Epithelial Splicing Regulatory Protein 1 is a Favorable Prognostic Factor in Pancreatic Cancer that Attenuates Pancreatic Metastases

Epithelial splicing regulatory protein 1 (ESRP1) binds the FGFR-2 auxiliary cis-element ISE/ISS-3, located in the intron between exon IIIb and IIIc, and primarily promotes FGFR-2 IIIb expression. Here we assessed the role of ESRP1 in pancreatic ductal adenocarcinoma (PDAC). Immunohistochemical analysis was performed using anti-ESRP1, FGFR-2 IIIb and FGFR-2 IIIc antibodies in 123 PDAC cases. ESRP1-expression vector and small interference RNA (siRNA) targeting ESRP1 were transfected into human PDAC cells, and cell growth, migration and invasion were analyzed. In vivo heterotopic and orthotopic implantations using ESRP1 overexpression clones were performed and effects on pancreatic tumor volumes and hepatic and pulmonary metastases determined. ESRP1 immunoreactivity was strong in the nuclei of cancer cells in well-to-moderately differentiated PDACs, but weak in poorly-differentiated cancers. Well-to-moderately differentiated cancers also exhibited high FGFR-2 IIIb and low FGFR-2 IIIc expression, whereas this ratio was reversed in the poorly-differentiated cancers. Increased ESRP1 expression was associated with longer survival by comparison with low-ESRP1 expression, and PANC-1 cells engineered to express ESRP1 exhibited increased FGFR-2 IIIb expression and decreased migration and invasion in vitro, whereas ESRP1 siRNA-transfected KLM-1 cells exhibited increased FGFR-2 IIIc expression and increased cell growth, migration and invasion. In vivo, ESRP1-overexpressing clones formed significantly fewer liver metastases as compared with control clones. ESRP1 regulates the expression pattern of FGFR-2 isoforms, attenuates cell growth, migration, invasion, and metastasis, and is a favorable prognostic factor in PDAC. Therefore, devising mechanisms to up-regulate ESRP1 may exert a beneficial therapeutic effect in PDAC

Aspects of Non-minimal Gauge Mediation

A large class of non-minimal gauge mediation models, such as (semi-)direct gauge mediation, predict a hierarchy between the masses of the supersymmetric standard model gauginos and those of scalar particles. We perform a comprehensive study of these non-minimal gauge mediation models, including mass calculations in semi-direct gauge mediation, to illustrate these features, and discuss the phenomenology of the models. We point out that the cosmological gravitino problem places stringent constraints on mass splittings, when the Bino is the NLSP. However, the GUT relation of the gaugino masses is broken unlike the case of minimal gauge mediation, and an NLSP other than the Bino (especially the gluino NLSP) becomes possible, relaxing the cosmological constraints. We also discuss the collider signals of the models.Comment: 56 pages, 8 figures; v2:minor corrections, references added; v3:minor correction

Tug-of-war between actomyosin-driven antagonistic forces determines the positioning symmetry in cell-sized confinement

細胞内構造の配置対称性が決まる仕組みを解明 --人工細胞と物理学からメカニズムに迫る--. 京都大学プレスリリース. 2020-06-15.The tug-of-war at the heart of cellular symmetry. 京都大学プレスリリース. 2020-06-26.Symmetric or asymmetric positioning of intracellular structures including the nucleus and mitotic spindle steers various biological processes such as cell migration, division, and embryogenesis. In typical animal cells, both a sparse actomyosin meshwork in the cytoplasm and a dense actomyosin cortex underneath the cell membrane participate in the intracellular positioning. However, it remains unclear how these coexisting actomyosin structures regulate the positioning symmetry. To reveal the potential mechanism, we construct an in vitro model composed of cytoplasmic extracts and nucleus-like clusters confined in droplets. Here we find that periodic centripetal actomyosin waves contract from the droplet boundary push clusters to the center in large droplets, while network percolation of bulk actomyosin pulls clusters to the edge in small droplets. An active gel model quantitatively reproduces molecular perturbation experiments, which reveals that the tug-of-war between two distinct actomyosin networks with different maturation time-scales determines the positioning symmetry

MAP-based denoising of dynamic PET data for quantitative receptor imaging

We propose a MAP-based denoising method for PET functional imaging. In PET, time activity curves in tissue (tTAC) is analyzed using LoganGraphical Analysis (LGA) as distribution volume (V). In the method, a prior distribution of tTACs is computed based on a set of simulated ones, which are outputs from a compartment model that describes the behavior of administered radioligand in tissue. Drawing a set of rate constants, that is a system parameter of the model, from a uniform distribution covering physiologically feasible range, we can obtain a corresponding simulated tTACs, which compose a manifold in a space of tTACs. Given a measured tTAC, we compute the posterior probability distribution at each point on this manifold. The denoised tTAC is derived as the point on the manifold where the computed posterior probability is the maximum.The purpose of this study was to experimentally analyze the relationship between the prior probability and the resultant estimates of V. For this analysis, we selected [11C]SA4503 as a radioligand and computed three prior probability distributions of the tTACs. Using each of these priors, we denoised a set of synthetic noisy tTACs and a set of clinical ones, and evaluated the estimation errors of V. The results showed that the estimated V became most accurate when the manifold was enough large that the maximum of the posterior probability was never located at the boundary of the manifold.SPIE Medical Imaging 201

Using Micromanipulation to Analyze Control of Vertebrate Meiotic Spindle Size

The polymerization/depolymerization dynamics of microtubules (MTs) have been reported to contribute to control of the size and shape of spindles, but quantitative analysis of how the size and shape correlate with the amount and density of MTs in the spindle remains incomplete. Here, we measured these parameters using 3D microscopy of meiotic spindles that self-organized in Xenopus egg extracts and presented a simple equation describing the relationship among these parameters. To examine the validity of the equation, we cut the spindle into two fragments along the pole-to-pole axis by micromanipulation techniques that rapidly decrease the amount of MTs. The spheroidal shape spontaneously recovered within 5 min, but the size of each fragment remained small. The equation we obtained quantitatively describes how the spindle size correlates with the amount of MTs while maintaining the shape and the MT density

Unbiased Logan Graphical Analysis Using the Renormalization Method

Objective: The Logan Graphical Analysis (LGA) is used for imaging a distribution volume VT. For LGA, we compute a set of {(x(t), y(t))} from the measured time-activity curves in tissue (tTAC) and plasma (pTAC) to find a best-fitting line y(t) = alpha x(t) + beta. (Eq.1).Here, x(t) and y(t) are defined as a ratio of an integrated pTAC over tTAC and an integrated tTAC over tTAC, respectively. As known[1] , linear regression (LR) underestimates VT and its unbiased estimator is expected. Renormalization Method (RM) [2] enables an unbiased maximum likelihood estimation under the existence of inhomogeneous noises both in x and y by successive evaluation of bias. In this study, the applicability of RM to LGA was investigated.\nMethods: Let Xt = (x(t),y(t),1)T and U=(u1,u2,u3)T. Then, we can rewrite (Eq.1) as XtTU = 0, where |U| = 1 and VT = -u1/u2. Let Ct denote the covariance matrix of the noise of Xt. The maximum likelihood estimates of ui minimize JMLE(U) = SIGMA tWt(U) (XtTU)2, where Wt(U)=1/(UTCtU). Though, the perturbation theorem tells us that the estimates become biased.RM removes the bias by iteratively minimizing JREN(U) instead of JMLE : JREN(U) = SIGMA tWt(U) {(XtTU)2 - UTCtU}, where the last term compensates the bias. In RM, the covariance matrix Ct should be given, and it is unknown in advance. Thus, a set of voxel-based noisy TACs were simulated using physiologically plausible kinetic parameters, and the mean of Ct was calculated from the set of simulated TACs.We applied RM and LM to synthesized tTACs and to real one of [11C]SA4503-PET. For generating the synthesized data, we simulated a set of voxel-based tTACs using a measured pTAC and the rate constant of [11C]SA4503 [3].\nResults: [pic_01] The simulation results are summarized in Fig. (A). RM plotted in red was almost identical (y=0.99x+0.23, r2=1.00), and LR plotted in blue showed the underestimation especially in large VT (y=0.70x+6.14, r2=0.94). The estimation of deviation was larger than that of LM. However, RM successfully suppressed the bias.The figures (B) and (C) show the results of imaging of VT obtained from the real data by RM and by LR, respectively. For the estimation, t* was set to be 15min post-injection. The computational time for RM was 10 min for 60 thousands voxels. RM gave brighter images than LR, and improved their contrast.\nConclusions: For computing unbiased estimates, we introduced RM. We estimated the average of each Ct based on simulations. Simulation results showed that RM suppresses the bias and has the potential to realize bias-free parametric imaging of VT.\nReferences:[1] Slifstein et al., J Nucl Med, 41, 12, 2000.[2] Kanatani, IEEE PAMI, 16, 3, 320-326, 1994.[3] Sakata et al., NeuroImage, 35, 1-8, 2007.Brain\u2709 & BrainPET\u270

A Noise Reduction Method for Graphical Analyses with KL-Expansion during Transient Equilibrium Condition

Objectives: Graphical analyses have advantages in unnecessary for the assumption of the number of compartments and fast computation. However, there is a large noise-induced bias or variability in the estimationof total volume of distribution (VT) [1]. Principal component analysis based method (mPCA) was proposed for a noise reduction in Logan graphical analysis (LGA) [2], which might lead to a deformation of kinetics of timeactivity curves (TACs). This study proposes a new noise reduction method for graphical analyses by Karhunen- Loève expansion (mKLE), which aims to maintain kinetics of voxel-based TACs.\nMethods: mKLE is based on the assumption that the ratio of a TAC to the input function is approximately constant after a certain time (t*) (transient equilibrium condition). Then the TAC after t* can be represented by one basis function. The basis function is expressed as an axis which passes across the origin and minimizesthe distance from TACs in feature space. mKLE was applied to a set of TACs to calculate the basis function. The noise-reduced TACs were obtained by projecting voxel-based TACs onto the basis function. mKLE was tested on human PET data of [11C]TMSX, an adenosine A2A antagonist radioligand [3]. Dynamic PET scans were acquired for 1 hour, and metabolite-corrected arterial input function was obtained. We tested 3 versions: without noise reduction and with mKLE or mPCA. The dynamic data were analyzed by LGA and multilinear analysis (MA1) to calculate the parametric VT images. The regional mean value of VT was calculated from the striatum drawn on the summed PET image. For all graphical methods, t* was set to be 30 min postinjection.\nResults: Figure (A) shows a typical example of TAC from a voxel: an original TAC, the noise-reduced TACs by mKLE and mPCA, and the ROI-averaged TAC. The mKLE estimated TAC agreed well with the ROI-averaged TAC. Figure (B) shows the VT images from the same subject of Fig. (A). In the ROI-averaged TAC, the estimated VT values of LGA and MA1 were the same as 1.23 mL/cm3. The lowest value of VT was given byLGA without noise-reduction (1.12 +- 0.17 mL/cm3). Both mKLE and mPCA similarly improved VT values estimated with LGA (1.23 +- 0.19 mL/cm3 for mKLE, 1.24 +- 0.18 mL/cm3 for mPCA). On the other hand, only mKLE improved VT estimated with MA1 (1.24 +- 0.18 mL/cm3), while mPCA caused overestimation and large variability (1.37 +- 3.21 mL/cm3).\nConclusions: The experimental results suggest that the proposed method is promising for noise reduction of VT imaging by graphical analyses.[pic_01]\nReferences:[1] Slifstein, et al., J Nucl Med 41, 2083-2088, 2000.[2] Joshi, et al., J Cereb Blood Flow Metab 28, 852-865, 2008.[3] Ishiwata, et al., J Nucl Med, 41:345-354, 2000.Brain\u2709 & BrainPET\u270