Segmental structure of the Brassica napus genome based on comparative analysis with Arabidopsis thaliana

Over 1000 genetically linked RFLP loci in Brassica napus were mapped to homologous positions in the Arabidopsis genome on the basis of sequence similarity. Blocks of genetically linked loci in B. napus frequently corresponded to physically linked markers in Arabidopsis. This comparative analysis allowed the identification of a minimum of 21 conserved genomic units within the Arabidopsis genome, which can be duplicated and rearranged to generate the present-day B. napus genome. The conserved regions extended over lengths as great as 50 cM in the B. napus genetic map, equivalent to approximately 9 Mb of contiguous sequence in the Arabidopsis genome. There was also evidence for conservation of chromosome landmarks, particularly centromeric regions, between the two species. The observed segmental structure of the Brassica genome strongly suggests that the extant Brassica diploid species evolved from a hexaploid ancestor. The comparative map assists in exploiting the Arabidopsis genomic sequence for marker and candidate gene identification within the larger, intractable genomes of the Brassica polyploids.     

Prion Protein Is Decreased in Alzheimer's Brain and Inversely Correlates with BACE1 Activity,Amyloid-β Levels and Braak Stage

The cellular prion protein (PrP^C) has been implicated in the development of Alzheimer''s disease (AD). PrP^C decreases amyloid-β (Aβ) production, which is involved in AD pathogenesis, by inhibiting β-secretase (BACE1) activity. Contactin 5 (CNTN5) has also been implicated in the development of AD by a genome-wide association study. Here we measured PrP^C and CNTN5 in frontal cortex samples from 24 sporadic AD and 24 age-matched control brains and correlated the expression of these proteins with markers of AD. PrP^C was decreased in sporadic AD compared to controls (by 49%, p = 0.014) but there was no difference in CNTN5 between sporadic AD and controls (p = 0.217). PrP^C significantly inversely correlated with BACE1 activity (r_s = −0.358, p = 0.006), Aβ load (r_s = −0.456, p = 0.001), soluble Aβ (r_s = −0.283, p = 0.026) and insoluble Aβ (r_s = −0.353, p = 0.007) and PrP^C also significantly inversely correlated with the stage of disease, as indicated by Braak tangle stage (r_s = −0.377, p = 0.007). CNTN5 did not correlate with Aβ load (r_s = 0.040, p = 0.393), soluble Aβ (r_s = 0.113, p = 0.223) or insoluble Aβ (r_s = 0.169, p = 0.125). PrP^C was also measured in frontal cortex samples from 9 Down''s syndrome (DS) and 8 age-matched control brains. In contrast to sporadic AD, there was no difference in PrP^C in the DS brains compared to controls (p = 0.625). These data are consistent with a role for PrP^C in regulating Aβ production and indicate that brain PrP^C level may be important in influencing the onset and progression of sporadic AD.     

<Emphasis Type="Bold">A user guide to the</Emphasis><Emphasis Type="BoldItalic">Brassica</Emphasis><Emphasis Type="Bold">60K Illumina Infinium</Emphasis>™ <Emphasis Type="Bold">SNP genotyping array</Emphasis>

The Brassica napus 60K Illumina Infinium? SNP array has had huge international uptake in the rapeseed community due to the revolutionary speed of acquisition and ease of analysis of this high-throughput genotyping data, particularly when coupled with the newly available reference genome sequence. However, further utilization of this valuable resource can be optimized by better understanding the promises and pitfalls of SNP arrays. We outline how best to analyze Brassica SNP marker array data for diverse applications, including linkage and association mapping, genetic diversity and genomic introgression studies. We present data on which SNPs are locus-specific in winter, semi-winter and spring B. napus germplasm pools, rather than amplifying both an A-genome and a C-genome locus or multiple loci. Common issues that arise when analyzing array data will be discussed, particularly those unique to SNP markers and how to deal with these for practical applications in Brassica breeding applications.     

Use of lithium and SB-415286 to explore the role of glycogen synthase kinase-3 in the regulation of glucose transport and glycogen synthase.

Glycogen synthase kinase 3 (GSK3) is inactivated by insulin and lithium and, like insulin, Li also activates glycogen synthase (GS) via inhibition of GSK3. Li also mimics insulin's ability to stimulate glucose transport (GT), an observation that has led to the suggestion that GSK3 may coordinate hormonal increases in GT and glycogen synthesis. Here we have used Li and SB-415286, a selective GSK3 inhibitor, to establish the importance of GSK3 in the hormonal activation of GT in terms of its effect on GS in L6 myotubes and 3T3-L1 adipocytes. Insulin, Li and SB-415286 all induced a significant inhibition of GSK3, which was associated with a marked dephosphorylation and activation of GS. In L6 myotubes, SB-415286 induced a much greater activation of GS (6.8-fold) compared to that elicited by insulin (4.2-fold) or Li (4-fold). In adipocytes, insulin, Li and SB-415286 all caused a comparable activation of GS despite a substantial differentiation-linked reduction in GSK3 expression ( approximately 85%) indicating that GSK3 remains an important determinant of GS activation in fat cells. Whilst Li and SB-415286 both inhibit GSK3 in muscle and fat cells, only Li stimulated GT. This increase in GT was not sensitive to inhibitors of PI3-kinase, MAP kinase or mTOR, but was suppressed by the p38 MAP kinase inhibitor, SB-203580. Consistent with this, phosphorylation of p38 MAP kinase induced by Li correlated with its stimulatory effect on GT. Our findings support a crucial role for GSK3 in the regulation of GS, but based on the differential effects of Li and SB-415286, it is unlikely that acute inhibition of GSK3 contributes towards the rapid stimulation of GT by insulin in muscle and fat cells.     

Comparison of three different methods for automated classification of cervical cells.

Over 4600 exfoliated squamous cervical cells taken from appropriate Papanicolaou samples were classified as normal, mildly dysplastic, moderately dysplastic and severely dysplastic by an experienced cytopathologist. The slides were de-stained and subsequently re-stained with Feulgen Thionin-SO2 stain. Images of the nuclei were then captured, recorded and processed employing an image cytometry device. Automated classification of the cells was carried out using three different methods--discriminant function analysis, a decision tree classifier and a neutral network classifier. The discriminant function analysis method, which combined all dysplastic cells into an abnormal group, achieved a combined error rate of less than 0.4% for moderate and severe dysplastic cells, and less than 40% for mildly dysplastic cells. All three methods yielded comparable results, which approached those of human performance.