Identifying the target genes of SUPPRESSOR OF GAMMA RESPONSE 1, a master transcription factor controlling DNA damage response in Arabidopsis

In mammalian cells, the transcription factor p53 plays a crucial role in transmitting DNA damage signals to maintain genome integrity. However, in plants, orthologous genes for p53 and checkpoint proteins are absent. Instead, the plant-specific transcription factor SUPPRESSOR OF GAMMA RADIATION 1 (SOG1) controls most of the genes induced by gamma irradiation and promotes DNA repair, cell cycle arrest, and stem cell death. Thus far, the genes directly controlled by SOG1 remain largely unknown, limiting the understanding of DNA damage signaling in plants. Here, we conducted a microarray analysis and chromatin immunoprecipitation (ChIP)-sequencing, and identified 146 Arabidopsis genes as direct targets of SOG1. By using the ChIP-sequencing data, we extracted the palindromic motif [CTT(N)7AAG] as a consensus SOG1-binding sequence, which mediates target gene induction in response to DNA damage. Furthermore, DNA damage-triggered phosphorylation of SOG1 is required for efficient binding to SOG1-binding sequence. Comparison between SOG1 and p53 target genes showed that both transcription factors control genes responsible for cell cycle regulation, such as CDK inhibitors, and DNA repair proteins, whereas SOG1 preferentially targets genes involved in homologous recombination. We also found that defense-related genes were enriched in the SOG1 target genes. Consistent with this, SOG1 is required for resistance against the hemi-biotrophic fungus Colletotrichum higginsianum, suggesting that SOG1 has a unique function in controlling immune response. This article is protected by copyright. All rights reserved.journal articl

アクティブホイールマウスにおける指腹部滑り知覚特性

application/pdf三重大学大学院 工学研究科 博士前期課程 機械工学専攻29thesi

Palladium-Catalyzed Carbonylative Synthesis of Diaryl Ketones from Arenes and Arylboronic Acids through C(sp²)–H Thianthrenation

The development of mild methodology for converting inert C–H bonds to value-added molecules has been an attractive research topic during the last few decades as it offers efficient preparation. Meanwhile, diaryl ketones hold potent applications in antitumor drugs, the agrochemical industry, and synthetic chemistry. Herein, we report versatile palladium-catalyzed carbonylative cross-coupling reactions of aryl thianthrenium salts with arylboronic acids. Arenes were transformed site selectively via C(sp2)–H thianthrenation, and various desired diaryl ketones were produced in good to excellent yields

Nonlinear Spatial Dynamic Panel Data Models with Endogenous Dominant Units: An Application to Share Data

This article develops a nonlinear spatial dynamic panel data model with one particularly interesting application to a structural interaction model for share data. To account for effects from dominant (popular) units, the spatial weights matrix in our model can allow for unbounded column sums. To account for heterogeneity, our model includes two-way fixed effects and heteroscedastic errors. We further consider the potential endogeneity of the spatial weight matrix constructed from socioeconomic distance. We investigate the quasi-maximum likelihood estimator (QMLE), generalized methods of moments estimator (GMME), and root estimator (RTE), and establish their consistency and asymptotic normality based on the near epoch dependence (NED) framework. The RTE can derive a relatively computationally simple and closed-form solution without evaluating the QMLE’s Jacobian matrix as well as the iterations by GMME. We consider both n,T→∞, and the strength of the dominant units is equal to 1 when T→∞. For the purpose of empirical analysis, we derive the marginal effects and their limiting distributions based on the proposed estimators. In an empirical application, we apply our model to China’s prefecture city-level data, revealing significant spillover effects of the tertiary industry share. These findings suggest that the development of the tertiary sector in large cities can foster its growth in small cities.</p

Basic characteristics of NOACs dose.

Basic characteristics of NOACs dose.</p

Modeling interactions between a pair of DNA loops: from physical models to biological models and to theoretical models.

The blue and green loops influence gene expression in direct and indirect manners, respectively. The first column depicts fundamental biological structures for three kinds of interactions between two DNA loops, where the Su and Hw (green dock) may form a loop; the enhancer and promoter (blue dock) may form another loop. The second column depicts physical structures for respective DNA–looping interactions in the first column, which consider two different paths of looping (i.e., the Su and Hw pair or the enhancer and promoter pair is first looping). The third column represents respective theoretical models by mapping the physical models in the second column into a multistate model of gene expression, where transition rates between active and inactive states actually represent the looping rates, which depend on the loop lengths (along the DNA line), denoted by d1 for the blue loop but by d2 for the green loop.</p

Effect of Interaction between Chromatin Loops on Cell-to-Cell Variability in Gene Expression - Fig 6

<p><b>Dependence of relative change ratios on the green loop length: (A) mean expression and (B) noise intensity.</b> Here, parameter values are set as <i>d</i>₁ = 1500, <i>λ</i>₂₁ = <i>λ</i>₃₂ = <i>λ</i>₃₄ = <i>λ</i>₄₁ = 0.3, <i>μ</i> = 10, <i>δ</i> = 1, <i>r</i> = 0.15, and <i>k</i>₁ = <i>k</i>₂ = 0.5 for alternating loops but with <i>d</i>₂ ∈ (40,10000) for nested loops.</p

DataSheet_1_Revealing Dynamic Mechanisms of Cell Fate Decisions From Single-Cell Transcriptomic Data.pdf

Cell fate decisions play a pivotal role in development, but technologies for dissecting them are limited. We developed a multifunction new method, Topographer, to construct a “quantitative” Waddington’s landscape of single-cell transcriptomic data. This method is able to identify complex cell-state transition trajectories and to estimate complex cell-type dynamics characterized by fate and transition probabilities. It also infers both marker gene networks and their dynamic changes as well as dynamic characteristics of transcriptional bursting along the cell-state transition trajectories. Applying this method to single-cell RNA-seq data on the differentiation of primary human myoblasts, we not only identified three known cell types, but also estimated both their fate probabilities and transition probabilities among them. We found that the percent of genes expressed in a bursty manner is significantly higher at (or near) the branch point (~97%) than before or after branch (below 80%), and that both gene-gene and cell-cell correlation degrees are apparently lower near the branch point than away from the branching. Topographer allows revealing of cell fate mechanisms in a coherent way at three scales: cell lineage (macroscopic), gene network (mesoscopic), and gene expression (microscopic).</p

Mathematical derivations and supplementary information. from Stochastic fluctuations in apoptotic threshold of tumour cells can enhance apoptosis and combat fractional killing

The derivation of all equations in the main text is provided, and more numerical results are demonstrated

Figure 2

<p>Period histogram (a and b) and dependence of R on τ (c) for 10³ cells. (a) k = 0.0, (b and c) k = 2.0. For (b), σ = 10, and for (c), T₀ = 12.2 (representing the intrinsic period). The lifetime ratio β in the different cells is chosen to obey the Gaussian distribution of mean <β> = 2 and standard deviation Δβ = 0.05.</p