Whole genome sequencing and mutation rate analysis of trios with paternal dioxin exposure

2018 ◽  
Vol 39 (10) ◽  
pp. 1384-1392 ◽  
Author(s):  
Nguyen Dang Ton ◽  
Hidewaki Nakagawa ◽  
Nguyen Hai Ha ◽  
Nguyen Thuy Duong ◽  
Vu Phuong Nhung ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Mutation Rate ◽  
Whole Genome ◽  
Rate Analysis ◽  
Dioxin Exposure

scholarly journals Whole Genome Sequencing of Mutation Accumulation Lines Reveals a Low Mutation Rate in the Social Amoeba Dictyostelium discoideum

PLoS ONE ◽  
2012 ◽  
Vol 7 (10) ◽  
pp. e46759 ◽  
Author(s):  
Gerda Saxer ◽  
Paul Havlak ◽  
Sara A. Fox ◽  
Michael A. Quance ◽  
Sharu Gupta ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Dictyostelium Discoideum ◽  
Genome Sequencing ◽  
Mutation Rate ◽  
Mutation Accumulation ◽  
Whole Genome ◽  
Social Amoeba ◽  
The Social

scholarly journals Hypermutation signature reveals a slippage and realignment model of translesion synthesis by Rev3 polymerase in cisplatin-treated yeast

2017 ◽  
Vol 114 (10) ◽  
pp. 2663-2668 ◽  
Author(s):  
Romulo Segovia ◽  
Yaoqing Shen ◽  
Scott A. Lujan ◽  
Steven J. M. Jones ◽  
Peter C. Stirling
Keyword(s):  
Dna Repair ◽  
Drug Interactions ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Mutation Rate ◽  
Synergistic Effects ◽  
Translesion Synthesis ◽  
Whole Genome ◽  
Sequencing Analysis ◽  
Template Strand

Gene–gene or gene–drug interactions are typically quantified using fitness as a readout because the data are continuous and easily measured in high throughput. However, to what extent fitness captures the range of other phenotypes that show synergistic effects is usually unknown. UsingSaccharomyces cerevisiaeand focusing on a matrix of DNA repair mutants and genotoxic drugs, we quantify 76 gene–drug interactions based on both mutation rate and fitness and find that these parameters are not connected. Independent of fitness defects, we identified six cases of synthetic hypermutation, where the combined effect of the drug and mutant on mutation rate was greater than predicted. One example occurred when yeast lackingRAD1were exposed to cisplatin, and we characterized this interaction using whole-genome sequencing. Our sequencing results indicate mutagenesis by cisplatin inrad1Δ cells appeared to depend almost entirely on interstrand cross-links at GpCpN motifs. Interestingly, our data suggest that the following base on the template strand dictates the addition of the mutated base. This result differs from cisplatin mutation signatures in XPF-deficientCaenorhabditis elegansand supports a model in which translesion synthesis polymerases perform a slippage and realignment extension across from the damaged base. Accordingly, DNA polymerase ζ activity was essential for mutagenesis in cisplatin-treatedrad1Δ cells. Together these data reveal the potential to gain new mechanistic insights from nonfitness measures of gene–drug interactions and extend the use of mutation accumulation and whole-genome sequencing analysis to define DNA repair mechanisms.

scholarly journals Use of whole genome sequencing to estimate the mutation rate of Mycobacterium tuberculosis during latent infection

2011 ◽  
Vol 43 (5) ◽  
pp. 482-486 ◽  
Author(s):  
Christopher B Ford ◽  
Philana Ling Lin ◽  
Michael R Chase ◽  
Rupal R Shah ◽  
Oleg Iartchouk ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Mutation Rate ◽  
Latent Infection ◽  
Whole Genome

scholarly journals Synthetic hypermutation: gene-drug mutation rate synergy reveals a translesion synthesis mechanism

2016 ◽  
Author(s):  
Romulo Segovia ◽  
Yaoqing Shen ◽  
Scott A. Lujan ◽  
Steven Jones ◽  
Peter C. Stirling
Keyword(s):  
Dna Repair ◽  
Drug Interactions ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Mutation Rate ◽  
Synergistic Effects ◽  
Translesion Synthesis ◽  
Whole Genome ◽  
Sequencing Analysis ◽  
Synthesis Mechanism

ABSTRACTGene-gene or gene-drug interactions are typically quantified using fitness as readout because the data is continuous and easily measured in high-throughput. However, to what extent fitness captures the range of other phenotypes that show synergistic effects is usually unknown. Using Saccharomyces cerevisiae, and focusing on a matrix of DNA repair mutants and genotoxic drugs, we quantify 76 gene-drug interactions based on both mutation rate and fitness and find that these parameters are not necessarily overlapping. Independent of fitness defects we identified six cases of synthetic hypermutation, where the combined effect of the drug and mutant on mutation rate was greater than predicted. One example occurred when yeast lacking RAD1 were exposed to cisplatin and we characterized this interaction using whole-genome sequencing. Our sequencing results indicate mutagenesis by cisplatin in rad1Δ cells appeared to depend almost entirely on interstrand crosslinks at GpCpN motifs. Interestingly, our data suggests that the 3’ base in these motifs templates the addition of the mutated base. This result differs from cisplatin mutation signatures in XPF-deficient C. elegans and supports a model in which translesion synthesis polymerases perform a slippage and realignment extension across from the damaged base. Accordingly, DNA polymerase ζ activity was essential for mutagenesis in cisplatin treated rad1Δ cells. Together these data reveal the potential to gain new mechanistic insights from non-fitness measures of gene-drug interactions and extend the use of mutation accumulation and whole-genome sequencing analysis to define DNA repair mechanisms.

Utility of Whole Genome Sequencing in diagnosing complex disorders: lesson from renal tubular disorders

2018 ◽  
Author(s):  
Mark Stevenson ◽  
Alistair T Pagnamenta ◽  
Heather G Mack ◽  
Judith A Savige ◽  
Kate E Lines ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Whole Genome ◽  
Complex Disorders ◽  
Tubular Disorders ◽  
Renal Tubular Disorders ◽  
Renal Tubular

0306 Exploring the feasibility of using copy number variants as genetic markers through large-scale whole genome sequencing experiments

2016 ◽  
Vol 94 (suppl_5) ◽  
pp. 146-146
Author(s):  
D. M. Bickhart ◽  
L. Xu ◽  
J. L. Hutchison ◽  
J. B. Cole ◽  
D. J. Null ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genetic Markers ◽  
Genome Sequencing ◽  
Copy Number ◽  
Large Scale ◽  
Copy Number Variants ◽  
Whole Genome
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