Browsing by Author "Miller, Neil A."

Now showing 1 - 3 of 3
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    An integrated clinico-metabolomic model improves prediction of death in sepsis
    (American Association for the Advancement of Science, 2013) Langley, Raymond J.; Tsalik, Ephraim L.; van Velkinburgh, Jennifer C.; Glickman, Seth W.; Rice, Brandon J.; Wang, Chunping; Chen, Bo; Carin, Lawrence; Suarez, Arturo; Mohney, Robert P.; Freeman, Debra H.; Wang, Mu; You, Jinsam; Wulff, Jacob; Thompson, J. Will; Moseley, M. Arthur; Reisinger, Stephanie; Edmonds, Brian T.; Grinnell, Brian; Nelson, David R.; Dinwiddie, Darrell L.; Miller, Neil A.; Saunders, Carol J.; Soden, Sarah S.; Rogers, Angela J.; Gazourian, Lee; Fredenburgh, Laura E.; Massaro, Anthony F.; Baron, Rebecca M.; Choi, Augustine M. K.; Corey, G. Ralph; Ginsburg, Geoffrey S.; Cairns, Charles B.; Otero, Ronny M.; Fowler, Vance G., Jr.; Rivers, Emanuel P.; Woods, Christopher W.; Kingsmore, Stephen F.; Medicine, School of Medicine
    Sepsis is a common cause of death, but outcomes in individual patients are difficult to predict. Elucidating the molecular processes that differ between sepsis patients who survive and those who die may permit more appropriate treatments to be deployed. We examined the clinical features and the plasma metabolome and proteome of patients with and without community-acquired sepsis, upon their arrival at hospital emergency departments and 24 hours later. The metabolomes and proteomes of patients at hospital admittance who would ultimately die differed markedly from those of patients who would survive. The different profiles of proteins and metabolites clustered into the following groups: fatty acid transport and β-oxidation, gluconeogenesis, and the citric acid cycle. They differed consistently among several sets of patients, and diverged more as death approached. In contrast, the metabolomes and proteomes of surviving patients with mild sepsis did not differ from survivors with severe sepsis or septic shock. An algorithm derived from clinical features together with measurements of five metabolites predicted patient survival. This algorithm may help to guide the treatment of individual patients with sepsis.
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    CYP2C8, CYP2C9, and CYP2C19 Characterization Using Next-Generation Sequencing and Haplotype Analysis: A GeT-RM Collaborative Project
    (Elsevier, 2022) Gaedigk, Andrea; Boone, Erin C.; Scherer, Steven E.; Lee, Seung-Been; Numanagić, Ibrahim; Sahinalp, Cenk; Smith, Joshua D.; McGee, Sean; Radhakrishnan, Aparna; Qin, Xiang; Wang, Wendy Y.; Farrow, Emily G.; Gonzaludo, Nina; Halpern, Aaron L.; Nickerson, Deborah A.; Miller, Neil A.; Pratt, Victoria M.; Kalman, Lisa V.; Medical and Molecular Genetics, School of Medicine
    Pharmacogenetic tests typically target selected sequence variants to identify haplotypes that are often defined by star (∗) allele nomenclature. Due to their design, these targeted genotyping assays are unable to detect novel variants that may change the function of the gene product and thereby affect phenotype prediction and patient care. In the current study, 137 DNA samples that were previously characterized by the Genetic Testing Reference Material (GeT-RM) program using a variety of targeted genotyping methods were recharacterized using targeted and whole genome sequencing analysis. Sequence data were analyzed using three genotype calling tools to identify star allele diplotypes for CYP2C8, CYP2C9, and CYP2C19. The genotype calls from next-generation sequencing (NGS) correlated well to those previously reported, except when novel alleles were present in a sample. Six novel alleles and 38 novel suballeles were identified in the three genes due to identification of variants not covered by targeted genotyping assays. In addition, several ambiguous genotype calls from a previous study were resolved using the NGS and/or long-read NGS data. Diplotype calls were mostly consistent between the calling algorithms, although several discrepancies were noted. This study highlights the utility of NGS for pharmacogenetic testing and demonstrates that there are many novel alleles that are yet to be discovered, even in highly characterized genes such as CYP2C9 and CYP2C19.
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    PharmVar and the Landscape of Pharmacogenetic Resources
    (Wiley, 2020-01) Gaedigk, Andrea; Whirl-Carrillo, Michelle; Pratt, Victoria M.; Miller, Neil A.; Klein, Teri E.; Medical and Molecular Genetics, School of Medicine
    Testing, reporting and translation of pharmacogenetics (PGx) into clinical recommendations requires vast knowledge resources. The Pharmacogene Variation (PharmVar) Consortium catalogs pharmacogene variation and provides standardized nomenclature that is utilized by the Pharmacogenomics Knowledgebase (PharmGKB) and the Clinical Pharmacogenetic Implementation Consortium (CPIC). PharmVar allele definitions are also widely used for test design and reporting. This perspective paints a landscape of PGx resources that are needed to facilitate implementation of PGx into clinical practice.