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Soumya Raychaudhuri - One of the best experts on this subject based on the ideXlab platform.

  • chromatin marks identify Critical Cell types for fine mapping complex trait variants
    Nature Genetics, 2013
    Co-Authors: Gosia Trynka, Cynthia Sando, Arbara E Strange, Shirley X Liu, Soumya Raychaudhuri
    Abstract:

    Soumya Raychaudhuri and colleagues report a broadly applicable method that uses chromatin marks, specifically H3K4me3, to identify Critical Cell types to fine map complex trait variants.

  • Chromatin marks identify Critical Cell types for fine mapping complex trait variants
    Nature genetics, 2012
    Co-Authors: Gosia Trynka, Cynthia Sandor, Buhm Han, Barbara Elaine Stranger, X. Shirley Liu, Soumya Raychaudhuri
    Abstract:

    If trait-associated variants alter regulatory regions, then they should fall within chromatin marks in relevant Cell types. However, it is unclear which of the many marks are most useful in defining Cell types associated with disease and fine mapping variants. We hypothesized that informative marks are phenotypically Cell type specific; that is, SNPs associated with the same trait likely overlap marks in the same Cell type. We examined 15 chromatin marks and found that those highlighting active gene regulation were phenotypically Cell type specific. Trimethylation of histone H3 at lysine 4 (H3K4me3) was the most phenotypically Cell type specific (P < 1 × 10(-6)), driven by colocalization of variants and marks rather than gene proximity (P < 0.001). H3K4me3 peaks overlapped with 37 SNPs for plasma low-density lipoprotein concentration in the liver (P < 7 × 10(-5)), 31 SNPs for rheumatoid arthritis within CD4(+) regulatory T Cells (P = 1 × 10(-4)), 67 SNPs for type 2 diabetes in pancreatic islet Cells (P = 0.003) and the liver (P = 0.003), and 14 SNPs for neuropsychiatric disease in neuronal tissues (P = 0.007). We show how Cell type-specific H3K4me3 peaks can inform the fine mapping of associated SNPs to identify causal variation.

Arbara E Strange - One of the best experts on this subject based on the ideXlab platform.

Gosia Trynka - One of the best experts on this subject based on the ideXlab platform.

  • chromatin marks identify Critical Cell types for fine mapping complex trait variants
    Nature Genetics, 2013
    Co-Authors: Gosia Trynka, Cynthia Sando, Arbara E Strange, Shirley X Liu, Soumya Raychaudhuri
    Abstract:

    Soumya Raychaudhuri and colleagues report a broadly applicable method that uses chromatin marks, specifically H3K4me3, to identify Critical Cell types to fine map complex trait variants.

  • Chromatin marks identify Critical Cell types for fine mapping complex trait variants
    Nature genetics, 2012
    Co-Authors: Gosia Trynka, Cynthia Sandor, Buhm Han, Barbara Elaine Stranger, X. Shirley Liu, Soumya Raychaudhuri
    Abstract:

    If trait-associated variants alter regulatory regions, then they should fall within chromatin marks in relevant Cell types. However, it is unclear which of the many marks are most useful in defining Cell types associated with disease and fine mapping variants. We hypothesized that informative marks are phenotypically Cell type specific; that is, SNPs associated with the same trait likely overlap marks in the same Cell type. We examined 15 chromatin marks and found that those highlighting active gene regulation were phenotypically Cell type specific. Trimethylation of histone H3 at lysine 4 (H3K4me3) was the most phenotypically Cell type specific (P < 1 × 10(-6)), driven by colocalization of variants and marks rather than gene proximity (P < 0.001). H3K4me3 peaks overlapped with 37 SNPs for plasma low-density lipoprotein concentration in the liver (P < 7 × 10(-5)), 31 SNPs for rheumatoid arthritis within CD4(+) regulatory T Cells (P = 1 × 10(-4)), 67 SNPs for type 2 diabetes in pancreatic islet Cells (P = 0.003) and the liver (P = 0.003), and 14 SNPs for neuropsychiatric disease in neuronal tissues (P = 0.007). We show how Cell type-specific H3K4me3 peaks can inform the fine mapping of associated SNPs to identify causal variation.

Lilia Alberghina - One of the best experts on this subject based on the ideXlab platform.

  • Whi5 phosphorylation embedded in the G 1 /S network dynamically controls Critical Cell size and Cell fate
    Nature communications, 2016
    Co-Authors: Pasquale Palumbo, Marco Vanoni, Valerio Cusimano, Stefano Busti, Francesca Marano, Costanzo Manes, Lilia Alberghina
    Abstract:

    In budding yeast, overcoming of a Critical size to enter S phase and the mitosis/mating switch--two central Cell fate events--take place in the G1 phase of the Cell cycle. Here we present a mathematical model of the basic molecular mechanism controlling the G1/S transition, whose major regulatory feature is multisite phosphorylation of nuclear Whi5. Cln3-Cdk1, whose nuclear amount is proportional to Cell size, and then Cln1,2-Cdk1, randomly phosphorylate both decoy and functional Whi5 sites. Full phosphorylation of functional sites releases Whi5 inhibitory activity, activating G1/S transcription. Simulation analysis shows that this mechanism ensures coherent release of Whi5 inhibitory action and accounts for many experimentally observed properties of mitotically growing or conjugating G1 Cells. Cell cycle progression and transcriptional analyses of a Whi5 phosphomimetic mutant verify the model prediction that coherent transcription of the G1/S regulon and ensuing G1/S transition requires full phosphorylation of Whi5 functional sites.

  • cAMP-mediated increase in the Critical Cell size required for the G1 to S transition in Saccharomyces cerevisiae
    Experimental cell research, 1992
    Co-Authors: Maurizio David Baroni, Paolo Monti, Giulia Marconi, Lilia Alberghina
    Abstract:

    Abstract In Saccharomyces cerevisiae, cyclic AMP is required for Cellular growth. In this study we show that cAMP also specifically inhibits the G1-S transition of the S. cerevisiae Cell cycle by increasing the Critical Cell size required at start, the major yeast Cell cycle control step. In fact: (a) addition of cAMP delays the time of entering into the S budded phase of small G1 Cells, while it is ineffective on large fast-growing Cells. (b) If Cell growth is strongly depressed, cAMP permanently inhibits Cell cycle commitment of Cells arrested at the α-factor-sensitive step. The Cell fraction inhibited by cAMP is inversely correlated with the average Cell size of treated populations. (c) The Critical protein content (Ps) and the Critical Cell volume (VB) required for budding in unperturbed exponentially growing yeast populations are largely increased by cAMP. On these bases, we propose a new cAMP role at start.

Seungyeol Yoo - One of the best experts on this subject based on the ideXlab platform.

  • On-line and real-time diagnosis method for proton membrane fuel Cell (PEMFC) stack by the superposition principle
    Journal of Power Sources, 2016
    Co-Authors: Young-hyun Lee, Jonghyeon Kim, Seungyeol Yoo
    Abstract:

    Abstract The Critical Cell voltage drop in a stack can be followed by stack defect. A method of detecting defective Cell is the Cell voltage monitoring. The other methods are based on the nonlinear frequency response. In this paper, the superposition principle for the diagnosis of PEMFC stack is introduced. If Critical Cell voltage drops exist, the stack behaves as a nonlinear system. This nonlinearity can explicitly appear in the ohmic overpotential region of a voltage-current curve. To detect the Critical Cell voltage drop, a stack is excited by two input direct test-currents which have smaller amplitude than an operating stack current and have an equal distance value from the operating current. If the difference between one voltage excited by a test current and the voltage excited by a load current is not equal to the difference between the other voltage response and the voltage excited by the load current, the stack system acts as a nonlinear system. This means that there is a Critical Cell voltage drop. The deviation from the value zero of the difference reflects the grade of the system nonlinearity. A simulation model for the stack diagnosis is developed based on the SPP, and experimentally validated.