Caffeine Consumption Prevents Diabetes-Induced Memory Impairment and Synaptotoxicity in the Hippocampus of NONcZNO10/LTJ Mice

Diabetic conditions are associated with modified brain function, namely with cognitive deficits, through largely undetermined processes. More than understanding the underlying mechanism, it is important to devise novel strategies to alleviate diabetes-induced cognitive deficits. Caffeine (a mixed antagonist of adenosine A1 and A2A receptors) emerges as a promising candidate since caffeine consumption reduces the risk of diabetes and effectively prevents memory deficits caused by different noxious stimuli. Thus, we took advantage of a novel animal model of type 2 diabetes to investigate the behavioural, neurochemical and morphological modifications present in the hippocampus and tested if caffeine consumption might prevent these changes. We used a model closely mimicking the human type 2 diabetes condition, NONcNZO10/LtJ mice, which become diabetic at 7–11 months when kept under an 11% fat diet. Caffeine (1 g/l) was applied in the drinking water from 7 months onwards. Diabetic mice displayed a decreased spontaneous alternation in the Y-maze accompanied by a decreased density of nerve terminal markers (synaptophysin, SNAP25), mainly glutamatergic (vesicular glutamate transporters), and increased astrogliosis (GFAP immunoreactivity) compared to their wild type littermates kept under the same diet. Furthermore, diabetic mice displayed up-regulated A2A receptors and down-regulated A1 receptors in the hippocampus. Caffeine consumption restored memory performance and abrogated the diabetes-induced loss of nerve terminals and astrogliosis. These results provide the first evidence that type 2 diabetic mice display a loss of nerve terminal markers and astrogliosis, which is associated with memory impairment; furthermore, caffeine consumption prevents synaptic dysfunction and astrogliosis as well as memory impairment in type 2 diabetes

Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting

<p>Abstract</p> <p>Background</p> <p>The World Health Organisation estimates that by 2030 there will be approximately 350 million people with type 2 diabetes. Associated with renal complications, heart disease, stroke and peripheral vascular disease, early identification of patients with undiagnosed type 2 diabetes or those at an increased risk of developing type 2 diabetes is an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) type 2 diabetes in adults.</p> <p>Methods</p> <p>We conducted a systematic search of PubMed and EMBASE databases to identify studies published before May 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident type 2 diabetes. We extracted key information that describes aspects of developing a prediction model including study design, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies and aspects of performance.</p> <p>Results</p> <p>Thirty-nine studies comprising 43 risk prediction models were included. Seventeen studies (44%) reported the development of models to predict incident type 2 diabetes, whilst 15 studies (38%) described the derivation of models to predict prevalent type 2 diabetes. In nine studies (23%), the number of events per variable was less than ten, whilst in fourteen studies there was insufficient information reported for this measure to be calculated. The number of candidate risk predictors ranged from four to sixty-four, and in seven studies it was unclear how many risk predictors were considered. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in eight studies (21%), whilst the selection procedure was unclear in ten studies (26%). Twenty-one risk prediction models (49%) were developed by categorising all continuous risk predictors. The treatment and handling of missing data were not reported in 16 studies (41%).</p> <p>Conclusions</p> <p>We found widespread use of poor methods that could jeopardise model development, including univariate pre-screening of variables, categorisation of continuous risk predictors and poor handling of missing data. The use of poor methods affects the reliability of the prediction model and ultimately compromises the accuracy of the probability estimates of having undiagnosed type 2 diabetes or the predicted risk of developing type 2 diabetes. In addition, many studies were characterised by a generally poor level of reporting, with many key details to objectively judge the usefulness of the models often omitted.</p

Large Tandem, Higher Order Repeats and Regularly Dispersed Repeat Units Contribute Substantially to Divergence Between Human and Chimpanzee Y Chromosomes

Comparison of human and chimpanzee genomes has received much attention, because of paramount role for understanding evolutionary step distinguishing us from our closest living relative. In order to contribute to insight into Y chromosome evolutionary history, we study and compare tandems, higher order repeats (HORs), and regularly dispersed repeats in human and chimpanzee Y chromosome contigs, using robust Global Repeat Map algorithm. We find a new type of long-range acceleration, human-accelerated HOR regions. In peripheral domains of 35mer human alphoid HORs, we find riddled features with ten additional repeat monomers. In chimpanzee, we identify 30mer alphoid HOR. We construct alphoid HOR schemes showing significant human-chimpanzee difference, revealing rapid evolution after human-chimpanzee separation. We identify and analyze over 20 large repeat units, most of them reported here for the first time as: chimpanzee and human ~1.6 kb 3mer secondary repeat unit (SRU) and ~23.5 kb tertiary repeat unit (~0.55 kb primary repeat unit, PRU); human 10848, 15775, 20309, 60910, and 72140 bp PRUs; human 3mer SRU (~2.4 kb PRU); 715mer and 1123mer SRUs (5mer PRU); chimpanzee 5096, 10762, 10853, 60523 bp PRUs; and chimpanzee 64624 bp SRU (10853 bp PRU). We show that substantial human-chimpanzee differences are concentrated in large repeat structures, at the level of as much as ~70% divergence, sizably exceeding previous numerical estimates for some selected noncoding sequences. Smeared over the whole sequenced assembly (25 Mb) this gives ~14% human--chimpanzee divergence. This is significantly higher estimate of divergence between human and chimpanzee than previous estimates.Comment: 22 pages, 7 figures, 12 tables. Published in Journal of Molecular Evolutio

An atlas of over 90.000 conserved noncoding sequences provides insight into crucifer regulatory regions

Despite the central importance of noncoding DNA to gene regulation and evolution, understanding of the extent of selection on plant noncoding DNA remains limited compared to that of other organisms. Here we report sequencing of genomes from three Brassicaceae species (Leavenworthia alabamica, Sisymbrium irio and Aethionema arabicum) and their joint analysis with six previously sequenced crucifer genomes. Conservation across orthologous bases suggests that at least 17% of the Arabidopsis thaliana genome is under selection, with nearly one-quarter of the sequence under selection lying outside of coding regions. Much of this sequence can be localized to approximately 90,000 conserved noncoding sequences (CNSs) that show evidence of transcriptional and post-transcriptional regulation. Population genomics analyses of two crucifer species, A. thaliana and Capsella grandiflora, confirm that most of the identified CNSs are evolving under medium to strong purifying selection. Overall, these CNSs highlight both similarities and several key differences between the regulatory DNA of plants and other species