolism and bioactivation of numerous clinically used drugs, CYP2D6 is one of the most extensively studied drug-metabolizing enzymes and pharmacogenes. CYP2D6 alleles confer no, decreased, normal, or increased activity and cause a wide range of activity among individuals and between populations. However, there is no standard approach to translate diplotypes into predicted phenotype.We exploited CYP2D6 allele-frequency data that have been compiled for Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines (>60,000 subjects, 173 reports) in order to estimate genotype-predicted phenotype status across major world populations based on activity score (AS) assignments.Allele frequencies vary considerably across the major ethnic groups predicting poor metabolizer status (AS = 0) between 0.4 and 5.4% across world populations. The prevalence of genotypic intermediate (AS = 0.5) and normal (AS = 1, 1.5, or 2) metabolizers ranges between 0.4 and 11% and between 67 and 90%, respectively. Finally, 1 to 21% of subjects (AS >2) are predicted to have ultrarapid metabolizer status.This comprehensive study summarizes allele frequencies, diplotypes, and predicted phenotype across major populations, providing a rich data resource for clinicians and researchers. Challenges of phenotype prediction from genotype data are highlighted and discussed.Genet Med advance online publication 07 July 2016Genetics in Medicine (2016); doi:10.1038/gim.2016.80.
View details for DOI 10.1038/gim.2016.80
View details for PubMedID 27388693
To move beyond a select few genes/drugs, the successful adoption of pharmacogenomics into routine clinical care requires a curated and machine-readable database of pharmacogenomic knowledge suitable for use in an electronic health record (EHR) with clinical decision support (CDS). Recognizing that EHR vendors do not yet provide a standard set of CDS functions for pharmacogenetics, the Clinical Pharmacogenetics Implementation Consortium (CPIC) Informatics Working Group is developing and systematically incorporating a set of EHR-agnostic implementation resources into all CPIC guidelines. These resources illustrate how to integrate pharmacogenomic test results in clinical information systems with CDS to facilitate the use of patient genomic data at the point of care. Based on our collective experience creating existing CPIC resources and implementing pharmacogenomics at our practice sites, we outline principles to define the key features of future knowledge bases and discuss the importance of these knowledge resources for pharmacogenomics and ultimately precision medicine.
View details for DOI 10.1093/jamia/ocw027
View details for PubMedID 27026620
Significant advances have been made in the clinical implementation of pharmacogenomics in recent years with tools for clinical decision support (CDS) being developed and integrated in the electronic health record (EHR). In this issue, the article by Hussain et al. describes the creation of a disease-drug association tool that enables providers to search by disease indications to receive a list of treatment options marked with pharmacogenomics annotations at the point of prescribing.
View details for DOI 10.1002/cpt.420
View details for PubMedID 27367543
The antiretroviral protease inhibitor atazanavir inhibits hepatic uridine diphosphate glucuronosyltransferase (UGT) 1A1, thereby preventing the glucuronidation and elimination of bilirubin. Resultant indirect hyperbilirubinemia with jaundice can cause premature discontinuation of atazanavir. Risk for bilirubin-related discontinuation is highest among individuals who carry two UGT1A1 decreased function alleles (UGT1A1*28 or *37). We summarize published literature that supports this association and provide recommendations for atazanavir prescribing when UGT1A1 genotype is known (updates at www.pharmgkb.org).
View details for DOI 10.1002/cpt.269
View details for PubMedID 26417955
This article provides nomenclature recommendations developed by an international workgroup to increase transparency and standardization of pharmacogenetic (PGx) result reporting. Presently, sequence variants identified by PGx tests are described using different nomenclature systems. In addition, PGx analysis may detect different sets of variants for each gene, which can affect interpretation of results. This practice has caused confusion and may thereby impede the adoption of clinical PGx testing. Standardization is critical to move PGx forward.
View details for DOI 10.1002/cpt.280
View details for PubMedID 26479518
High throughput sequencing has facilitated a precipitous drop in the cost of genomic sequencing, prompting predictions of a revolution in medicine via genetic personalization of diagnostic and therapeutic strategies. There are significant barriers to realizing this goal that are related to the difficult task of interpreting personal genetic variation. A comprehensive, widely accessible application for interpretation of whole genome sequence data is needed. Here, we present a series of methods for identification of genetic variants and genotypes with clinical associations, phasing genetic data and using Mendelian inheritance for quality control, and providing predictive genetic information about risk for rare disease phenotypes and response to pharmacological therapy in single individuals and father-mother-child trios. We demonstrate application of these methods for disease and drug response prognostication in whole genome sequence data from twelve unrelated adults, and for disease gene discovery in one father-mother-child trio with apparently simplex congenital ventricular arrhythmia. In doing so we identify clinically actionable inherited disease risk and drug response genotypes in pre-symptomatic individuals. We also nominate a new candidate gene in congenital arrhythmia, ATP2B4, and provide experimental evidence of a regulatory role for variants discovered using this framework.
View details for DOI 10.1371/journal.pgen.1005496
View details for Web of Science ID 000364401600008
View details for PubMedID 26448358
View details for PubMedCentralID PMC4598191
PSB brings together top researchers from around the world to exchange research results and address open issues in all aspects of computational biology. PSB 2015 marks the twentieth anniversary of PSB. Reaching a milestone year is an accomplishment well worth celebrating. It is long enough to have seen big changes occur, but recent enough to be relevant for today. As PSB celebrates twenty years of service, we would like to take this opportunity to congratulate the PSB community for your success. We would also like the community to join us in a time of celebration and reflection on this accomplishment.
View details for PubMedID 25592562
Phenytoin is a widely used antiepileptic drug with a narrow therapeutic index and large interpatient variability, partly due to genetic variations in the gene encoding cytochrome P450 (CYP)2C9 (CYP2C9). Furthermore, the variant allele HLA-B*15:02, encoding human leukocyte antigen, is associated with an increased risk of Stevens-Johnson syndrome and toxic epidermal necrolysis in response to phenytoin treatment. We summarize evidence from the published literature supporting these associations and provide recommendations for the use of phenytoin based on CYP2C9 and/or HLA-B genotype (also available on PharmGKB: http://www.pharmgkb.org). The purpose of this guideline is to provide information for the interpretation of HLA-B and/or CYP2C9 genotype tests so that the results can guide dosing and/or use of phenytoin. Detailed guidelines for the use of phenytoin as well as analyses of cost-effectiveness are out of scope. Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines are periodically updated at http://www.pharmgkb.org.
View details for DOI 10.1038/clpt.2014.159
View details for PubMedID 25099164
Whole-genome sequencing (WGS) is increasingly applied in clinical medicine and is expected to uncover clinically significant findings regardless of sequencing indication.To examine coverage and concordance of clinically relevant genetic variation provided by WGS technologies; to quantitate inherited disease risk and pharmacogenomic findings in WGS data and resources required for their discovery and interpretation; and to evaluate clinical action prompted by WGS findings.An exploratory study of 12 adult participants recruited at Stanford University Medical Center who underwent WGS between November 2011 and March 2012. A multidisciplinary team reviewed all potentially reportable genetic findings. Five physicians proposed initial clinical follow-up based on the genetic findings.Genome coverage and sequencing platform concordance in different categories of genetic disease risk, person-hours spent curating candidate disease-risk variants, interpretation agreement between trained curators and disease genetics databases, burden of inherited disease risk and pharmacogenomic findings, and burden and interrater agreement of proposed clinical follow-up.Depending on sequencing platform, 10% to 19% of inherited disease genes were not covered to accepted standards for single nucleotide variant discovery. Genotype concordance was high for previously described single nucleotide genetic variants (99%-100%) but low for small insertion/deletion variants (53%-59%). Curation of 90 to 127 genetic variants in each participant required a median of 54 minutes (range, 5-223 minutes) per genetic variant, resulted in moderate classification agreement between professionals (Gross ?, 0.52; 95% CI, 0.40-0.64), and reclassified 69% of genetic variants cataloged as disease causing in mutation databases to variants of uncertain or lesser significance. Two to 6 personal disease-risk findings were discovered in each participant, including 1 frameshift deletion in the BRCA1 gene implicated in hereditary breast and ovarian cancer. Physician review of sequencing findings prompted consideration of a median of 1 to 3 initial diagnostic tests and referrals per participant, with fair interrater agreement about the suitability of WGS findings for clinical follow-up (Fleiss ?, 0.24; P?001).In this exploratory study of 12 volunteer adults, the use of WGS was associated with incomplete coverage of inherited disease genes, low reproducibility of detection of genetic variation with the highest potential clinical effects, and uncertainty about clinically reportable findings. In certain cases, WGS will identify clinically actionable genetic variants warranting early medical intervention. These issues should be considered when determining the role of WGS in clinical medicine.
View details for DOI 10.1001/jama.2014.1717
View details for PubMedID 24618965
View details for PubMedCentralID PMC4119063
The Clinical Pharmacogenetics Implementation Consortium (CPIC) publishes genotype-based drug guidelines to help clinicians understand how available genetic test results could be used to optimize drug therapy. CPIC has focused initially on well-known examples of pharmacogenomic associations that have been implemented in selected clinical settings, publishing nine to date. Each CPIC guideline adheres to a standardized format and includes a standard system for grading levels of evidence linking genotypes to phenotypes and assigning a level of strength to each prescribing recommendation. CPIC guidelines contain the necessary information to help clinicians translate patient-specific diplotypes for each gene into clinical phenotypes or drug dosing groups. This paper reviews the development process of the CPIC guidelines and compares this process to the Institute of Medicine's Standards for Developing Trustworthy Clinical Practice Guidelines.
View details for PubMedID 24479687
Human leukocyte antigen B (HLA-B) is a gene that encodes a cell surface protein involved in presenting antigens to the immune system. The variant allele HLA-B*15:02 is associated with an increased risk of Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) in response to carbamazepine treatment. We summarize evidence from the published literature supporting this association and provide recommendations for the use of carbamazepine based on HLA-B genotype (also available on PharmGKB: http://www.pharmgkb.org). The purpose of this article is to provide information to allow the interpretation of clinical HLA-B*15:02 genotype tests so that the results can be used to guide the use of carbamazepine. The guideline provides recommendations for the use of carbamazepine when HLA-B*15:02 genotype results are available. Detailed guidelines regarding the selection of alternative therapies, the use of phenotypic tests, when to conduct genotype testing, and cost-effectiveness analyses are beyond the scope of this document. Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines are published and updated periodically on the PharmGKB website at (http://www.pharmgkb.org).
View details for DOI 10.1038/clpt.2013.103
View details for PubMedID 23695185
The need for efficient text-mining tools that support curation of the biomedical literature is ever increasing. In this article, we describe an experiment aimed at verifying whether a text-mining tool capable of extracting meaningful relationships among domain entities can be successfully integrated into the curation workflow of a major biological database. We evaluate in particular (i) the usability of the system's interface, as perceived by users, and (ii) the correlation of the ranking of interactions, as provided by the text-mining system, with the choices of the curators.
View details for DOI 10.1093/database/bas021
View details for Web of Science ID 000304924100001
View details for PubMedID 22529178
View details for PubMedCentralID PMC3332569
Personalized medicine is expected to benefit from combining genomic information with regular monitoring of physiological states by multiple high-throughput methods. Here, we present an integrative personal omics profile (iPOP), an analysis that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. Our iPOP analysis revealed various medical risks, including type 2 diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions. Extremely high-coverage genomic and transcriptomic data, which provide the basis of our iPOP, revealed extensive heteroallelic changes during healthy and diseased states and an unexpected RNA editing mechanism. This study demonstrates that longitudinal iPOP can be used to interpret healthy and diseased states by connecting genomic information with additional dynamic omics activity.
View details for DOI 10.1016/j.cell.2012.02.009
View details for Web of Science ID 000301889500023
View details for PubMedID 22424236
View details for PubMedCentralID PMC3341616
The mission of the Pharmacogenomics Knowledge Base (PharmGKB; www.pharmgkb.org ) is to collect, encode and disseminate knowledge about the impact of human genetic variations on drug responses. It is an important worldwide resource of clinical pharmacogenomic biomarkers available to all. The PharmGKB website has evolved to highlight our knowledge curation and aggregation over our previous emphasis on collecting primary data. This review summarizes the methods we use to drive this expanded scope of 'Knowledge Acquisition to Clinical Applications', the new features available on our website and our future goals.
View details for DOI 10.2217/BMM.11.94
View details for Web of Science ID 000298488200009
View details for PubMedID 22103613
View details for PubMedCentralID PMC3339046
Warfarin is a widely used anticoagulant with a narrow therapeutic index and large interpatient variability in the dose required to achieve target anticoagulation. Common genetic variants in the cytochrome P450-2C9 (CYP2C9) and vitamin K-epoxide reductase complex (VKORC1) enzymes, in addition to known nongenetic factors, account for ~50% of warfarin dose variability. The purpose of this article is to assist in the interpretation and use of CYP2C9 and VKORC1 genotype data for estimating therapeutic warfarin dose to achieve an INR of 2-3, should genotype results be available to the clinician. The Clinical Pharmacogenetics Implementation Consortium (CPIC) of the National Institutes of Health Pharmacogenomics Research Network develops peer-reviewed gene-drug guidelines that are published and updated periodically on http://www.pharmgkb.org based on new developments in the field.(1).
View details for DOI 10.1038/clpt.2011.185
View details for Web of Science ID 000295119200035
View details for PubMedID 21900891
View details for PubMedCentralID PMC3187550
Thiopurine methyltransferase (TPMT) activity exhibits monogenic co-dominant inheritance, with ethnic differences in the frequency of occurrence of variant alleles. With conventional thiopurine doses, homozygous TPMT-deficient patients (~1 in 178 to 1 in 3,736 individuals with two nonfunctional TPMT alleles) experience severe myelosuppression, 30-60% of individuals who are heterozygotes (~3-14% of the population) show moderate toxicity, and homozygous wild-type individuals (~86-97% of the population) show lower active thioguanine nucleolides and less myelosuppression. We provide dosing recommendations (updates at http://www.pharmgkb.org) for azathioprine, mercaptopurine (MP), and thioguanine based on TPMT genotype.
View details for DOI 10.1038/clpt.2010.320
View details for Web of Science ID 000287439600018
View details for PubMedID 21270794
Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery.
View details for DOI 10.1038/nbt.1666
View details for Web of Science ID 000281719100019
View details for PubMedID 20829833
PharmGKB is a knowledge base that captures the relationships between drugs, diseases/phenotypes and genes involved in pharmacokinetics (PK) and pharmacodynamics (PD). This information includes literature annotations, primary data sets, PK and PD pathways, and expert-generated summaries of PK/PD relationships between drugs, diseases/phenotypes and genes. PharmGKB's website is designed to effectively disseminate knowledge to meet the needs of our users. PharmGKB currently has literature annotations documenting the relationship of over 500 drugs, 450 diseases and 600 variant genes. In order to meet the needs of whole genome studies, PharmGKB has added new functionalities, including browsing the variant display by chromosome and cytogenetic locations, allowing the user to view variants not located within a gene. We have developed new infrastructure for handling whole genome data, including increased methods for quality control and tools for comparison across other data sources, such as dbSNP, JSNP and HapMap data. PharmGKB has also added functionality to accept, store, display and query high throughput SNP array data. These changes allow us to capture more structured information on phenotypes for better cataloging and comparison of data. PharmGKB is available at www.pharmgkb.org.
View details for DOI 10.1093/nar/gkm1009
View details for Web of Science ID 000252545400160
View details for PubMedID 18032438
View details for PubMedCentralID PMC2238877
View details for PubMedID 15882130
To determine how genetic variations contribute the variations in drug response, we need to know the genes that are related to drugs of interest. But there are no publicly available data-bases of known gene-drug relationships, and it is time-consuming to search the literature for this information. We have developed a resource to support the storage, summarization, and dissemination of key gene-drug interactions of relevance to pharmacogenetics. Extracting all gene-drug relationships from the literature is a daunting task, so we distributed a tool to acquire this knowledge from the scientific community. We also developed a categorization scheme to classify gene-drug relationships according to the type of pharmacogenetic evidence that supports them. Our resource (http://www.pharmgkb.org/home/project-community.jsp) can be queried by gene or drug, and it summarizes gene-drug relationships, categories of evidence, and supporting literature. This resource is growing, containing entries for 138 genes and 215 drugs of pharmacogenetics significance, and is a core component of PharmGKB, a pharmacogenetics knowledge base (http://www.pharmgkb.org).
View details for Web of Science ID 000226723300159
View details for PubMedID 15360921
The crystal structures of the ribosome reveal remarkable complexity and provide a starting set of snapshots with which to understand the dynamics of translation. To augment the static crystallographic models with dynamic information present in crosslink, footprint, and cleavage data, we examined 2691 proximity measurements and focused on the subset that was apparently incompatible with >40 published crystal structures. The measurements from this subset generally involve regions of the structure that are functionally conserved and structurally flexible. Local movements in the crystallographic states of the ribosome that would satisfy biochemical proximity measurements show coherent patterns suggesting alternative conformations of the ribosome. Three different types of data obtained for the two subunits display similar "mismatching" patterns, suggesting that the signals are robust and real. In particular, there is an indication of coherent motion in the decoding region within the 30S subunit and central protuberance and surrounding areas of the 50S subunit. Directions of rearrangements fluctuate around the proposed path of tRNA translocation and the plane parallel to the interface of the two subunits. Our results demonstrate that systematic combination and analysis of noisy, apparently incompatible data sources can provide biologically useful signals about structural dynamics.
View details for Web of Science ID 000186175900001
View details for PubMedID 14561879
View details for PubMedCentralID PMC1287051
View details for Web of Science ID 000181756700019
The many interactions of tRNA with the ribosome are fundamental to protein synthesis. During the peptidyl transferase reaction, the acceptor ends of the aminoacyl and peptidyl tRNAs must be in close proximity to allow peptide bond formation, and their respective anticodons must base pair simultaneously with adjacent trinucleotide codons on the mRNA. The two tRNAs in this state can be arranged in two nonequivalent general configurations called the R and S orientations, many versions of which have been proposed for the geometry of tRNAs in the ribosome. Here, we report the combined use of computational analysis and tethered hydroxyl-radical probing to constrain their arrangement. We used Fe(II) tethered to the 5' end of anticodon stem-loop analogs (ASLs) of tRNA and to the 5' end of deacylated tRNA(Phe) to generate hydroxyl radicals that probe proximal positions in the backbone of adjacent tRNAs in the 70S ribosome. We inferred probe-target distances from the resulting RNA strand cleavage intensities and used these to calculate the mutual arrangement of A-site and P-site tRNAs in the ribosome, using three different structure estimation algorithms. The two tRNAs are constrained to the S configuration with an angle of about 45 degrees between the respective planes of the molecules. The terminal phosphates of 3'CCA are separated by 23 A when using the tRNA crystal conformations, and the anticodon arms of the two tRNAs are sufficiently close to interact with adjacent codons in mRNA.
View details for Web of Science ID 000085267900007
View details for PubMedID 10688361
View details for PubMedCentralID PMC1369908
Considerable evidence indicates that free radical injury may underlie the pathologic changes in muscular dystrophies from mammalian and avian species. We have investigated the role of oxidative injury in muscle necrosis in mice with a muscular dystrophy due to a defect in the dystrophin gene (the mdx strain). In order to avoid secondary consequences of muscle necrosis, all experiments were done on muscle prior to the onset of the degenerative process (i.e. during the 'pre-necrotic' phase) which lasted up to 20 days of age in the muscles examined. In pre-necrotic mdx muscle, there was an induction of expression of genes encoding antioxidant enzymes, indicative of a cellular response to oxidative stress. In addition, the levels of lipid peroxidation were greater in mdx muscle than in the control. Since the free radical nitric oxide (NO*) has been shown to mediate oxidative injury in various disease states, and because dystrophin has been shown to form a complex with the enzyme nitric oxide synthase, we examined pre-necrotic mdx muscle for evidence of NO*-mediated injury by measuring cellular nitrotyrosine formation. By both immunohistochemical and electrochemical analyses, no evidence of increased nitrotyrosine levels in mdx muscle was detected. Therefore, although no relationship with NO*-mediated toxicity was found, we found evidence of increased oxidative stress preceding the onset of muscle cell death in dystrophin-deficient mice. These results lend support to the hypothesis that free radical-mediated injury may contribute to the pathogenesis of muscular dystrophies.
View details for Web of Science ID 000077605200013
View details for PubMedID 9879685
The gamma-glutamyl carboxylase and vitamin K epoxidase activities of a series of mutants of bovine vitamin K-dependent carboxylase with progressively larger COOH-terminal deletions have been analyzed. The recombinant wild-type (residues 1-758) and mutant protein carboxylases, Cbx 711, Cbx 676, and Cbx 572, representing residues 1-711, 1-676, and 1-572, respectively, were expressed in baculovirus-infected Sf9 cells. Wild-type carboxylase had a Km for the substrate Phe-Leu-Glu-Glu-Leu (FLEEL) of 0.87 mM; the carboxylation of FLEEL was stimulated 2.5-fold by proPT18, the propeptide of prothrombin. Its Km for vitamin K hydroquinone was 23 microM and the specific epoxidase activity of the carboxylase was 938 pmol vitamin KO/30 min/pmol of carboxylase. Cbx 711, which was also stimulated by proPT18, had a Km for FLEEL, a Km for vitamin K hydroquinone, and a specific epoxidase activity that was comparable to the wild-type carboxylase. In contrast Cbx 572 lacked both carboxylase and epoxidase activities. Although Cbx 676 had a normal carboxylase active site in terms of the Km for FLEEL and its stimulation by proPT18, the Km for vitamin K hydroquinone was 540 microM, and the specific epoxidase activity was 97 pmol KO/30 min/pmol of Cbx 676. The catalytic efficiencies of Cbx 676 for glutamate carboxylation and vitamin K epoxidation were decreased 15- and 400-fold, respectively, from wild-type enzyme reflecting the requirement for formation of an activated vitamin K species for carboxylation to occur. These data indicate that the truncation of COOH-terminal segments of the carboxylase had no effect on FLEEL or propeptide recognition, but in the case of Cbx 676, selectively affected the interaction with vitamin K hydroquinone and the generation of epoxidase activity. These data suggest that a vitamin K epoxidase activity domain may reside near the COOH terminus while the carboxylase active site domain resides toward the NH2 terminus.
View details for Web of Science ID A1995QL58000055
View details for PubMedID 7890642