Genome sequencing: a grin without a cat

In some types of unicellular algae, the chloroplasts have their own nucleus — a legacy of the time when the chloroplast was a free-living cell. The sequence of the genome in one such nucleus is now revealed.

Plastid division in mallomonas (Synurophyceae, Heterokonta)

Laser scanning confocal microscopy and TEM were used to study the morphology of secondary plastids in algae of the genus Mallomonas (Synurophyceae). At interphase, Mallomonas splendens (G. S. West) Playfair, M. rasilis Dürrschm., M. striata Asmund, and M. adamas K. Harris et W. H. Bradley contained a single H-shaped plastid consisting of two large lobes connected by a narrow isthmus. Labeling of DNA revealed a necklace-like arrangement of plastid nucleoids at the periphery of the M. splendens plastid and a less-patterned array in M. rasilis. The TEM of M. splendens and M. rasilis showed an electron-dense belt surrounding the plastid isthmus in interphase cells; this putative plastid-dividing ring (PD ring) was adpressed to the inner pair of the four plastid membranes, suggesting that it is homologous to the PD ring of green and red plastids. The PD ring did not contain actin (indicated by lack of staining with phalloidin) and displayed filaments or tubules of 5–10 nm in diameter that may be homologous to the tubules described in red algal PD rings. Confocal microscopy of chl autofluorescence from M. splendens showed that the plastid isthmus was severed as mitosis began, giving rise to two single-lobed daughter plastids, which, as mitosis and cell division progressed, separated from one another and then each constricted to form the H-shaped plastids of daughter cells. Similar plastid division cycles were observed in M. rasilis and M. adamas; however, the plastid isthmus of M. striata was retained throughout most of cell division and was eventually severed by the cell cleavage furrow.

Genome - wide association study identifies novel breast cancer susceptibility loci

Breast cancer exhibits familial aggregation, consistent with variation in genetic susceptibility to the disease. Known susceptibility genes account for less than 25% of the familial risk of breast cancer, and the residual genetic variance is likely to be due to variants conferring more moderate risks. To identify further susceptibility alleles, we conducted a two-stage genome-wide association study in 4,398 breast cancer cases and 4,316 controls, followed by a third stage in which 30 single nucleotide polymorphisms (SNPs) were tested for confirmation in 21,860 cases and 22,578 controls from 22 studies. We used 227,876 SNPs that were estimated to correlate with 77% of known common SNPs in Europeans at r² > 0.5. SNPs in five novel independent loci exhibited strong and consistent evidence of association with breast cancer (P < 10^-7). Four of these contain plausible causative genes (FGFR2, TNRC9, MAP3K1 and LSP1). At the second stage, 1,792 SNPs were significant at the P < 0.05 level compared with an estimated 1,343 that would be expected by chance, indicating that many additional common susceptibility alleles may be identifiable by this approach.

Insights into bacterial genome composition through variable target GC content profiling

This study presents a new computational method for guanine (G) and cytosine (C), or GC, content profiling based on the idea of multiple resolution sampling (MRS). The benefit of our new approach over existing techniques follows from its ability to locate significant regions without prior knowledge of the sequence, nor the features being sought. The use of MRS has provided novel insights into bacterial genome composition. Key findings include those that are related to the core composition of bacterial genomes, to the identification of large genomic islands (in Enterobacterial genomes), and to the identification of surface protein determinants in human pathogenic organisms (e.g., Staphylococcus genomes). We observed that bacterial surface binding proteins maintain abnormal GC content, potentially pointing to a viral origin. This study has demonstrated that GC content holds a high informational worth and hints at many underlying evolutionary processes. For online Supplementary Material, see www.liebertonline.com.

Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies

Background
Automated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies.

Results
Using a search space of 9556 genes, all but one of the systems pruned the genome in favour of genes associated with moderate to highly significant SNPs. Of the 11 genes associated with highly significant SNPs identified by the genome-wide association studies, eight were flagged as likely candidates by at least one of the prediction systems. A list of candidates produced by a previous consensus approach did not match any of the genes implicated by 706 moderate to highly significant SNPs flagged by the genome-wide association studies. We prioritized genes associated with medium significance SNPs.

Conclusion
The study appraises the relative success of several candidate gene prediction systems against independent genetic data. Even when confronted with challengingly large intervals, the candidate gene prediction systems can successfully select likely disease genes. Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. We suggest consensus approaches fail because they penalize novel predictions made from independent underlying databases. To realize their full potential further work needs to be done on prioritization and annotation of genes.

Analysis of genome-wide association study data using the protein knowledge base

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