Genome-based prediction of test cross performance in two subsequent breeding cycles

Genome-based prediction of genetic values is expected to overcome shortcomings that limit the application of QTL mapping and marker-assisted selection in plant breeding. Our goal was to study the genome-based prediction of test cross performance with genetic effects that were estimated using genotypes from the preceding breeding cycle. In particular, our objectives were to employ a ridge regression approach that approximates best linear unbiased prediction of genetic effects, compare cross validation with validation using genetic material of the subsequent breeding cycle, and investigate the prospects of genome-based prediction in sugar beet breeding. We focused on the traits sugar content and standard molasses loss (ML) and used a set of 310 sugar beet lines to estimate genetic effects at 384 SNP markers. In cross validation, correlations >0.8 between observed and predicted test cross performance were observed for both traits. However, in validation with 56 lines from the next breeding cycle, a correlation of 0.8 could only be observed for sugar content, for standard ML the correlation reduced to 0.4. We found that ridge regression based on preliminary estimates of the heritability provided a very good approximation of best linear unbiased prediction and was not accompanied with a loss in prediction accuracy. We conclude that prediction accuracy assessed with cross validation within one cycle of a breeding program can not be used as an indicator for the accuracy of predicting lines of the next cycle. Prediction of lines of the next cycle seems promising for traits with high heritabilities.     

Pathways affected by asbestos exposure in normal and tumour tissue of lung cancer patients

Background:

The early detection of prostate cancer has resulted in an increase in the number of patients with localized prostate cancer and has paralleled the reported reduction in prostate cancer mortality. The increased rate of detection of patients with localized prostate cancer may also increase the risk of potentially morbid therapy in a patient with indolent cancer. Defining the biomarker correlates of prostate cancer virulence will facilitate the appropriate application and development of therapy for patients with early disease.

Methods:

A 255 core prostate cancer tissue microarray (TMA) from 47 prostatectomy specimens with organ confined tumor was constructed. Prostate cancer foci of transition and peripheral zone origin were represented on the TMA. Further, replicate cores of the two Gleason grades comprising the Gleason score, representative of Gleason scores 5-9, were arrayed from each prostatectomy specimen. Standard immunohistochemical techniques were used to assess expression of nine, cell death and cell cycle regulatory proteins implicated in the pathogenesis of prostate cancer (bax, bcl-2, bcl-x_L, bin1, CD95, mdm2, p21, p53, and NFkB).

Results:

The Spearman correlation coefficient revealed a strong correlation of bax, bin1, FAS, p65 and p21 expression with Gleason grade. Spearman correlation coefficients showed that expression of, bax and bin1, bax and MDM2, Bax and p21, and bax and p65 NFkB was highly associated. Other significant associations were identified between bin1 and p21, bin1 and MDM2, bin1 and p65 NFkB and between p21 and p65 NFκB. A model for predicting the biological potential of Gleason score 7 prostate cancer using multivariable logistic regression methods was developed. The findings also indicate that the profile of specific markers for Gleason grade 3 prostate cancer correlates with the overall context of the Gleason score.

Conclusion:

These data support the view that important molecular differences exist among and between the Gleason scores. Furthermore, there is significant molecular heterogeneity among prostatectomy specimens containing Gleason grade 3 cancer. This observation may have broader implications regarding the determination of risk among patients with prostate cancer that is currently considered to be of either good prognosis or unclear prognosis, i.e. Gleason score 7 tumors.     

CYP1A1, GSTM1 and GSTT1 polymorphisms and lung cancer: a pooled analysis of gene-gene interactions

Gene-environment interactions have been extensively studied in lung cancer. It is likely that several genetic polymorphisms cooperate in increasing the individual risk. Therefore, the study of gene-gene interactions might be important to identify high-susceptibility subgroups. GSEC is an initiative aimed at collecting available data sets on metabolic polymorphisms and the risks of cancer at several sites and performing pooled analyses of the original data. Authors of published papers have provided original data sets. The present paper refers to gene-gene interactions in lung cancer and considers three polymorphisms in three metabolic genes: CYP1A1, GSTM1 and GSTT1. The present analyses compare the gene-gene interactions of the CYP1A1*2A, GSTM1 and GSTT1 polymorphisms from studies on lung cancer conducted in Europe and the USA between 1991 and 2000. Only Caucasians have been included. The data set includes 1466 cases and 1488 controls. The only clear-cut association was found with CYP1A1*2A. This association remained unchanged after stratification by polymorphisms in other genes (with an odds ratio [OR] of approximately 2.5), except when interaction with GSTM1 was considered. When the OR for CYP1A1*2A was stratified according to the GSTM1 genotype, the OR was increased only among the subjects who had the null (homozygous deletion) GSTM1 genotype (OR=2.8, 95% CI=0.9-8.4). The odds ratio for the interactive term (CYP1A1*2A by GSTM1) in logistic regression was 2.7 (95% CI=0.5-15.3). An association between lung cancer and the homozygous CYP1A1*2A genotype is confirmed. An apparent and biologically plausible interaction is suggested between this genotype and GSTM1.     

Large-scale identification of essential Salmonella genes by trapping lethal insertions

A novel screening approach based on insertion-duplication mutagenesis (IDM) was established to efficiently screen for essential genes of Salmonella enterica serovar Typhimurium under laboratory conditions. Small, randomly generated genomic fragments were cloned into a conditionally replicating vector, and the resulting library of single Salmonella clones was grown under permissive conditions. Upon switching to non-permissive temperature, discrimination between lethal and non-lethal insertions following homologous recombination allowed the trapping of genes with essential functions. Further characterization of a total of 498 fragments resulting in such lethal knockout revealed 145 known essential genes and 112 functionally characterized or hypothetical genes not yet shown to encode essential genes, among them three Salmonella-specific genes. The essentiality was demonstrated for a prioritised set of 15 putative indispensable genes by creating conditional lethal phenotypes. The results of this large-scale screening indicate that in rich media, the class of Salmonella genes indispensable for growth is composed of approximately 490 genes.     

Crystal structures of two novel dye-decolorizing peroxidases reveal a beta-barrel fold with a conserved heme-binding motif

BtDyP from Bacteroides thetaiotaomicron (strain VPI-5482) and TyrA from Shewanella oneidensis are dye-decolorizing peroxidases (DyPs), members of a new family of heme-dependent peroxidases recently identified in fungi and bacteria. Here, we report the crystal structures of BtDyP and TyrA at 1.6 and 2.7 A, respectively. BtDyP assembles into a hexamer, while TyrA assembles into a dimer; the dimerization interface is conserved between the two proteins. Each monomer exhibits a two-domain, alpha+beta ferredoxin-like fold. A site for heme binding was identified computationally, and modeling of a heme into the proposed active site allowed for identification of residues likely to be functionally important. Structural and sequence comparisons with other DyPs demonstrate a conservation of putative heme-binding residues, including an absolutely conserved histidine. Isothermal titration calorimetry experiments confirm heme binding, but with a stoichiometry of 0.3:1 (heme:protein).