10 research outputs found
Is Predominant Clonal Evolution a Common Evolutionary Adaptation to Parasitism in Pathogenic Parasitic Protozoa, Fungi, Bacteria, and Viruses?
We propose that predominant clonal evolution (PCE) in microbial pathogens be defined as restrained recombination on an evolutionary scale, with genetic exchange scarce enough to not break the prevalent pattern of clonal population structure. The main features of PCE are (1) strong linkage disequilibrium, (2) the widespread occurrence of stable genetic clusters blurred by occasional bouts of genetic exchange ('near-clades'), (3) the existence of a "clonality threshold", beyond which recombination is efficiently countered by PCE, and near-clades irreversibly diverge. We hypothesize that the PCE features are not mainly due to natural selection but also chiefly originate from in-built genetic properties of pathogens. We show that the PCE model obtains even in microbes that have been considered as 'highly recombining', such as Neisseria meningitidis, and that some clonality features are observed even in Plasmodium, which has been long described as panmictic. Lastly, we provide evidence that PCE features are also observed in viruses, taking into account their extremely fast genetic turnover. The PCE model provides a convenient population genetic framework for any kind of micropathogen. It makes it possible to describe convenient units of analysis (clones and near-clades) for all applied studies. Due to PCE features, these units of analysis are stable in space and time, and clearly delimited. The PCE model opens up the possibility of revisiting the problem of species definition in these organisms. We hypothesize that PCE constitutes a major evolutionary strategy for protozoa, fungi, bacteria, and viruses to adapt to parasitism
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Implementation of preemptive DNA sequence–based pharmacogenomics testing across a large academic medical center: The Mayo-Baylor RIGHT 10K Study
The Mayo-Baylor RIGHT 10K Study enabled preemptive, sequence-based pharmacogenomics (PGx)-driven drug prescribing practices in routine clinical care within a large cohort. We also generated the tools and resources necessary for clinical PGx implementation and identified challenges that need to be overcome. Furthermore, we measured the frequency of both common genetic variation for which clinical guidelines already exist and rare variation that could be detected by DNA sequencing, rather than genotyping.Targeted oligonucleotide-capture sequencing of 77 pharmacogenes was performed using DNA from 10,077 consented Mayo Clinic Biobank volunteers. The resulting predicted drug response–related phenotypes for 13 genes, including CYP2D6 and HLA, affecting 21 drug–gene pairs, were deposited preemptively in the Mayo electronic health record.For the 13 pharmacogenes of interest, the genomes of 79% of participants carried clinically actionable variants in 3 or more genes, and DNA sequencing identified an average of 3.3 additional conservatively predicted deleterious variants that would not have been evident using genotyping.Implementation of preemptive rather than reactive and sequence-based rather than genotype-based PGx prescribing revealed nearly universal patient applicability and required integrated institution-wide resources to fully realize individualized drug therapy and to show more efficient use of health care resources