Scheme of <i>PIG-O</i> mutation assay in DT40 cells.

<p>Scheme of <i>PIG-O</i> mutation assay in DT40 cells.</p

Spontaneous mutational spectrum of <i>PIG-O</i> gene in DT40 cells.

<p>Spontaneous mutational spectrum of <i>PIG-O</i> gene in DT40 cells.</p

Primers for the RT-PCR and Sequencing of Chicken <i>PIG-O</i> cDNA.

<p><b>^a</b>Primer annealing sites relative to the A of the ATG initiation codon (XM_001232869).</p

Frequency of GPI anchor-deficient DT40 cells exposed to MMS.

<p>(A) The effect of 24-hour MMS exposure at 0, 1, 3, 10, 30, or 100 µM on cell growth of DT40 cells was evaluated over three days after MMS exposure. The cell growth assay was performed during/after MMS treatment. Each point represents the mean and S.D. (bars) from three independent experiments. (B) The length of the phenotype expression period in the PA^r DT40 cells was optimized after 100 µM MMS treatment for 24 hours. The frequency of PA^r DT40 cells was determined before and after MMS treatment. Each point represents the mean and S.D. (bars) from at least three independent experiments. (C) Frequency of PA^r DT40 cells was determined after exposure to MMS at different concentrations. DT40 cells were exposed to MMS at 0, 1, 3, 10, 30, 40, 60, and 100 µM for 24 hours. The cells were further cultured for five days in fresh medium without MMS. The frequency of PA^r DT40 cells was determined for each group. Each point represents the mean and S.D. (bars) from at least three independent experiments. P<0.05. Discontinued line shows the mean and S.D. of mutational frequency in the control samples (0.5±0.8 mutants/10⁶ cells).</p

Characterization of PA-resistant (PA^r) DT40 cells and validation of PA selection-based GPI anchor-deficient cell detection assay.

<p>(<b>A</b>) In a low cell density experiment using a 24-well plate (2.5×10³ cells/250 µL/well), the intact DT40 cells and six different clones of DT40 cells that survived from the first PA treatment at 1.2 nM were exposed to PA (0.0221–1.2 nM). After a three-day cultivation, cell viability was determined by XTT. Each point represents the mean and S.D. (bars) from three independent experiments for DT40 cells and single experiment for six different clones of DT40 cells resistant to PA. (<b>B</b>) Using one of the PA^r clones used for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033563#pone-0033563-g003" target="_blank">Figure 3A</a>, different numbers of PA^r cells (0 to 80 cells/plate) were seeded onto 96-well plates containing intact DT40 cells (40×10³ cells/well) to validate the accuracy of the PA selection step of the assay. The cells were exposed to PA at 1.2 nM. After a seven-day incubation, colony formation was scored visually using an inverted microscope. Plating efficiency was also determined using PA^r cells.</p

Mutational spectrum of <i>PIG-O</i> gene in DT40 cells exposed to MMS at 100 µM.

<p>Mutational spectrum of <i>PIG-O</i> gene in DT40 cells exposed to MMS at 100 µM.</p

Molecular Dosimetry of Endogenous and Exogenous O⁶‑Methyl-dG and N7-Methyl‑G Adducts Following Low Dose [<i>D</i>₃]‑Methylnitrosourea Exposures in Cultured Human Cells

For DNA-reactive chemicals, a low dose linear assessment of cancer risk is the science policy default. In the present study, we quantitated the endogenous and exogenous N7-methyl-G and O⁶-methyl-dG adducts in human lymphoblastoid cells exposed to low dose [<i>D</i>₃]-methylnitrosourea. Endogenous amounts of both adducts remained nearly constant, while the exogenous adducts showed linear dose-responses. The data show that O⁶-methyl-dG adducts ≥1.8/10⁸ dG correlated with published studies that demonstrated significant increases of mutations under these conditions. The combined results do not support linear extrapolations to zero when data are available for science-based regulations

Dosimetry of <i>N</i>⁶‑Formyllysine Adducts Following [¹³C²H₂]‑Formaldehyde Exposures in Rats

With formaldehyde as the major source of endogenous <i>N</i>⁶-formyllysine protein adducts, we quantified endogenous and exogenous <i>N</i>⁶-formyllysine in the nasal epithelium of rats exposed by inhalation to 0.7, 2, 5.8, and 9.1 ppm [¹³C²H₂]-formaldehyde using liquid chromatography-coupled tandem mass spectrometry. Exogenous <i>N</i>⁶-formyllysine was detected in the nasal epithelium, with concentration-dependent formation in total as well as fractionated (cytoplasmic, membrane, nuclear) proteins, but was not detected in the lung, liver, or bone marrow. Endogenous adducts dominated at all exposure conditions, with a 6 h 9.1 ppm formaldehyde exposure resulting in one-third of the total load of <i>N</i>⁶-formyllysine being derived from exogenous sources. The results parallel previous studies of formaldehyde-induced DNA adducts

<i>N</i>⁶‑Formyllysine as a Biomarker of Formaldehyde Exposure: Formation and Loss of <i>N</i>⁶‑Formyllysine in Nasal Epithelium in Long-Term, Low-Dose Inhalation Studies in Rats

Exposure to both endogenous and exogenous formaldehyde has been established to be carcinogenic, likely by virtue of forming nucleic acid and proteins adducts such as <i>N</i>⁶-formyllysine. To better assess <i>N</i>⁶-formyllysine as a biomarker of formaldehyde exposure, we studied accumulation of <i>N</i>⁶-formyllysine adducts in tissues of rats exposed by inhalation to 2 ppm [¹³C²H₂]-formaldehyde for 7, 14, 21, and 28 days (6 h/day) and investigated adduct loss over a 7-day postexposure period using liquid chromatography-coupled tandem mass spectrometry. Our results showed formation of exogenous adducts in nasal epithelium and to some extent in trachea but not in distant tissues of lung, bone marrow, or white blood cells, with a 2-fold increase over endogenous <i>N</i>⁶-formyllysine over a 3-week exposure period. Postexposure analyses indicated a biexponential decay of <i>N</i>⁶-formyllysine in proteins extracted from different cellular compartments, with half-lives of ∼25 and ∼182 h for the fast and slow phases, respectively, in cytoplasmic proteins. These results parallel the behavior of DNA adducts and DNA–protein cross-links, with protein adducts cleared faster than DNA–protein cross-links, and point to the potential utility of <i>N</i>⁶-formyllysine protein adducts as biomarkers of formaldehyde

Gut Microbiome Phenotypes Driven by Host Genetics Affect Arsenic Metabolism

Large individual differences in susceptibility to arsenic-induced diseases are well-documented and frequently associated with different patterns of arsenic metabolism. In this context, the role of the gut microbiome in directly metabolizing arsenic and triggering systemic responses in diverse organs raises the possibility that gut microbiome phenotypes affect the spectrum of metabolized arsenic species. However, it remains unclear how host genetics and the gut microbiome interact to affect the biotransformation of arsenic. Using an integrated approach combining 16S rRNA gene sequencing and HPLC-ICP-MS arsenic speciation, we demonstrate that IL-10 gene knockout leads to a significant taxonomic change of the gut microbiome, which in turn substantially affects arsenic metabolism