28 research outputs found

    Dissection of a QTL Hotspot on Mouse Distal Chromosome 1 that Modulates Neurobehavioral Phenotypes and Gene Expression

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    A remarkably diverse set of traits maps to a region on mouse distal chromosome 1 (Chr 1) that corresponds to human Chr 1q21–q23. This region is highly enriched in quantitative trait loci (QTLs) that control neural and behavioral phenotypes, including motor behavior, escape latency, emotionality, seizure susceptibility (Szs1), and responses to ethanol, caffeine, pentobarbital, and haloperidol. This region also controls the expression of a remarkably large number of genes, including genes that are associated with some of the classical traits that map to distal Chr 1 (e.g., seizure susceptibility). Here, we ask whether this QTL-rich region on Chr 1 (Qrr1) consists of a single master locus or a mixture of linked, but functionally unrelated, QTLs. To answer this question and to evaluate candidate genes, we generated and analyzed several gene expression, haplotype, and sequence datasets. We exploited six complementary mouse crosses, and combed through 18 expression datasets to determine class membership of genes modulated by Qrr1. Qrr1 can be broadly divided into a proximal part (Qrr1p) and a distal part (Qrr1d), each associated with the expression of distinct subsets of genes. Qrr1d controls RNA metabolism and protein synthesis, including the expression of ∼20 aminoacyl-tRNA synthetases. Qrr1d contains a tRNA cluster, and this is a functionally pertinent candidate for the tRNA synthetases. Rgs7 and Fmn2 are other strong candidates in Qrr1d. FMN2 protein has pronounced expression in neurons, including in the dendrites, and deletion of Fmn2 had a strong effect on the expression of few genes modulated by Qrr1d. Our analysis revealed a highly complex gene expression regulatory interval in Qrr1, composed of multiple loci modulating the expression of functionally cognate sets of genes

    AI is a viable alternative to high throughput screening: a 318-target study

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    : High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

    Moving knowledge into action for more effective practice, programmes and policy: protocol for a research programme on integrated knowledge translation

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    Essential items for reporting of scaling studies of health interventions (SUCCEED) : protocol for a systematic review and Delphi process

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    CITATION: Gogovor, A., et al. 2020. Essential items for reporting of scaling studies of health interventions (SUCCEED) : protocol for a systematic review and Delphi process. Systematic Reviews, 9:11, doi:10.1186/s13643-019-1258-3.The original publication is available at https://systematicreviewsjournal.biomedcentral.comBackground: The lack of a reporting guideline for scaling of evidence-based practices (EBPs) studies has prompted the registration of the Standards for reporting studies assessing the impact of scaling strategies of EBPs (SUCCEED) with EQUATOR Network. The development of SUCCEED will be guided by the following main steps recommended for developing health research reporting guidelines. Methods: Executive Committee. We established a committee composed of members of the core research team and of an advisory group. Systematic review. The protocol was registered with the Open Science Framework on 29 November 2019 (https://osf. io/vcwfx/). We will include reporting guidelines or other reports that may include items relevant to studies assessing the impact of scaling strategies. We will search the following electronic databases: EMBASE, PsycINFO, Cochrane Library, CINAHL, Web of Science, from inception. In addition, we will systematically search websites of EQUATOR and other relevant organizations. Experts in the field of reporting guidelines will also be contacted. Study selection and data extraction will be conducted independently by two reviewers. A narrative analysis will be conducted to compile a list of items for the Delphi exercise. Consensus process. We will invite panelists with expertise in: development of relevant reporting guidelines, methodologists, content experts, patient/member of the public, implementers, journal editors, and funders. We anticipated that three rounds of web-based Delphi consensus will be needed for an acceptable degree of agreement. We will use a 9-point scale (1 = extremely irrelevant to 9 = extremely relevant). Participants’ response will be categorized as irrelevant (1–3), equivocal (4–6) and relevant (7–9). For each item, the consensus is reached if at least 80% of the participants’ votes fall within the same category. The list of items from the final round will be discussed at face-to-face consensus meeting. Guideline validation. Participants will be authors of scaling studies. We will collect quantitative (questionnaire) and qualitative (semi-structured interview) data. Descriptive analyses will be conducted on quantitative data and constant comparative techniques on qualitative data. Discussion: Essential items for reporting scaling studies will contribute to better reporting of scaling studies and facilitate the transparency and scaling of evidence-based health interventions.https://systematicreviewsjournal.biomedcentral.com/articles/10.1186/s13643-019-1258-3Publisher's versio

    Linkage of a familial platelet disorder with a propensity to develop myeloid malignancies to human chromosome 21q22.1-22.2

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    Linkage analysis was performed on a large pedigree with an autosomal dominant platelet disorder and a striking propensity in affected family members to develop hematologic malignancy, predominantly acute myelogenous leukemia. We report the linkage of the autosomal dominant platelet disorder to markers on chromosome 21q22. Four genetic markers completely cosegregate with the trait and yield maximum logarithm of difference scores ranging from 4.9 to 10.5 (θ = .001). Two flanking markers, D21S1265 and D21S167, define a critical region for the disease locus of 15.2 centimorgan. Further analysis of this locus may identify a gene product that affects platelet production and function and contributes to the molecular evolution of hematologic malignancy.7 page(s
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