scholarly journals Massive parallel variant characterization identifies NUDT15 alleles associated with thiopurine toxicity

2019 ◽  
Author(s):  
Chase C. Suiter ◽  
Takaya Moriyama ◽  
Kenneth A. Matreyek ◽  
Wentao Yang ◽  
Emma Rose Scaletti ◽  
...  
Keyword(s):  
Protein Stability ◽  
Poor Performance ◽  
Massively Parallel ◽  
Single Amino Acid ◽  
Clinical Settings ◽  
Structural Elements ◽  
Risk Alleles ◽  
Toxicity Risk ◽  
Hematopoietic Toxicity ◽  
Treatment Individualization

AbstractAs a prototype of genomics-guided precision medicine, individualized thiopurine dosing based on pharmacogenetics is a highly effective way to mitigate hematopoietic toxicity of this class of drugs. Recently, NUDT15 deficiency was identified as a novel genetic cause of thiopurine toxicity, and NUDT15-informed preemptive dose reduction is quickly adopted in clinical settings. To exhaustively identify pharmacogenetic variants in this gene, we developed massively parallel NUDT15 function assays to determine variants’ effect on protein abundance and thiopurine cytotoxicity. Of the 3,097 possible missense variants, we characterized the abundance of 2,922 variants and found 54 hotspot residues at which variants resulted in complete loss of protein stability. Analyzing 2,935 variants in the thiopurine cytotoxicity-based assay, we identified 17 additional residues where variants altered NUDT15 activity without affecting protein stability. We identified structural elements key to NUDT15 stability and/or catalytical activity with single amino-acid resolution. Functional effects for NUDT15 variants accurately predicted toxicity risk alleles in 2,398 patients treated with thiopurines, with 100% sensitivity and specificity, in contrast with poor performance of bioinformatic prediction algorithms. In conclusion, our massively parallel variant function assays identified 1,103 deleterious NUDT15 variants, providing a comprehensive reference of variant function and vastly improving the ability to implement pharmacogenetics-guided thiopurine treatment individualization.

scholarly journals Massively parallel variant characterization identifies NUDT15 alleles associated with thiopurine toxicity

2020 ◽  
Vol 117 (10) ◽  
pp. 5394-5401 ◽  
Author(s):  
Chase C. Suiter ◽  
Takaya Moriyama ◽  
Kenneth A. Matreyek ◽  
Wentao Yang ◽  
Emma Rose Scaletti ◽  
...  
Keyword(s):  
Protein Stability ◽  
Massively Parallel ◽  
Single Amino Acid ◽  
Clinical Settings ◽  
Structural Elements ◽  
Risk Alleles ◽  
Toxicity Risk ◽  
Hematopoietic Toxicity ◽  
Treatment Individualization ◽  
Comprehensive Reference

As a prototype of genomics-guided precision medicine, individualized thiopurine dosing based on pharmacogenetics is a highly effective way to mitigate hematopoietic toxicity of this class of drugs. Recently, NUDT15 deficiency was identified as a genetic cause of thiopurine toxicity, and NUDT15-informed preemptive dose reduction was quickly adopted in clinical settings. To exhaustively identify pharmacogenetic variants in this gene, we developed massively parallel NUDT15 function assays to determine the variants’ effect on protein abundance and thiopurine cytotoxicity. Of the 3,097 possible missense variants, we characterized the abundance of 2,922 variants and found 54 hotspot residues at which variants resulted in complete loss of protein stability. Analyzing 2,935 variants in the thiopurine cytotoxicity-based assay, we identified 17 additional residues where variants altered NUDT15 activity without affecting protein stability. We identified structural elements key to NUDT15 stability and/or catalytical activity with single amino acid resolution. Functional effects for NUDT15 variants accurately predicted toxicity risk alleles in patients treated with thiopurines with far superior sensitivity and specificity compared to bioinformatic prediction algorithms. In conclusion, our massively parallel variant function assays identified 1,152 deleterious NUDT15 variants, providing a comprehensive reference of variant function and vastly improving the ability to implement pharmacogenetics-guided thiopurine treatment individualization.

scholarly journals Massively parallel single-amino-acid mutagenesis

2015 ◽  
Vol 12 (3) ◽  
pp. 203-206 ◽  
Author(s):  
Jacob O Kitzman ◽  
Lea M Starita ◽  
Russell S Lo ◽  
Stanley Fields ◽  
Jay Shendure
Keyword(s):  
Amino Acid ◽  
Massively Parallel ◽  
Single Amino Acid ◽  
Amino Acid Mutagenesis

scholarly journals Multiplex Assessment of Protein Variant Abundance by Massively Parallel Sequencing

2018 ◽  
Author(s):  
Kenneth A. Matreyek ◽  
Lea M. Starita ◽  
Jason J. Stephany ◽  
Beth Martin ◽  
Melissa A. Chiasson ◽  
...  
Keyword(s):  
Amino Acid ◽  
Massively Parallel Sequencing ◽  
Dominant Negative ◽  
Massively Parallel ◽  
Single Amino Acid ◽  
Protein Variant ◽  
Parallel Sequencing ◽  
Missense Variants ◽  
Functional Variants ◽  
Amino Acid Variants

ABSTRACTDetermining the pathogenicity of human genetic variants is a critical challenge, and functional assessment is often the only option. Experimentally characterizing millions of possible missense variants in thousands of clinically important genes will likely require generalizable, scalable assays. Here we describe Variant Abundance by Massively Parallel Sequencing (VAMP-seq), which measures the effects of thousands of missense variants of a protein on intracellular abundance in a single experiment. We apply VAMP-seq to quantify the abundance of 7,595 single amino acid variants of two proteins, PTEN and TPMT, in which functional variants are clinically actionable. We identify 1,079 PTEN and 805 TPMT single amino acid variants that result in low protein abundance, and may be pathogenic or alter drug metabolism, respectively. We observe selection for low-abundance PTEN variants in cancer, and our abundance data suggest that a PTEN variant accounting for ~10% of PTEN missense variants in melanomas functions via a dominant negative mechanism. Finally, we demonstrate that VAMP-seq can be applied to other genes, highlighting its potential as a generalizable assay for characterizing missense variants.

scholarly journals Clinimetric Criteria for Patient-Reported Outcome Measures

2021 ◽  
pp. 1-11
Author(s):  
Danilo Carrozzino ◽  
Chiara Patierno ◽  
Jenny Guidi ◽  
Carmen Berrocal Montiel ◽  
Jianxin Cao ◽  
...  
Keyword(s):  
Outcome Measures ◽  
Subjective Experience ◽  
Rating Scales ◽  
Poor Performance ◽  
Patient Reported Outcome Measures ◽  
Patient Reported Outcome ◽  
Clinical Settings ◽  
Patient Reported ◽  
History Of ◽  
Clinical Measurements

Patient-reported outcome measures (PROMs) are self-rated scales and indices developed to improve the detection of the patients’ subjective experience. Given that a considerable number of PROMs are available, it is important to evaluate their validity and usefulness in a specific research or clinical setting. Published guidelines, based on psychometric criteria, do not fit in with the complexity of clinical challenges, because of their quest for homogeneity of components and inadequate attention to sensitivity. Psychometric theory has stifled the field and led to the routine use of scales widely accepted yet with a history of poor performance. Clinimetrics, the science of clinical measurements, may provide a more suitable conceptual and methodological framework. The aims of this paper are to outline the major limitations of the psychometric model and to provide criteria for clinimetric patient-reported outcome measures (CLIPROMs). The characteristics related to reliability, sensitivity, validity, and clinical utility of instruments are critically reviewed, with particular reference to the differences between clinimetric and psychometric approaches. Of note is the fact that PROMs, rating scales, and indices developed according to psychometric criteria may display relevant clinimetric properties. The present paper underpins the importance of the clini­metric methodology in choosing the appropriate PROMs. CLIPROM criteria may also guide the development of new indices and the validation of existing PROMs to be employed in clinical settings.

scholarly journals Enhancing seismic regulatory compliance practices for non-structural elements in New Zealand

2018 ◽  
Vol 51 (1) ◽  
pp. 47-54
Author(s):  
Temitope Egbelakin ◽  
Itohan E. Yakubu ◽  
Justin Bowden
Keyword(s):  
New Zealand ◽  
Construction Industry ◽  
Poor Performance ◽  
Regulatory Compliance ◽  
Ease Of Use ◽  
Performance Standard ◽  
Structural Elements ◽  
Face To Face ◽  
One Stop ◽  
Improved Performance

Most non-structural elements (NSEs) including ceilings, piping, services equipment and cladding systems, etc., are typically prone to failure in the event of relatively low to medium earthquake shakings. The poor performance of NSEs demonstrated in recent earthquake events in New Zealand has revealed a gap in NSE design and construction practices, especially regarding compliance with the NSE performance standard (NZS 4219:2009). This study sought to examine the NZ 4219:2009 and compliance in New Zealand’s construction industry, towards improving the performance of NSEs during earthquakes.Using a face-to-face interview enquiry technique, findings from this study revealed that although majority of the participants consider the NZS 4219:2009 to be very important in improving the performance of NSEs during earthquakes, some shortcomings were also identified: (i) non-compliance with the NZ 4219:2009 by construction professionals; (ii) exclusion of guidelines for specific NSEs from the scope of the NZS 4219:2009; (iii) poor ease of use of the NZS 4219:2009 and other relevant excluded NSE guidelines; and (iv) lack of clarity in the NZS 4219:2009 regarding attribution of ultimate design responsibility for NSE seismic coordination. As a recommendation, the establishment of a robust, simple-to-use seismic specification document that will provide one-stop specifications for the design and installation of NSEs could be a possible solution to promoting strong compliance practices within the New Zealand construction industry, towards achieving improved performance of NSEs during earthquakes.

scholarly journals Quantifying the Evolutionary Constraints and Potential of Hepatitis C Virus NS5A Protein

mSystems ◽  
2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Lei Dai ◽  
Yushen Du ◽  
Hangfei Qi ◽  
Christian D. Huber ◽  
Dongdong Chen ◽  
...  
Keyword(s):  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Protein Stability ◽  
Viral Evolution ◽  
Antiviral Drug ◽  
Single Amino Acid ◽  
Fitness Costs ◽  
Adaptive Potential ◽  
Fitness Effects ◽  
Ns5a Protein

ABSTRACT RNA viruses, such as hepatitis C virus (HCV), influenza virus, and SARS-CoV-2, are notorious for their ability to evolve rapidly under selection in novel environments. It is known that the high mutation rate of RNA viruses can generate huge genetic diversity to facilitate viral adaptation. However, less attention has been paid to the underlying fitness landscape that represents the selection forces on viral genomes, especially under different selection conditions. Here, we systematically quantified the distribution of fitness effects of about 1,600 single amino acid substitutions in the drug-targeted region of NS5A protein of HCV. We found that the majority of nonsynonymous substitutions incur large fitness costs, suggesting that NS5A protein is highly optimized. The replication fitness of viruses is correlated with the pattern of sequence conservation in nature, and viral evolution is constrained by the need to maintain protein stability. We characterized the adaptive potential of HCV by subjecting the mutant viruses to selection by the antiviral drug daclatasvir at multiple concentrations. Both the relative fitness values and the number of beneficial mutations were found to increase with the increasing concentrations of daclatasvir. The changes in the spectrum of beneficial mutations in NS5A protein can be explained by a pharmacodynamics model describing viral fitness as a function of drug concentration. Overall, our results show that the distribution of fitness effects of mutations is modulated by both the constraints on the biophysical properties of proteins (i.e., selection pressure for protein stability) and the level of environmental stress (i.e., selection pressure for drug resistance). IMPORTANCE Many viruses adapt rapidly to novel selection pressures, such as antiviral drugs. Understanding how pathogens evolve under drug selection is critical for the success of antiviral therapy against human pathogens. By combining deep sequencing with selection experiments in cell culture, we have quantified the distribution of fitness effects of mutations in hepatitis C virus (HCV) NS5A protein. Our results indicate that the majority of single amino acid substitutions in NS5A protein incur large fitness costs. Simulation of protein stability suggests viral evolution is constrained by the need to maintain protein stability. By subjecting the mutant viruses to selection under an antiviral drug, we find that the adaptive potential of viral proteins in a novel environment is modulated by the level of environmental stress, which can be explained by a pharmacodynamics model. Our comprehensive characterization of the fitness landscapes of NS5A can potentially guide the design of effective strategies to limit viral evolution.

scholarly journals Erratum: Corrigendum: Massively parallel single-amino-acid mutagenesis

2017 ◽  
Vol 14 (5) ◽  
pp. 540-540
Author(s):  
Jacob O Kitzman ◽  
Lea M Starita ◽  
Russell S Lo ◽  
Stanley Fields ◽  
Jay Shendure
Keyword(s):  
Amino Acid ◽  
Massively Parallel ◽  
Single Amino Acid ◽  
Amino Acid Mutagenesis

scholarly journals Discordance between different bioinformatic methods for identifying resistance genes from short-read genomic data, with a focus on Escherichia coli

2021 ◽  
Author(s):  
Timothy J Davies ◽  
Jeremy Swann ◽  
Anna E Sheppard ◽  
Hayleah Pickford ◽  
Samuel Lipworth ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Random Sequence ◽  
Real Life ◽  
Poor Performance ◽  
Real Data ◽  
Clinical Settings ◽  
Large Set ◽  
Gene Variants ◽  
E Coli ◽  
Gene Profiles

Several bioinformatics genotyping algorithms are now commonly used to characterise antimicrobial resistance (AMR) gene profiles in whole genome sequencing (WGS) data, with a view to understanding AMR epidemiology and developing resistance prediction workflows using WGS in clinical settings. Accurately evaluating AMR in Enterobacterales, particularly Escherichia coli, is of major importance, because this is a common pathogen. However, robust comparisons of different genotyping approaches on relevant simulated and large real-life WGS datasets are lacking. Here, we used both simulated datasets and a large set of real E. coli WGS data (n=1818 isolates) to systematically investigate genotyping methods in greater detail. Simulated constructs and real sequences were processed using four different bioinformatic programs (ABRicate, ARIBA, KmerResistance, and SRST2, run with the ResFinder database) and their outputs compared. For simulations tests where 3,092 AMR gene variants were inserted into random sequence constructs, KmerResistance was correct for all 3,092 simulations, ABRicate for 3,082 (99.7%), ARIBA for 2,927 (94.7%) and SRST2 for 2,120 (68.6%). For simulations tests where two closely related gene variants were inserted into random sequence constructs, ABRicate identified the correct alleles in 11,382/46,279 (25%) of simulations, ARIBA in 2494/46,279 (5%), SRST in 2539/46,279 (5%) and KmerResistance in 38,826/46,279 (84%). In real data, across all methods, 1392/1818 (76%) isolates had discrepant allele calls for at least one gene. Our evaluations revealed poor performance in scenarios that would be expected to be challenging (e.g. identification of AMR genes at <10x coverage, discriminating between closely related AMR gene sequences), but also identified systematic sequence classification (i.e. naming) errors even in straightforward circumstances, which contributed to 1081/3092 (35%) errors in our most simple simulations and at least 2530/4321 (59%) discrepancies in real data. Further, many of the remaining discrepancies were likely artefactual, with reporting cut-off differences accounted for at least 1430/4321 (33%) discrepants. Comparing outputs generated by running multiple algorithms on the same dataset can help identify and resolve these artefacts, but ideally new and more robust genotyping algorithms are needed.

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