Induction of DNA Damage and G₂ Cell Cycle Arrest by Diepoxybutane through the Activation of the Chk1-Dependent Pathway in Mouse Germ Cells

1,2:3,4-Diepoxybutane (DEB) is a major carcinogenic metabolite of 1,3-butadiene (BD), which has been shown to cause DNA strand breaks in cells through its potential genotoxicity. The adverse effect of DEB on male reproductive cells in response to DNA damage has not been thoroughly studied, and the related mechanism is yet to be elucidated. Using mouse spermatocyte-derived GC-2 cells, we demonstrated in the present study that DEB caused the proliferation inhibition and marked cell cycle arrest at the G₂ phase but not apoptosis. DEB also induced DNA damage as evidenced by γ-H2AX expression, the comet assay, and the cytokinesis-block micronucleus assay. Meanwhile, DEB triggered the Chk1/Cdc25c/Cdc2 signal pathway, which could be abated in the presence of UCN-01 or Chk1 siRNA. GC-2 cells exposed to DEB experienced ROS generation and pretreatment of <i>N</i>-acetyl-l-cysteine, partly attenuated DEB-induced DNA damage, and G₂ arrest. Furthermore, measurement of testicular cells showed an increased proportion of tetraploid cells in mice administrated with DEB, alongside the enhanced expression of p-Chk1. Also, the defective reproductive phenotypes, including reduced sperm motility, increased sperm malformation, and histological abnormality of testes, were observed. In conclusion, these results suggest DEB induces DNA damage and G₂ cell cycle arrest by activating the Chk1-dependent pathway, while oxidative stress may be associated with eliciting toxicity in male reproductive cells

Data_Sheet_1_Evaluation of Chlamydia trachomatis screening from the perspective of health economics: a systematic review.docx

BackgroundMost Chlamydia trachomatis (CT) infections are asymptomatic. The infection can persist and lead to severe sequelae. Therefore, screening for CT can primarily prevent serious sequelae.AimTo systematically evaluate CT screening from the perspective of health economics, summarize previous findings from different target populations, and make practical recommendations for developing local CT screening strategies.MethodsPubMed, Web of Science, Embase, Cochran Library, and National Health Service Economic Evaluation Database (Ovid) were searched from January 1, 2000, to March 4, 2023. Studies reporting the cost-effectiveness, cost-benefit, or cost-utility of CT screening were eligible to be included. A narrative synthesis was used to analyze and report the results following the PRISMA guidelines. The Consensus on Health Economic Criteria (CHEC) list was used to assess the methodological quality of included studies.ResultsOur review finally comprised 39 studies addressing four populations: general sexually active people (n = 25), pregnant women (n = 4), women attending STD and abortion clinics (n = 4), and other high-risk individuals (n = 6). The total number of participants was ~7,991,198. The majority of studies assessed the cost-effectiveness or cost-utility of the screening method. The results showed that the following screening strategies may be cost-effective or cost-saving under certain conditions: performing CT screening in young people aged 15–24 in the general population, military recruits, and high school students; incorporating CT screening into routine antenatal care for pregnant women aged 15–30; opportunistic CT screening for women attending STD and abortion clinics; home-obtained sampling for CT screening using urine specimens or vaginal swab; performing CT screening for 14–30-year-old people who enter correctional institutions (i.e., jail, detention) as soon as possible; providing CT screening for female sex workers (FSWs) based on local incidence and prevalence; adding routine CT screening to HIV treatment using rectal samples from men who have sex with men (MSM).ConclusionWe found that CT screening in general sexually active people aged 15–24, military recruits, high school students, pregnant women aged 15–30, women attending STD and abortion clinics, people entering jail, detention, FSWs, and MSM has health economic value. Due to the different prevalence of CT, diversities of economic conditions, and varying screening costs among different populations and different countries, regions, or settings, no uniform and standard screening strategies are currently available. Therefore, each country should consider its local condition and the results of health economic evaluations of CT screening programs in that country to develop appropriate CT screening strategies.</p

Additional file 1 of Transcriptome-wide identification and characterization of genes exhibit allele-specific imprinting in maize embryo and endosperm

Supplementary Material

Additionnal file 2: Table S4.

Regions with 3 fold higher recombinant ratio than genome average. D_Downstream, D_Exon, D_Intron, D_Upstream, D_Intergenic: SNP density in 2 k–downstream, exon, intron, 2 k–upstream of genic regions and SNP density in intergenic regions. (XLSX 21 kb

Additional file 1: Figure S1. of Identification of minor effect QTLs for plant architecture related traits using super high density genotyping and large recombinant inbred population in maize (Zea mays)

Distribution of SNPs polymorphic between Zheng58 and Chang7–2 in different genomic regions. Figure S2. Sequencing depth profile of Zhengdan958 RILs. Figure S3. Distribution of false GBS SNPs in 1 Mb windows by parent. Figure S4. Pair-wise recombinational fractions (upper left) and LOD scores (lower right) of the bins. Figure S5. The distribution of segregation distortions across ten chromosomes.Segregation distortions were tested by Chi-test, and –log10 (PChi-test) were plotted against their physical positions. Threshold of no distortion (p < 0.01, after Bonferroni-correction) were showed as red dashed lines. Figure S6. The 5 classes of silk color. Figure S7. Phenotypic distributions of PH, EH, EH/PH, TNB, TL and ULN. Table S1. The 240 barcodes used in GBS. Table S2. GBS error rate for parental lines. Table S3. Pair-wise Pearson’s correlation coefficients (lower left) and p-values (upper right) among recombination, gene density, and SNP density in 1 Mb intervals by genomic region. Table S5. Pearson correlation coefficients among different traits in two environments.Lower left, the correlation coefficients; Upper right, p-values of correlation test. Table S6. Heritability of different traits. Table S7. Summary of mapped QTLs. Table S8. Markers used for verifying qPH1a. (PDF 934 kb