119 research outputs found

    Selection Mechanisms Underlying High Impact Biomedical Research - A Qualitative Analysis and Causal Model

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    BACKGROUND: Although scientific innovation has been a long-standing topic of interest for historians, philosophers and cognitive scientists, few studies in biomedical research have examined from researchers' perspectives how high impact publications are developed and why they are consistently produced by a small group of researchers. Our objective was therefore to interview a group of researchers with a track record of high impact publications to explore what mechanism they believe contribute to the generation of high impact publications. METHODOLOGY/PRINCIPAL FINDINGS: Researchers were located in universities all over the globe and interviews were conducted by phone. All interviews were transcribed using standard qualitative methods. A Grounded Theory approach was used to code each transcript, later aggregating concept and categories into overarching explanation model. The model was then translated into a System Dynamics mathematical model to represent its structure and behavior. Five emerging themes were found in our study. First, researchers used heuristics or rules of thumb that came naturally to them. Second, these heuristics were reinforced by positive feedback from their peers and mentors. Third, good communication skills allowed researchers to provide feedback to their peers, thus closing a positive feedback loop. Fourth, researchers exhibited a number of psychological attributes such as curiosity or open-mindedness that constantly motivated them, even when faced with discouraging situations. Fifth, the system is dominated by randomness and serendipity and is far from a linear and predictable environment. Some researchers, however, took advantage of this randomness by incorporating mechanisms that would allow them to benefit from random findings. The aggregation of these themes into a policy model represented the overall expected behavior of publications and their impact achieved by high impact researchers. CONCLUSIONS: The proposed selection mechanism provides insights that can be translated into research coaching programs as well as research policy models to optimize the introduction of high impact research at a broad scale among institutional and governmental agencies

    Extensive Gene-Specific Translational Reprogramming in a Model of B Cell Differentiation and Abl-Dependent Transformation

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    To what extent might the regulation of translation contribute to differentiation programs, or to the molecular pathogenesis of cancer? Pre-B cells transformed with the viral oncogene v-Abl are suspended in an immortalized, cycling state that mimics leukemias with a BCR-ABL1 translocation, such as Chronic Myelogenous Leukemia (CML) and Acute Lymphoblastic Leukemia (ALL). Inhibition of the oncogenic Abl kinase with imatinib reverses transformation, allowing progression to the next stage of B cell development. We employed a genome-wide polysome profiling assay called Gradient Encoding to investigate the extent and potential contribution of translational regulation to transformation and differentiation in v-Abl-transformed pre-B cells. Over half of the significantly translationally regulated genes did not change significantly at the level of mRNA abundance, revealing biology that might have been missed by measuring changes in transcript abundance alone. We found extensive, gene-specific changes in translation affecting genes with known roles in B cell signaling and differentiation, cancerous transformation, and cytoskeletal reorganization potentially affecting adhesion. These results highlight a major role for gene-specific translational regulation in remodeling the gene expression program in differentiation and malignant transformation

    Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA Sequencing in a porcine model

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    Background: Obesity is a complex metabolic condition in strong association with various diseases, like type 2 diabetes, resulting in major public health and economic implications. Obesity is the result of environmental and genetic factors and their interactions, including genome-wide genetic interactions. Identification of co-expressed and regulatory genes in RNA extracted from relevant tissues representing lean and obese individuals provides an entry point for the identification of genes and pathways of importance to the development of obesity. The pig, an omnivorous animal, is an excellent model for human obesity, offering the possibility to study in-depth organ-level transcriptomic regulations of obesity, unfeasible in humans. Our aim was to reveal adipose tissue co-expression networks, pathways and transcriptional regulations of obesity using RNA Sequencing based systems biology approaches in a porcine model. Methods: We selected 36 animals for RNA Sequencing from a previously created F2 pig population representing three extreme groups based on their predicted genetic risks for obesity. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to detect clusters of highly co-expressed genes (modules). Additionally, regulator genes were detected using Lemon-Tree algorithms. Results: WGCNA revealed five modules which were strongly correlated with at least one obesity-related phenotype (correlations ranging from -0.54 to 0.72, P <0.001). Functional annotation identified pathways enlightening the association between obesity and other diseases, like osteoporosis (osteoclast differentiation, P = 1.4E(-7)), and immune-related complications (e. g. Natural killer cell mediated cytotoxity, P = 3.8E(-5); B cell receptor signaling pathway, P = 7.2E(-5)). Lemon-Tree identified three potential regulator genes, using confident scores, for the WGCNA module which was associated with osteoclast differentiation: CCR1, MSR1 and SI1 (probability scores respectively 95.30, 62.28, and 34.58). Moreover, detection of differentially connected genes identified various genes previously identified to be associated with obesity in humans and rodents, e.g. CSF1R and MARC2. Conclusions: To our knowledge, this is the first study to apply systems biology approaches using porcine adipose tissue RNA-Sequencing data in a genetically characterized porcine model for obesity. We revealed complex networks, pathways, candidate and regulatory genes related to obesity, confirming the complexity of obesity and its association with immune-related disorders and osteoporosis

    Endothelial dysfunction and diabetes: roles of hyperglycemia, impaired insulin signaling and obesity

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