High glucose regulates cyclin D1/E of human mesenchymal stem cells through TGF‐β1 expression via Ca2+/PKC/MAPKs and PI3K/Akt/mTOR signal pathways

The elucidation of factors that support human mesenchymal stem cells (hMSCs) growth has remained unresolved partly because of the reliance of many researchers on ill‐defined, proprietary medium formulation. Thus, we investigated the effects of high glucose (D ‐glucose, 25 mM) on hMSCs proliferation. High glucose significantly increased [³H]‐thymidine incorporation and cell‐cycle regulatory protein expression levels compared with 5 mM D ‐glucose or 25 mM L ‐glucose. In addition, high glucose increased transforming growth factor‐β1 (TGF‐β₁) mRNA and protein expression levels. High glucose‐induced cell‐cycle regulatory protein expression levels and [³H]‐thymidine incorporation, which were inhibited by TGF‐β₁ siRNA transfection and TGF‐β₁ neutralizing antibody treatment. High glucose‐induced phosphorylation of protein kinase C (PKC), p44/42 mitogen‐activated protein kinases (MAPKs), p38 MAPK, Akt, and mammalian target of rapamycin (mTOR) in a time‐dependent manner. Pretreatment of PKC inhibitors (staurosporine, 10^?6 M; bisindolylmaleimide I, 10^?6 M), LY 294002 (PI3 kinase inhibitor, 10^?6 M), Akt inhibitor (10^?5 M), PD 98059 (p44/42 MAPKs inhibitor, 10^?5 M), SB 203580 (p38 MAPK inhibitor, 10^?6 M), and rapamycin (mTOR inhibitor, 10^?8 M) blocked the high glucose‐induced cellular proliferation and TGF‐β₁ protein expression. In conclusion, high glucose stimulated hMSCs proliferation through TGF‐β₁ expression via Ca²⁺/PKC/MAPKs as well as PI3K/Akt/mTOR signal pathways. J. Cell. Physiol. 224:59–70, 2010 © 2010 Wiley‐Liss, Inc.     

A Principled Approach to Deriving Approximate Conditional Sampling Distributions in Population Genetics Models with Recombination

The multilocus conditional sampling distribution (CSD) describes the probability that an additionally sampled DNA sequence is of a certain type, given that a collection of sequences has already been observed. The CSD has a wide range of applications in both computational biology and population genomics analysis, including phasing genotype data into haplotype data, imputing missing data, estimating recombination rates, inferring local ancestry in admixed populations, and importance sampling of coalescent genealogies. Unfortunately, the true CSD under the coalescent with recombination is not known, so approximations, formulated as hidden Markov models, have been proposed in the past. These approximations have led to a number of useful statistical tools, but it is important to recognize that they were not derived from, though were certainly motivated by, principles underlying the coalescent process. The goal of this article is to develop a principled approach to derive improved CSDs directly from the underlying population genetics model. Our approach is based on the diffusion process approximation and the resulting mathematical expressions admit intuitive genealogical interpretations, which we utilize to introduce further approximations and make our method scalable in the number of loci. The general algorithm presented here applies to an arbitrary number of loci and an arbitrary finite-alleles recurrent mutation model. Empirical results are provided to demonstrate that our new CSDs are in general substantially more accurate than previously proposed approximations.THE probability of observing a sample of DNA sequences under a given population genetics model—which is referred to as the sampling probability or likelihood—plays an important role in a wide range of problems in a genetic variation study. When recombination is involved, however, obtaining an analytic formula for the sampling probability has hitherto remained a challenging open problem (see Jenkins and Song 2009, 2010 for recent progress on this problem). As such, much research (Griffiths and Marjoram 1996; Kuhner et al. 2000; Nielsen 2000; Stephens and Donnelly 2000; Fearnhead and Donnelly 2001; De Iorio and Griffiths 2004a,b; Fearnhead and Smith 2005; Griffiths et al. 2008; Wang and Rannala 2008) has focused on developing Monte Carlo methods on the basis of the coalescent with recombination (Griffiths 1981; Kingman 1982a,b; Hudson 1983), a well-established mathematical framework that models the genealogical history of sample chromosomes. These Monte Carlo-based full-likelihood methods mark an important development in population genetics analysis, but a well-known obstacle to their utility is that they tend to be computationally intensive. For a whole-genome variation study, approximations are often unavoidable, and it is therefore important to think of ways to minimize the trade-off between scalability and accuracy.A popular likelihood-based approximation method that has had a significant impact on population genetics analysis is the following approach introduced by Li and Stephens (2003): Given a set Φ of model parameters (e.g., mutation rate, recombination rate, etc.), the joint probability p(h₁, … , h_n | Φ) of observing a set {h₁, … , h_n} of haplotypes sampled from a population can be decomposed as a product of conditional sampling distributions (CSDs), denoted by π,(1)where π(h_k+1|h₁, …, h_k, Φ) is the probability of an additionally sampled haplotype being of type h_k+1, given a set of already observed haplotypes h₁, …, h_k. In the presence of recombination, the true CSD π is unknown, so Li and Stephens proposed using an approximate CSD in place of π, thus obtaining the following approximation of the joint probability:(2)Li and Stephens referred to this approximation as the product of approximate conditionals (PAC) model. In general, the closer is to the true CSD π, the more accurate the PAC model becomes. Notable applications and extensions of this framework include estimating crossover rates (Li and Stephens 2003; Crawford et al. 2004) and gene conversion parameters (Gay et al. 2007; Yin et al. 2009), phasing genotype data into haplotype data (Stephens and Scheet 2005; Scheet and Stephens 2006), imputing missing data to improve power in association mapping (Stephens and Scheet 2005; Li and Abecasis 2006; Marchini et al. 2007; Howie et al. 2009), inferring local ancestry in admixed populations (Price et al. 2009), inferring human colonization history (Hellenthal et al. 2008), inferring demography (Davison et al. 2009), and so on.Another problem in which the CSD plays a fundamental role is importance sampling of genealogies under the coalescent process (Stephens and Donnelly 2000; Fearnhead and Donnelly 2001; De Iorio and Griffiths 2004a,b; Fearnhead and Smith 2005; Griffiths et al. 2008). In this context, the optimal proposal distribution can be written in terms of the CSD π (Stephens and Donnelly 2000), and as in the PAC model, an approximate CSD may be used in place of π. The performance of an importance sampling scheme depends critically on the proposal distribution and therefore on the accuracy of the approximation . Often in conjunction with composite-likelihood frameworks (Hudson 2001; Fearnhead and Donnelly 2002), importance sampling has been used in estimating fine-scale recombination rates (McVean et al. 2004; Fearnhead and Smith 2005; Johnson and Slatkin 2009).So far, a significant scope of intuition has gone into choosing the approximate CSDs used in these problems (Marjoram and Tavaré 2006). In the case of completely linked loci, Stephens and Donnelly (2000) suggested constructing an approximation by assuming that the additional haplotype h_k+1 is an imperfect copy of one of the first k haplotypes, with copying errors corresponding to mutation. Fearnhead and Donnelly (2001) generalized this construction to include crossover recombination, assuming that the haplotype h_k+1 is an imperfect mosaic of the first k haplotypes (i.e., h_k+1 is obtained by copying segments from h₁, …, h_k, where crossover recombination can change the haplotype from which copying is performed). The associated CSD, which we denote by , can be interpreted as a hidden Markov model and so admits an efficient dynamic programming solution. Finally, Li and Stephens (2003) proposed a modification to Fearnhead and Donnelly''s model that limits the hidden state space, thereby providing a computational simplification; we denote the corresponding approximate CSD by .Although these approaches are computationally appealing, it is important to note that they are not derived from, though are certainly motivated by, principles underlying typical population genetics models, in particular the coalescent process (Griffiths 1981; Kingman 1982a,b; Hudson 1983). The main objective of this article is to develop a principled technique to derive an improved CSD directly from the underlying population genetics model. Rather than relying on intuition, we base our work on mathematical foundation. The theoretical framework we employ is the diffusion process. De Iorio and Griffiths (2004a,b) first introduced the diffusion-generator approximation technique to obtain an approximate CSD in the case of a single locus (i.e., no recombination). Griffiths et al. (2008) later extended the approach to two loci to include crossover recombination, assuming a parent-independent mutation model at each locus. In this article, we extend the framework to develop a general algorithm that applies to an arbitrary number of loci and an arbitrary finite-alleles recurrent mutation model.Our work can be summarized as follows. Using the diffusion-generator approximation technique, we derive a recursion relation satisfied by an approximate CSD. This recursion can be used to construct a closed system of coupled linear equations, in which the conditional sampling probability of interest appears as one of the unknown variables. The system of equations can be solved using standard numerical analysis techniques. However, the size of the system grows superexponentially with the number of loci and, consequently, so does the running time. To remedy this drawback, we introduce additional approximations to make our approach scalable in the number of loci. Specifically, the recursion admits an intuitive genealogical interpretation, and, on the basis of this interpretation, we propose modifications to the recursion, which then can be easily solved using dynamic programming. The computational complexity of the modified algorithm is polynomial in the number of loci, and, importantly, the resulting CSD has little loss of accuracy compared to that following from the full recursion.The accuracy of approximate CSDs has not been discussed much in the literature, except in the application-specific context for which they are being employed. In this article, we carry out an empirical study to explicitly test the accuracy of various CSDs and demonstrate that our new CSDs are in general substantially more accurate than previously proposed approximations. We also consider the PAC framework and show that our approximations also produce more accurate PAC-likelihood estimates. We note that for the maximum-likelihood estimation of recombination rates, the actual value of the likelihood may not be so important, as long as it is maximized near the true recombination rate. However, in many other applications—e.g., phasing genotype data into haplotype data, imputing missing data, importance sampling, and so on—the accuracy of the CSD and PAC-likelihood function over a wide range of parameter values may be important. Thus, we believe that the theoretical work presented here will have several practical implications; our method can be applied in a wide range of statistical tools that use CSDs, improving their accuracy.The remainder of this article is organized as follows. To provide intuition for the ensuing mathematics, we first describe a genealogical process that gives rise to our CSD. Using our genealogical interpretation, we consider two additional approximations and relate these to previously proposed CSDs. Then, in the following section, we derive our CSD using the diffusion-generator approach and provide mathematical statements for the additional approximations; some interesting limiting behavior is also described there. This section is self-contained and may be skipped by the reader uninterested in mathematical details. Finally, in the subsequent section, we carry out a simulation study to compare the accuracy of various approximate CSDs and demonstrate that ours are generally the most accurate.     

Regioselective hydroxylation of daidzein using P450 (CYP105D7) from Streptomyces avermitilis MA4680

Regiospecific 3′‐hydroxylation reaction of daidzein was performed with CYP105D7 from Streptomyces avermitilis MA4680 expressed in Escherichia coli. The apparent K_m and k_cat values of CYP105D7 for daidzein were 21.83 ± 6.3 µM and 15.01 ± 0.6 min^?1 in the presence of 1 µM of CYP105D7, putidaredoxin (CamB) and putidaredoxin reductase (CamA), respectively. When CYP105D7 was expressed in S. avermitilis MA4680, its cytochrome P450 activity was confirmed by the CO‐difference spectra at 450 nm using the whole cell extract. When the whole‐cell reaction for the 3′‐hydroxylation reaction of daidzein was carried out with 100 µM of daidzein in 100 mM of phosphate buffer (pH 7.5), the recombinant S. avermitilis grown in R2YE media overexpressing CYP105D7 and ferredoxin FdxH (SAV7470) showed a 3.6‐fold higher conversion yield (24%) than the corresponding wild type cell (6.7%). In a 7 L (working volume 3 L) jar fermentor, the recombinants S. avermitilis grown in R2YE media produced 112.5 mg of 7,3′,4′‐trihydroxyisoflavone (i.e., 29.5% conversion yield) from 381 mg of daidzein in 15 h. Biotechnol. Bioeng. 2010. 105: 697–704. © 2009 Wiley Periodicals.     

DJ-1, an oncogene and causative gene for familial Parkinson’s disease, is essential for SV40 transformation in mouse fibroblasts through up-regulation of c-Myc

Simian virus 40 (SV40) is a tumor virus and its early gene product large T-antigen (LT) is responsible for the transforming activity of SV40. Parkinson’s disease causative gene DJ-1 is also a ras-dependent oncogene, but the mechanism of its oncogene function is still not known. In this study, we found that there were no transformed foci when fibroblasts from DJ-1-knockout mice were transfected with LT. We also found that DJ-1 directly bound to LT and that the expression level of c-Myc in transformed cells was parallel to that of DJ-1. These findings indicate that DJ-1 is essential for SV40 transformation.

Structured summary

MINT-7988969: DJ-1 (uniprotkb:Q99497) binds (MI:0407) to LT SV40 (uniprotkb:P03070) by pull down (MI:0096) MINT-7988948: LT SV40 (uniprotkb:P03070) physically interacts (MI:0914) with DJ-1 (uniprotkb:Q99LX0) and p53 (uniprotkb:P02340) by anti bait coimmunoprecipitation (MI:0006)     

The small RNA expression profile of the developing murine urinary and reproductive systems

microRNAs (miRNAs) are small non-coding RNAs with fundamental roles in the regulation of gene expression. miRNAs assemble with Argonaute (Ago) proteins to miRNA-protein complexes (miRNPs), which interact with distinct binding sites on mRNAs and regulate gene expression. Specific miRNAs are key regulators of tissue and organ development and it has been shown in mammals that miRNAs are also involved in the pathogenesis of many diseases including cancer. Here, we have characterized the miRNA expression profile of the developing murine genitourinary system. Using a computational approach, we have identified several miRNAs that are specific for the analyzed tissues or the developmental stage. Our comprehensive miRNA expression atlas of the developing genitourinary system forms an invaluable basis for further functional in vivo studies.     

General Epistatic Models of the Risk of Complex Diseases

The range of possible gene interactions in a multilocus model of a complex inherited disease is studied by exploring genotype-specific risks subject to the constraint that the allele frequencies and marginal risks are known. We quantify the effect of gene interactions by defining the interaction ratio, , where K_R is the recurrence risk to relatives with relationship R for the true model and is the recurrence risk to relatives for a multiplicative model with the same marginal risks. We use a Markov chain Monte Carlo (MCMC) procedure to sample from the space of possible models. We find that the average of C_R increases with the number of loci for both low frequency (p = 0.03) and higher frequency (p = 0.25) causative alleles. Furthermore, the probability that C_R > 1 is nearly 1. Similar results are obtained when more weight is given to risk models that are closer to the comparable multiplicative model. These results imply that, in general, gene interactions will result in greater heritability of a complex inherited disease than is expected on the basis of a multiplicative model of interactions and hence may provide a partial explanation for the problem of missing heritability of complex diseases.ALTHOUGH many genome-wide association studies (GWAS) have been performed and have found hundreds of SNPs associated with higher risk of complex inherited diseases, those SNPs so far account for only a small fraction of the inherited risk of those diseases (Altshuler et al. 2008). Several not mutually exclusive explanations have been proposed for the “missing heritability,” i.e., the heritability that is not yet accounted for by SNPs found in GWAS (Manolio et al. 2009): (i) common alleles of small effect that have not been found because GWAS done so far have been underpowered, (ii) low-frequency alleles of moderate effect that are difficult to find using HapMap SNPs, (iii) rare copy-number variants that are not in strong linkage disequilibrium (LD) with HapMap SNPs, (iv) inherited epigenetic factors that are not in strong LD with HapMap SNPs, and (v) interactions among causative alleles that conceal their true contribution to heritability. In this article we investigate the last possibility and determine the extent to which interactions may account for missing heritability.Our analysis is in the same spirit as that of Culverhouse et al. (2002). We assume that the risk of being affected by a complex disease is determined by an individual''s genotype at two or more loci and that the frequencies of causative alleles and the average risks for each one-locus genotype (the marginal risks) are known. Culverhouse et al. (2002) assumed the marginal risks were the same for all genotypes and all loci. In that case, causative alleles have odds ratios of 1; they contribute to risk only through their interactions. Culverhouse et al. found the risk function that maximized the heritability and showed that the maximum possible heritability attributable to interactions increased with the number of loci. They concluded that it is quite possible that interactions among loci that have no main effect could contribute substantially to the heritability of a complex disease and indeed could account for “virtually all the variation in affection status for diseases with any prevalence” (Culverhouse et al. 2002, p. 468).We generalize the analysis of Culverhouse et al. in three ways. First, we allow causative alleles to have odds ratios >1. Second, we explore the entire space of models instead of focusing only on the risk model that maximizes heritability. Third, we examine how the importance of gene interactions depends on the “distance” between a risk model and a comparable multiplicative model. We show that gene interactions can substantially increase the heritability of risk as measured by recurrence risk, K_R, and that the effect increases with the number of loci carrying causative alleles. Furthermore, we show that these results are true even if more weight is given to models that are closer to a comparable multiplicative model.Geometrically, the space of feasible genotype-specific risks subject to the aforementioned constraints (i.e., that the allele frequencies and marginal risks are known) corresponds to a high-dimensional convex polytope, and the computational problem of interest involves integrating a quadratic function over the polytope. The dimension of the polytope grows exponentially with the number of loci, and, therefore, analytic computation is intractable for more than two loci. Hence, we devise a Monte Carlo approach to tackle the problem. Note that, because of high dimensionality, rejection algorithms are not appropriate for this kind of problem. We instead employ a Markov chain Monte Carlo (MCMC) algorithm based on a random walk that always stays inside the polytope. We present empirical results for up to five loci and obtain a closed-form formula for the minimum of K_R over the polytope; the latter result applies to an arbitrary number of loci. Interestingly, the minimum of K_R decreases as the number L of loci increases, but the average of K_R over the polytope increases with L.     

Calcium-dependent Association of Calmodulin with the Rubella Virus Nonstructural Protease Domain

The rubella virus (RUBV) nonstructural (NS) protease domain, a Ca²⁺- and Zn²⁺-binding papain-like cysteine protease domain within the nonstructural replicase polyprotein precursor, is responsible for the self-cleavage of the precursor into two mature products, P150 and P90, that compose the replication complex that mediates viral RNA replication; the NS protease resides at the C terminus of P150. Here we report the Ca²⁺-dependent, stoichiometric association of calmodulin (CaM) with the RUBV NS protease. Co-immunoprecipitation and pulldown assays coupled with site-directed mutagenesis demonstrated that both the P150 protein and a 110-residue minidomain within NS protease interacted directly with Ca²⁺/CaM. The specific interaction was mapped to a putative CaM-binding domain. A 32-mer peptide (residues 1152–1183, denoted as RUBpep) containing the putative CaM-binding domain was used to investigate the association of RUBV NS protease with CaM or its N- and C-terminal subdomains. We found that RUBpep bound to Ca²⁺/CaM with a dissociation constant of 100–300 nm. The C-terminal subdomain of CaM preferentially bound to RUBpep with an affinity 12.5-fold stronger than the N-terminal subdomain. Fluorescence, circular dichroism and NMR spectroscopic studies revealed a “wrapping around” mode of interaction between RUBpep and Ca²⁺/CaM with substantially more helical structure in RUBpep and a global structural change in CaM upon complex formation. Using a site-directed mutagenesis approach, we further demonstrated that association of CaM with the CaM-binding domain in the RUBV NS protease was necessary for NS protease activity and infectivity.     

Evidence of Different Ploidy Eggs Produced by Diploid F2 Hybrids of Carassius auratus (♀) × Cyprinus carpio (♂)

Based on the presence of three types of eggs with different diameters 0.13, 0.17 and 0.2 cm, we made two crosses: F₂ (♀) × diploid red crucian carp (♂), and F₂ (♀) × F₁₀ tetraploid (♂). The ploidy levels of the progeny of the two crosses were examined by chromosome counting and DNA content measurement by flow cytometer. In the offspring of the former cross, tetraploids, trip-loids, and diploid were obtained. In the progeny of the latter cross, tetraploids and triploids were observed. The production of the different ploidy level fish in the progeny of the two crosses provided a further evidence that F₂ might generate triploid, diploid and haploid eggs. The presence of the male tetraploid found in F₂ (♀) × diploid red crucian carp (♂) suggested that the genotype of XXXY probably existed in the tetraploid progeny. The gonadal structures of the tetraploids and triploids indicated that both female and male tetraploids were fertile and the triploids were sterile. We concluded that the formations of different ploidy level eggs from F₂ were contributed by endoreduplication and fusion of germ cells.     

花曲柳窄吉丁的空间分布

2004年4月~9月,在天津市大港区官港森林公园对花曲柳窄吉丁Agrilus planipennis的空间分布和垂直分布进行了调查研究,应用扩散系数(C)等6种指数法分析测定,确定了该虫在林地中的空间分布呈聚集分布,分布的基本成分是个体群。扩散蔓延规律为聚集型扩散。花曲柳窄吉丁幼虫在树干的垂直分布主要区间在50~150 cm之间。对幼虫垂直分布与树干高度的关系进行分析,其符合立方曲线,曲线方程为:Y=-5.142 9+17.943 7X+21.662 3X2-14.545X3;相关系数R2=0.915,P=0.013。