81 research outputs found

    Genome Fragmentation Is Not Confined to the Peridinin Plastid in Dinoflagellates

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    When plastids are transferred between eukaryote lineages through series of endosymbiosis, their environment changes dramatically. Comparison of dinoflagellate plastids that originated from different algal groups has revealed convergent evolution, suggesting that the host environment mainly influences the evolution of the newly acquired organelle. Recently the genome from the anomalously pigmented dinoflagellate Karlodinium veneficum plastid was uncovered as a conventional chromosome. To determine if this haptophyte-derived plastid contains additional chromosomal fragments that resemble the mini-circles of the peridin-containing plastids, we have investigated its genome by in-depth sequencing using 454 pyrosequencing technology, PCR and clone library analysis. Sequence analyses show several genes with significantly higher copy numbers than present in the chromosome. These genes are most likely extrachromosomal fragments, and the ones with highest copy numbers include genes encoding the chaperone DnaK(Hsp70), the rubisco large subunit (rbcL), and two tRNAs (trnE and trnM). In addition, some photosystem genes such as psaB, psaA, psbB and psbD are overrepresented. Most of the dnaK and rbcL sequences are found as shortened or fragmented gene sequences, typically missing the 3′-terminal portion. Both dnaK and rbcL are associated with a common sequence element consisting of about 120 bp of highly conserved AT-rich sequence followed by a trnE gene, possibly serving as a control region. Decatenation assays and Southern blot analysis indicate that the extrachromosomal plastid sequences do not have the same organization or lengths as the minicircles of the peridinin dinoflagellates. The fragmentation of the haptophyte-derived plastid genome K. veneficum suggests that it is likely a sign of a host-driven process shaping the plastid genomes of dinoflagellates

    Genome Evolution of a Tertiary Dinoflagellate Plastid

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    The dinoflagellates have repeatedly replaced their ancestral peridinin-plastid by plastids derived from a variety of algal lineages ranging from green algae to diatoms. Here, we have characterized the genome of a dinoflagellate plastid of tertiary origin in order to understand the evolutionary processes that have shaped the organelle since it was acquired as a symbiont cell. To address this, the genome of the haptophyte-derived plastid in Karlodinium veneficum was analyzed by Sanger sequencing of library clones and 454 pyrosequencing of plastid enriched DNA fractions. The sequences were assembled into a single contig of 143 kb, encoding 70 proteins, 3 rRNAs and a nearly full set of tRNAs. Comparative genomics revealed massive rearrangements and gene losses compared to the haptophyte plastid; only a small fraction of the gene clusters usually found in haptophytes as well as other types of plastids are present in K. veneficum. Despite the reduced number of genes, the K. veneficum plastid genome has retained a large size due to expanded intergenic regions. Some of the plastid genes are highly diverged and may be pseudogenes or subject to RNA editing. Gene losses and rearrangements are also features of the genomes of the peridinin-containing plastids, apicomplexa and Chromera, suggesting that the evolutionary processes that once shaped these plastids have occurred at multiple independent occasions over the history of the Alveolata

    Patient complexity in quality comparisons for glycemic control: An observational study

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    <p>Abstract</p> <p>Background</p> <p>Patient complexity is not incorporated into quality of care comparisons for glycemic control. We developed a method to adjust hemoglobin A1c levels for patient characteristics that reflect complexity, and examined the effect of using adjusted A1c values on quality comparisons.</p> <p>Methods</p> <p>This cross-sectional observational study used 1999 national VA (US Department of Veterans Affairs) pharmacy, inpatient and outpatient utilization, and laboratory data on diabetic veterans. We adjusted individual A1c levels for available domains of complexity: age, social support (marital status), comorbid illnesses, and severity of disease (insulin use). We used adjusted A1c values to generate VA medical center level performance measures, and compared medical center ranks using adjusted versus unadjusted A1c levels across several thresholds of A1c (8.0%, 8.5%, 9.0%, and 9.5%).</p> <p>Results</p> <p>The adjustment model had R<sup>2 </sup>= 8.3% with stable parameter estimates on thirty random 50% resamples. Adjustment for patient complexity resulted in the greatest rank differences in the best and worst performing deciles, with similar patterns across all tested thresholds.</p> <p>Conclusion</p> <p>Adjustment for complexity resulted in large differences in identified best and worst performers at all tested thresholds. Current performance measures of glycemic control may not be reliably identifying quality problems, and tying reimbursements to such measures may compromise the care of complex patients.</p

    Biosynthesis and Molecular Genetics of Polyketides in Marine Dinoflagellates

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    Marine dinoflagellates are the single most important group of algae that produce toxins, which have a global impact on human activities. The toxins are chemically diverse, and include macrolides, cyclic polyethers, spirolides and purine alkaloids. Whereas there is a multitude of studies describing the pharmacology of these toxins, there is limited or no knowledge regarding the biochemistry and molecular genetics involved in their biosynthesis. Recently, however, exciting advances have been made. Expressed sequence tag sequencing studies have revealed important insights into the transcriptomes of dinoflagellates, whereas other studies have implicated polyketide synthase genes in the biosynthesis of cyclic polyether toxins, and the molecular genetic basis for the biosynthesis of paralytic shellfish toxins has been elucidated in cyanobacteria. This review summarises the recent progress that has been made regarding the unusual genomes of dinoflagellates, the biosynthesis and molecular genetics of dinoflagellate toxins. In addition, the evolution of these metabolic pathways will be discussed, and an outlook for future research and possible applications is provided

    Parental diet, pregnancy outcomes and offspring health:metabolic determinants in developing oocytes and embryos

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    The periconceptional period, embracing the terminal stages of oocyte growth and post-fertilisation development up to implantation, is sensitive to parental nutrition. Deficiencies or excesses in a range of macro- and micronutrients during this period can lead to impairments in fertility, fetal development and long-term offspring health. Obesity and genotype-related differences in regional adiposity are associated with impaired liver function and insulin resistance, and contribute to fatty acid-mediated impairments in sperm viability and oocyte and embryo quality, all of which are associated with endoplasmic reticulum stress and compromised fertility. Disturbances to maternal protein metabolism can elevate ammonium concentrations in reproductive tissues and disturb embryo and fetal development. Associated with this are disturbances to one-carbon metabolism, which can lead to epigenetic modifications to DNA and associated proteins in offspring that are both insulin resistant and hypertensive. Many enzymes involved in epigenetic gene regulation use metabolic cosubstrates (e.g. acetyl CoA and S-adenosyl methionine) to modify DNA and associated proteins, and so act as 'metabolic sensors' providing a link between parental nutritional status and gene regulation. Separate to their genomic contribution, spermatozoa can also influence embryo development via direct interactions with the egg and by seminal plasma components that act on oviductal and uterine tissues

    Investigating the Impact of Uncertainty About Item Parameters on Ability Estimation

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    bias, item response theory (IRT), measurement error, maximum likelihood estimator (MLE), weighted likelihood estimator (WLE),
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