Celltype & region–resolved mouse brain proteome
http://www.nature.com/neuro/journal/v18/n12/full/nn.4160.html proteins enriched there v liver & in specific regions (eg NCX v STR)
http://www.nature.com/neuro/journal/v18/n12/full/nn.4160.html
Archive for the 'SciLit' Category
Cell type- and brain region-resolved mouse brain proteome : Nature Neuroscience : Nature Publishing Group
December 13, 2015Understanding the Cellular and Molecular Mechanisms of Physical Activity-Induced Health Benefits: Cell Metabolism
December 5, 2015Cellular…Mechanisms of Physical Activity-Induced Health Benefits http://www.cell.com/cell-metabolism/abstract/S1550-4131(15)00223-5 Mitochondria important to “#exercise responsome”
QT:{{”
… augmenting overall mitochondrial density and oxidative
phosphorylation capacity by as much as 2-fold (Hood et al., 2011). Moreover, PA affects mito-chondrial quality as well as quantity, and recent studies suggest that the functional properties of these organelles are much more heterogeneous and dynamic in nature than previously appreci-ated (Jacobs and Lundby, 2013). Interestingly, PA-induced mito-chondrial biogenesis also occurs in tissues other than skeletal muscle, including brain (E et al., 2013; Steiner et al., 2011), liver (Boveris and Navarro, 2008; E et al., 2013; Navarro et al., 2004), adipose tissue (Laye et al., 2009; Sutherland et al., 2009), and kidney (Navarro et al., 2004), providing evidence that exercise also increases metabolic demand in these tissues and/or stimu-lates inter-organ crosstalk.
….
The rate-limiting impediment to discovery of molecular trans-ducers and their function is not the ‘‘omic” core technology, but the bioinformatics to extract the most useful signals and generate the most appropriate biological interpretation, including those associated with exercise adaptation. Robust computational and bioinformatics analytical tools allowing inte-gration of large datasets from a multiplicity of ‘‘omics” platforms with in vivo exercise physiology assays and measurements would contribute greatly to our understanding of the response to acute bouts of exercise and long-term adaptation to regular exercise exposure.
….
this regard, the development of detailed molecular profiles in cells and tissues in response to acute and chronic exposures to exercise (‘‘the exercise responsomes”) would provide the benchmark against which all other exercise-related conditions, including aging, sex differences, disease states, etc., could be compared for commonality and specificity.
…
Resources are needed not only to fund new trainees, but also to restructure current programs in a manner that combines studies in integrative physiology and bioenergetics with training in basic biochemistry, cellular and molecular biology, and bioinformatics. Additional resources are needed to establish mechanisms for assembling and supporting interdisciplinary teams that are able to catalyze and sustain ex-ercise research. The field would likewise benefit from a program to support a multi-site consortium of exercise scientists with complimentary expertise and resources that together are well positioned to tackle the large, challenging problems relevant to the overarching mission.
“}}
http://www.cell.com/cell-metabolism/abstract/S1550-4131(15)00223-5
Understanding multicellular function and disease with human tissue-specific networks : Nature Genetics : Nature Publishing Group
November 28, 2015Human tissue-specific #networks by @TroyanskayaLab
http://www.nature.com/ng/journal/v47/n6/full/ng.3259.html
Brain-specific ones & NetWAS approach for combining #GWAS genes
access all tissue networks including the brain-specific
networks at giant.princeton.edu
Enhancer Evolution across 20 Mammalian Species: Cell
November 28, 2015#Enhancer Evolution across 20 Mammal[s is faster than for promoters] by @PaulFlicek lab http://www.cell.com/cell/abstract/S0092-8674(15)00007-0 H3K27ac & H3K4me3 #chipseq
Software licenses and retraction
November 25, 2015Treefinder Retraction Note [interestingly, done editorially due to change in software-license terms] http://www.biomedcentral.com/1471-2148/15/243 HT @bornalibran
expression patterns in brain
November 18, 2015Canonical genetic signatures [across 132 structures] of the adult human #brain [in 6 individuals]
http://www.nature.com/neuro/journal/vaop/ncurrent/full/nn.4171.html HT @ozgunharmanci
QT:{{”
We applied a correlation-based metric called differential stability to assess reproducibility of gene expression patterning across 132 structures in six individual brains, revealing mesoscale genetic organization. The genes with the highest differential stability are highly biologically relevant, with enrichment for brain-related annotations, disease associations, drug targets and literature citations.
“}}
Thermodynamics of Antimicrobial Lipopeptide Binding to Membranes: Origins of Affinity and Selectivity: Biophysical Journal
November 15, 2015#Thermodynamics of Antimicrobial Lipopeptide Binding to Membranes http://www.cell.com/biophysj/abstract/S0006-3495(14)00928-X Non-human selectivity studied w. coarse-grained MD
A Multiscale Coarse-Graining Method for Biomolecular Systems – The Journal of Physical Chemistry B (ACS Publications)
November 15, 2015A Multiscale Coarse-Graining Method for Biomolecul[es] http://pubs.acs.org/doi/abs/10.1021/jp044629q Simplified force field from fitting to all-atom #simulations
Sergei Izvekov and Gregory A. Voth *
J. Phys. Chem. B, 2005, 109 (7), pp 2469–2473
DOI: 10.1021/jp044629q
Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells : Nature Biotechnology : Nature Publishing Group
November 14, 2015Heterogeneity in #singlecell RNAseq…hidden subpopulations by @OliverStegle lab http://www.nature.com/nbt/journal/v33/n2/full/nbt.3102.html scLVM corrects for cell cycle phase
Buettner, Florian, Kedar N. Natarajan, F. Paolo Casale, Valentina
Proserpio, Antonio Scialdone, Fabian J. Theis, Sarah A. Teichmann,
John C. Marioni, and Oliver Stegle. "Computational analysis of
cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals
hidden subpopulations of cells." Nature biotechnology 33, no. 2
(2015): 155-160.
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