Gene profiling of the erythro- and megakaryoblastic leukaemias induced by the Graffi murine retrovirus

<p>Abstract</p> <p>Background</p> <p>Acute erythro- and megakaryoblastic leukaemias are associated with very poor prognoses and the mechanism of blastic transformation is insufficiently elucidated. The murine Graffi leukaemia retrovirus induces erythro- and megakaryoblastic leukaemias when inoculated into NFS mice and represents a good model to study these leukaemias.</p> <p>Methods</p> <p>To expand our understanding of genes specific to these leukaemias, we compared gene expression profiles, measured by microarray and RT-PCR, of all leukaemia types induced by this virus.</p> <p>Results</p> <p>The transcriptome level changes, present between the different leukaemias, led to the identification of specific cancerous signatures. We reported numerous genes that may be potential oncogenes, may have a function related to erythropoiesis or megakaryopoiesis or have a poorly elucidated physiological role. The expression pattern of these genes has been further tested by RT-PCR in different samples, in a Friend erythroleukaemic model and in human leukaemic cell lines.</p> <p>We also screened the megakaryoblastic leukaemias for viral integrations and identified genes targeted by these integrations and potentially implicated in the onset of the disease.</p> <p>Conclusions</p> <p>Taken as a whole, the data obtained from this global gene profiling experiment have provided a detailed characterization of Graffi virus induced erythro- and megakaryoblastic leukaemias with many genes reported specific to the transcriptome of these leukaemias for the first time.</p

Genetic markers of neurodegeneration: a role for 3-hydroxy -3-methylglutaryl-coenzyme A reductase in sporadic Alzheimer's disease

Mounting evidence now converges towards an altered cholesterol metabolism in Alzheimer's disease (AD). Other recent reports suggest that statins, or 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) inhibitors, the rate-limiting enzyme in the biosynthetic pathway of cholesterol, protect against AD. Using a large French Canadian cohort, we demonstrate that a single nucleotide polymorphism (SNP) within Hmgr gene greatly diminishes the risk of developing AD, while a second SNP appears as a risk factor, by influencing the age of onset of the disease. This latter SNP may interfere in neurofibrillary tangles (NFT) formation, one of the neuropathological hallmarks of AD, as well as in HMGR expression and protein levels in the frontal cortex, a region severely affected by AD. Thus, the involvement of Hmgr gene and its downstream metabolic pathway in AD onset, and perhaps also in its progression, seems more than likely.Maintes preuves convergent aujourd'hui vers un métabolisme du cholestérol anormal dans la maladie d'Alzheimer (MA). D'autres constats suggèrent que les statines, ou inhibiteurs de la 3-hydroxy-3-méthylglutaryl coenzyme A réductase (HMGR), l'enzyme limitante de la voie de synthèse du cholestérol, protégeraient contre cette maladie. À partir d'une large cohorte de Canadiens français, nous démontrons qu'un polymorphisme du gène codant pour HMGR diminue fortement le risque de développer la MA, alors qu'un second constitue un facteur de risque influençant l'âge de début de la maladie. Ce dernier semble intervenir dans la formation d'enchevêtrements neurofibrillaires, l'une des marques neuropathologiques de la MA, ainsi que sur l'expression et les niveaux de protéines de HMGR dans le cortex frontal, l'une des régions gravement touchée par la maladie. L'implication de ce gène, et la voie métabolique s'y rattachant, dans le développement, et possiblement la progression, de la MA semble donc plus que probable

csv result files

Results for correlation with age, trajectory with age, mortality, frailty (diagnosis and criteria number), cancer, and CV

Calcium

Serum calcium trajectory during aging and its effects on mortality and health outcome

Statistical Distance as a Measure of Physiological Dysregulation Is Largely Robust to Variation in Its Biomarker Composition

Physiological dysregulation may underlie aging and many chronic diseases, but is challenging to quantify because of the complexity of the underlying systems. Recently, we described a measure of physiological dysregulation, Dᴍ, that uses statistical distance to assess the degree to which an individual’s biomarker profile is normal versus aberrant. However, the sensitivity of Dᴍ to details of the calculation method has not yet been systematically assessed. In particular, the number and choice of biomarkers and the definition of the reference population (RP, the population used to define a “normal” profile) may be important. Here, we address this question by validating the method on 44 common clinical biomarkers from three longitudinal cohort studies and one cross-sectional survey. Dᴍs calculated on different biomarker subsets show that while the signal of physiological dysregulation increases with the number of biomarkers included, the value of additional markers diminishes as more are added and inclusion of 10-15 is generally sufficient. As long as enough markers are included, individual markers have little effect on the final metric, and even Dᴍs calculated from mutually exclusive groups of markers correlate with each other at r~0.4-0.5. We also used data subsets to generate thousands of combinations of study populations and RPs to address sensitivity to differences in age range, sex, race, data set, sample size, and their interactions. Results were largely consistent (but not identical) regardless of the choice of RP; however, the signal was generally clearer with a younger and healthier RP, and RPs too different from the study population performed poorly. Accordingly, biomarker and RP choice are not particularly important in most cases, but caution should be used across very different populations or for fine-scale analyses. Biologically, the lack of sensitivity to marker choice and better performance of younger, healthier RPs confirm an interpretation of Dᴍ physiological dysregulation and as an emergent property of a complex system

Diffusion of a collaborative care model in primary care: a longitudinal qualitative study

<p>Background</p> <p>Although collaborative team models (CTM) improve care processes and health outcomes, their diffusion poses challenges related to difficulties in securing their adoption by primary care clinicians (PCPs). The objectives of this study are to understand: (1) how the perceived characteristics of a CTM influenced clinicians' decision to adopt -or not- the model; and (2) the model's diffusion process.</p> <p>Methods</p> <p>We conducted a longitudinal case study based on the Diffusion of Innovations Theory. First, diffusion curves were developed for all 175 PCPs and 59 nurses practicing in one borough of Paris. Second, semi-structured interviews were conducted with a representative sample of 40 PCPs and 15 nurses to better understand the implementation dynamics.</p> <p>Results</p> <p>Diffusion curves showed that 3.5 years after the start of the implementation, 100% of nurses and over 80% of PCPs had adopted the CTM. The dynamics of the CTM's diffusion were different between the PCPs and the nurses. The slopes of the two curves are also distinctly different. Among the nurses, the <it>critical mass</it> of adopters was attained faster, since they adopted the CTM earlier and more quickly than the PCPs. Results of the semi-structured interviews showed that these differences in diffusion dynamics were mostly founded in differences between the PCPs' and the nurses' perceptions of the CTM's <it>compatibility</it> with norms, values and practices and its <it>relative advantage</it> (impact on patient management and work practices). Opinion leaders played a key role in the diffusion of the CTM among PCPs.</p> <p>Conclusion</p> <p>CTM diffusion is a social phenomenon that requires a major commitment by clinicians and a willingness to take risks; the role of opinion leaders is key. Paying attention to the notion of a <it>critical mass</it> of adopters is essential to developing implementation strategies that will accelerate the adoption process by clinicians.</p