Clinical pharmacogenetic analysis in 5,001 individuals with diagnostic Exome Sequencing data.

Exome sequencing is utilized in routine clinical genetic diagnosis. The technical robustness of repurposing large-scale next-generation sequencing data for pharmacogenetics has been demonstrated, supporting the implementation of preemptive pharmacogenetic strategies based on adding clinical pharmacogenetic interpretation to exomes. However, a comprehensive study analyzing all actionable pharmacogenetic alleles contained in international guidelines and applied to diagnostic exome data has not been performed. Here, we carried out a systematic analysis based on 5001 Spanish or Latin American individuals with diagnostic exome data, either Whole Exome Sequencing (80%), or the so-called Clinical Exome Sequencing (20%) (60 Mb and 17 Mb, respectively), to provide with global and gene-specific clinical pharmacogenetic utility data. 788 pharmacogenetic alleles, distributed through 19 genes included in Clinical Pharmacogenetics Implementation Consortium guidelines were analyzed. We established that Whole Exome and Clinical Exome Sequencing performed similarly, and 280 alleles in 11 genes (CACNA1S, CYP2B6, CYP2C9, CYP4F2, DPYD, G6PD, NUDT15, RYR1, SLCO1B1, TPMT, and UGT1A1) could be used to inform of pharmacogenetic phenotypes that change drug prescription. Each individual carried in average 2.2 alleles and overall 95% (n = 4646) of the cohort could be informed of at least one actionable pharmacogenetic phenotype. Differences in variant allele frequency were observed among the populations studied and the corresponding gnomAD population for 7.9% of the variants. In addition, in the 11 selected genes we uncovered 197 novel variants, among which 27 were loss-of-function. In conclusion, we provide with the landscape of actionable pharmacogenetic information contained in diagnostic exomes, that can be used preemptively in the clinics.

A Novel Approach for the Identification of Pharmacogenetic Variants in MT-RNR1 through Next-Generation Sequencing Off-Target Data.

Specific genetic variants in the mitochondrially encoded 12S ribosomal RNA gene (MT-RNR1) cause aminoglycoside-induced irreversible hearing loss. Mitochondrial DNA is usually not included in targeted sequencing experiments; however, off-target data may deliver this information. Here, we extract MT-RNR1 genetic variation, including the most relevant ototoxicity variant m.1555A>G, using the off-target reads of 473 research samples, sequenced through a capture-based, custom-targeted panel and whole exome sequencing (WES), and of 1245 diagnostic samples with clinical WES. Sanger sequencing and fluorescence-based genotyping were used for genotype validation. There was a correlation between off-target reads and mitochondrial coverage (rcustomPanel = 0.39, p = 2 × 10-13 and rWES = 0.67, p = 7 × 10-21). The median read depth of MT-RNR1 m.1555 was similar to the average mitochondrial genome coverage, with saliva and blood samples giving comparable results. The genotypes from 415 samples, including three m.1555G carriers, were concordant with fluorescence-based genotyping data. In clinical WES, median MT-RNR1 coverage was 56×, with 90% of samples having ≥20 reads at m.1555 position, and one m.1494T and three m.1555G carriers were identified with no evidence for heteroplasmy. Altogether, this study shows that obtaining MT-RNR1 genotypes through off-target reads is an efficient strategy that can impulse preemptive pharmacogenetic screening of this mitochondrial gene.

An Implementation-Focused Bio/Algorithmic Workflow for Synthetic Biology.

As synthetic biology moves away from trial and error and embraces more formal processes, workflows have emerged that cover the roadmap from conceptualization of a genetic device to its construction and measurement. This latter aspect (i.e., characterization and measurement of synthetic genetic constructs) has received relatively little attention to date, but it is crucial for their outcome. An end-to-end use case for engineering a simple synthetic device is presented, which is supported by information standards and computational methods and focuses on such characterization/measurement. This workflow captures the main stages of genetic device design and description and offers standardized tools for both population-based measurement and single-cell analysis. To this end, three separate aspects are addressed. First, the specific vector features are discussed. Although device/circuit design has been successfully automated, important structural information is usually overlooked, as in the case of plasmid vectors. The use of the Standard European Vector Architecture (SEVA) is advocated for selecting the optimal carrier of a design and its thorough description in order to unequivocally correlate digital definitions and molecular devices. A digital version of this plasmid format was developed with the Synthetic Biology Open Language (SBOL) along with a software tool that allows users to embed genetic parts in vector cargoes. This enables annotation of a mathematical model of the device's kinetic reactions formatted with the Systems Biology Markup Language (SBML). From that point onward, the experimental results and their in silico counterparts proceed alongside, with constant feedback to preserve consistency between them. A second aspect involves a framework for the calibration of fluorescence-based measurements. One of the most challenging endeavors in standardization, metrology, is tackled by reinterpreting the experimental output in light of simulation results, allowing us to turn arbitrary fluorescence units into relative measurements. Finally, integration of single-cell methods into a framework for multicellular simulation and measurement is addressed, allowing standardized inspection of the interplay between the carrier chassis and the culture conditions.