Logical design of oral glucose ingestion pattern minimizing blood glucose in humans

Excessive increase in blood glucose level after eating increases the risk of macroangiopathy, and a method for not increasing the postprandial blood glucose level is desired. However, a logical design method of the dietary ingestion pattern controlling the postprandial blood glucose level has not yet been established. We constructed a mathematical model of blood glucose control by oral glucose ingestion in three healthy human subjects, and predicted that intermittent ingestion 30 min apart was the optimal glucose ingestion patterns that minimized the peak value of blood glucose level. We confirmed with subjects that this intermittent pattern consistently decreased the peak value of blood glucose level. We also predicted insulin minimization pattern, and found that the intermittent ingestion 30 min apart was optimal, which is similar to that of glucose minimization pattern. Taken together, these results suggest that the glucose minimization is achieved by suppressing the peak value of insulin concentration, rather than by enhancing insulin concentration. This approach could be applied to design optimal dietary ingestion patterns

Adult phenology of two species of tiger beetles (Carabidae, Cicindelinae) and estimates of population size of Cylindela elisae , in Tottori Sand Dunes, Honshu, Japan in 2016.

2015年の調査に引き続き，2016年の夏季にも鳥取砂丘オアシス周辺で標識再捕により，当地で絶滅が心配されるエリザハンミョウの生息数を推定した。マークできた個体はエリザハンミョウが270（昨年は304），カワラハンミョウが170（昨年は77），コハンミョウが4（昨年は1：昨年調査の報告書である鶴崎ら2016では「コニワハンミョウ」と誤記）であった。Jolly-Seber法によるエリザハンミョウの個体数推定値はもっとも多かった調査日（6月28日と7月16日）でともに約1,460で，2015年の2,300個体よりも少なかった。おそらく2016年の夏季の高気温のため，成虫の出現期は2015年よりも早く推移し，6 月中旬には成虫が出現し，8月下旬には終息した。エリザハンミョウの幼虫の営巣地はオアシス周辺の尻無川右岸の裸地であるが，成虫は左岸のコウボウシバ群落でも見つかった。カワラハンミョウの成虫は6月下旬から10月上旬まで見られ，170個体をマークしたが再捕獲できた個体は皆無であった。コハンミョウは尻無川の近くで4個体マークしたが本種も再捕獲にいたらなかった。昨年の2個体（マークしたのは1個体）に続けての確認で，細々ではあるが，本種は当地で世代を繰り返している可能性が高い。 / Following a survey of the population size of a tiger beetle species, Cylindera elisae(Motschulsky,1859) in the Tottori Sand Dunes, Tottori City, in 2015 (Tsurusaki et al.2016), we estimated population size of　the same population of the same species also in 2016 by using mark-recapture experiments. A total of　270 adults of Cy. elisae , 170 adults of Chaetodera laetescripta (Motschulsky, 1860) and 4 adults of Myriochila　speculifera (Chevrolat, 1865) (This species was　erroneously recorded as Cicindela　transbaicalica japanensis　Chaudoir, 1863 in Tsurusaki et al. 2016) were individually marked during the summer in 2016.　None of　those　adults marked were recaptured for the　two latter species. The highest number of　adults of Cy. elisae estimated　by the Jolly-Seber method was ca. 1,460 recorded on both　28 June and 16 July

HDAC 阻害剤は Diethylstilbestrol による性腺刺激ホルモン細胞からのプロラクチン細胞への分化転換を抑制する

Diethylstilbestrol (DES), an estrogen agonist, increases prolactin (PRL) cells through transdifferentiation of follicle-stimulating hormone (FSH) and luteinizing hormone (LH) cells to PRL cells as well as proliferation of PRL cells in adult male mouse pituitary. Since hyperacetylation of histone H3 is implicated in the regulation of activation of various genes, we examined the effect of DES on the state of histone H3 acetylation. DES significantly reduced the immunohistochemical signal for acetylated histone H3 at lysine 9 (H3K9ac) in PRL, LH and FSH cells, but not for H3K18ac or H3K23ac. DES-treated mice were injected intraperitoneally with HDAC inhibitors (HDACi), sodium phenylbutyrate (NaPB) or valproic acid (VPA), to mimic the acetylation level of histone H3. As expected, HDACi treatment restored the level of H3K9ac expression in these cells, and also inhibited DES-induced increase in PRL cells. Furthermore, NaPB and VPA also abrogated the effects of DES on the population density of both LH and FSH cells. Similarly, the numbers of proliferating and apoptotic cells in the pituitary in NaPB- or VPA-treated mice were comparable to those of the control mice. Considered together, these results indicated that the acetylation level of histone H3 plays an important role in DES-induced transdifferentiation of LH to PRL cells as well as proliferation of PRL cells.長崎大学学位論文 学位記番号:博(医歯薬)甲第1128号 学位授与年月日:平成31年3月20日Author: Nandar Tun, Yasuaki Shibata, Myat Thu Soe, Myo Win Htun, Takehiko KojiCitation: Histochemistry and Cell Biology, 151(4), pp.291-303; 2018Nagasaki University (長崎大学)課程博

Laguerre Filter Analysis with Partial Least Square Regression Reveals a Priming Effect of ERK and CREB on c-FOS Induction.

Signaling networks are made up of limited numbers of molecules and yet can code information that controls different cellular states through temporal patterns and a combination of signaling molecules. In this study, we used a data-driven modeling approach, the Laguerre filter with partial least square regression, to describe how temporal and combinatorial patterns of signaling molecules are decoded by their downstream targets. The Laguerre filter is a time series model used to represent a nonlinear system based on Volterra series expansion. Furthermore, with this approach, each component of the Volterra series expansion is expanded by Laguerre basis functions. We combined two approaches, application of a Laguerre filter and partial least squares (PLS) regression, and applied the combined approach to analysis of a signal transduction network. We applied the Laguerre filter with PLS regression to identify input and output (IO) relationships between MAP kinases and the products of immediate early genes (IEGs). We found that Laguerre filter with PLS regression performs better than Laguerre filter with ordinary regression for the reproduction of a time series of IEGs. Analysis of the nonlinear characteristics extracted using the Laguerre filter revealed a priming effect of ERK and CREB on c-FOS induction. Specifically, we found that the effects of a first pulse of ERK enhance the subsequent effects on c-FOS induction of treatment with a second pulse of ERK, a finding consistent with prior molecular biological knowledge. The variable importance of projections and output loadings in PLS regression predicted the upstream dependency of each IEG. Thus, a Laguerre filter with partial least square regression approach appears to be a powerful method to find the processing mechanism of temporal patterns and combination of signaling molecules by their downstream gene expression

The mean VIP score and a dendrogram based on hierarchical clustering of input loading.

<p>(A) Averaged VIP scores of the input. (B) Computational and theoretical c-FOS time course given two pulsatile and impulsive pERK and pCREB inputs. For the top panels, c-FOS time course was simulated when two pulses were given as inputs. The bottom panels show the theoretical impulse responses when two impulses were given as inputs. The amplitude of the second impulse was tuned from 0 to 1. The calculation with nonlinear model is shown in blue and without nonlinearity is shown in red. (C) Hierarchical clustering as applied to the input loadings. The distance between two samples is defined as 1-<i>r</i> where <i>r</i> is the Pearson correlation. The distance between two clusters is defined as the shortest distance between two samples drawn from each of the clusters. The leaf nodes represent the output loadings, and the order of principle components is described in parenthesis. The loadings and scores of the input and output used for principal component analysis are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160548#pone.0160548.s005" target="_blank">S5 Fig</a>.</p

Development of Laguerre filter with PLS regression.

<p>(A) A schematic representation of 1^st and 2^nd Volterra kernels. If a nonlinearity exists in a system (such as the negative feedback as illustrated), the non-zero values appears in 2^nd kernel. (B) Examples of Laguerre polynomial functions with different parameters. (C) The network modules and input patterns used to create artificial input-output time series. We used 10 input patterns in total, including 2 input patterns for test models. 7 modules represent some of the commonly found biochemical network architectures. (D) The log-scale mean square error (MSE) of the four models for each modules. (E) Heatmap showing MSE of LF-PLS normalized by MSE of FIR-PLS.</p

pMAPKs and pCREB as inputs and immediate early gene expression as outputs.

<p>(A) The time series of pMAPKs and pCREB in response to NGF (5 ng/ml, red), PACAP (100 nM, blue), EGF (5 ng/ml, green), and anisomycin (50 ng/ml, black) were measured by quantitative image cytometry (QIC) at 3-min intervals for 180 min. One time series with respect to each growth factor is shown to facilitate visualization. Other time series are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160548#pone.0160548.s001" target="_blank">S1 Fig</a>. (B) The time series of the immediate early genes (IEGs) measured by QIC (circles) are shown, together with simulated results of Laguerre filter combined with partial least square (PLS) regression (LF-PLS) (solid lines). The color codes are same as described for panel A. These data, together with responses to other doses of the growth factors (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160548#pone.0160548.s001" target="_blank">S1 Fig</a>), were used for parameter estimation of LF-PLS. The circle plots in panels (A) and (B) were created based on data from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160548#pone.0160548.ref011" target="_blank">11</a>]. (C) The MSE of leave one out error of cross validation (LOO CV) for each method. The full MSE of LOO CV of Laguerre filter is shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160548#pone.0160548.s006" target="_blank">S6 Fig</a>. The numerical value of MSE of training data set and LOO CV are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160548#pone.0160548.s008" target="_blank">S1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160548#pone.0160548.s009" target="_blank">S2</a> Tables, respectively.</p

First and second order Volterra kernels.

<p>The solid line in upper panels indicates the 1st order Volterra kernel <i>k</i>₁ in the time space. The color in the lower panels indicates the 2nd order Volterra kernel <i>k</i>₂ in the time space.</p