Multivariate exploratory tools for microarray data analysis

The ultimate success of microarray technology in basic and applied biological sciences depends critically on the development of statistical methods for gene expression data analysis. The most widely used tests for differential expression of genes are essentially univariate. Such tests disregard the multidimensional structure of microarray data. Multivariate methods are needed to utilize the information hidden in gene interactions and hence to provide more powerful and biologically meaningful methods for finding subsets of differentially expressed genes. The objective of this paper is to develop methods of multidimensional search for biologically significant genes, considering expression signals as mutually dependent random variables. To attain these ends, we consider the utility of a pertinent distance between random vectors and its empirical counterpart constructed from gene expression data. The distance furnishes exploratory procedures aimed at finding a target subset of differentially expressed genes. To determine the size of the target subset, we resort to successive elimination of smaller subsets resulting from each step of a random search algorithm based on maximization of the proposed distance. Different stopping rules associated with this procedure are evaluated. The usefulness of the proposed approach is illustrated with an application to the analysis of two sets of gene expression data.     

Down regulation of ribosomal protein mRNAs during neuronal differentiation of human NTERA2 cells

We have analysed the expression of 32 ribosomal protein (RP) mRNAs during retinoic acid induced neuronal differentiation of human NTERA2 cells. Except for a new S27 variant (S27v), all were down regulated both in selectively replated differentiated neurons and the most differentiated continuous cultures, i.e., non-replated cultures. However, the expression profiles of the individual RP mRNAs were different, most (L3, L7, L8, L10, L13, L23a, L27a, L36a, L39, P0, S2, S3, S3a, S4X, S6, S9, S12, S13, S16, S19, S20, S23, and S27a) exhibited a constant down regulation, whereas a few were either initially constant (L11, L32, S8, and S11) or up regulated (L6, L15, L17, L31, and S27y) and then down regulated. The expression of S27v remained elevated in the most differentiated continuous cultures but was down regulated in replated differentiated neurons. The down regulation of RP mRNAs was variable: the expression levels in differentiated replated neurons were between 10% (S3) and 90% (S11) of the levels in undifferentiated cells. The ratio between rRNA and RP mRNA changed during the differentiation; in differentiated neurons there were, on average, about half the number of RP mRNAs per rRNA as compared to undifferentiated cells. The expression profiles of a few translation-related proteins were also determined. EF1alpha1, EF1beta1, and EF1delta were down regulated, whereas the expression of the neuron and muscle specific EF1alpha2 increased. The reduction in the expression of RP mRNAs was coordinated with a reduction in the expression level of the proliferation marker PCNA. The expression levels of most RP mRNAs were lower in purified differentiated post-mitotic neurons than in the most differentiated continuous cultures, despite similar levels of PCNA, suggesting that both the differentiation state and the proliferative status of the cells affect the expression of RP mRNAs.     

Differential expression of four genes encoding 1-aminocyclopropane-1-carboxylate synthase in Lupinus albus during germination, and in response to indole-3-acetic acid and wounding

1-Aminocyclopropane-1-carboxylate (ACC) synthase (ACS; EC 4.4.1.14) is the key regulatory enzyme of the ethylene biosynthetic pathway and is encoded by a multigene family in Arabidopsis thaliana, tomato, mung bean and other plants. Southern blot analysis revealed the existence of at least five ACS genes in white lupin (Lupinus albus L.) genome. Four complete and one partial sequences representing different ACS genes were cloned from the lupin genomic library. The levels of expression of two of the genes, LA-ACS1 and LA-ACS3, were found to increase after hypocotyl wounding. Apparently, these two genes were up-regulated by exogenous IAA treatment of seedlings. The LA-ACS3 mRNA levels were also elevated in the apical part of hypocotyl, which is reported to contain a high endogenous auxin concentration. This gene may be involved in the auxin- and ethylene-controlled apical hook formation. The expression of the LA-ACS4 gene was found to be almost undetectable. This gene may represent a “silent” twin of LA-ACS5 as these two genes share a considerable level of homology in coding and non-coding regions. The LA-ACS5 mRNA is strongly up-regulated in the embryonic axis of germinating seeds at the time of radicle emergence, and was also found in roots and hypocotyls of lupin seedlings. Received: 19 July 1999 / Accepted: 3 March 2000     

多模态医学教育体制下基层影像学人才培训创新模式探索

在医学教育模式多元化背景下,通过将"集中强化培训"和"点线式网络化实战训练"等教学方法相结合,本文着重探索基层卫生机构中影像学医务人员继续教育新模式。通过为基层影像学医务人员提供快速扎实继续教育通道和平台,并在其实际工作中建立实用型会诊学习体系,以形成"精良多元、持续有序"的教育模式,解决基层人员"培训难,提高难,会诊难"的问题。新培训模式能够不断提高基层卫生机构影像学医务人员业务水平,为根本提升基层医疗诊断水平奠定基础。     

Evolving modular genetic regulatory networks with a recursive,top-down approach

Being able to design genetic regulatory networks (GRNs) to achieve a desired cellular function is one of the main goals of synthetic biology. However, determining minimal GRNs that produce desired time-series behaviors is non-trivial. In this paper, we propose a ‘top-down’ approach to evolving small GRNs and then use these to recursively boot-strap the identification of larger, more complex, modular GRNs. We start with relatively dense GRNs and then use differential evolution (DE) to evolve interaction coefficients. When the target dynamical behavior is found embedded in a dense GRN, we narrow the focus of the search and begin aggressively pruning out excess interactions at the end of each generation. We first show that the method can quickly rediscover known small GRNs for a toggle switch and an oscillatory circuit. Next we include these GRNs as non-evolvable subnetworks in the subsequent evolution of more complex, modular GRNs. Successful solutions found in canonical DE where we truncated small interactions to zero, with or without an interaction penalty term, invariably contained many excess interactions. In contrast, by incorporating aggressive pruning and the penalty term, the DE was able to find minimal or nearly minimal GRNs in all test problems.     

Direct and simple detection of recombinant proteins from cell lysates using differential scanning fluorimetry

A simple, inexpensive, and universal method to quantify the recombinant proteins in Escherichia coli cell lysate using differential scanning fluorimetry (DSF) is reported. This method is based on the precise correlation between Δ(fluorescence intensity) determined by DSF and the amount of protein in solution. We first demonstrated the effectiveness of the DSF method using two commercially available enzymes, α-amylase and cellobiase, and then confirmed its utility with two recombinant proteins, amylosucrase and maltogenic amylase, expressed in E. coli. The Δ(fluorescence intensity) in DSF analysis accurately correlated with the concentration of the purified enzymes as well as the recombinant proteins in E. coli cell lysates. The main advantage of this method over other techniques such as Western blotting, enzyme-linked immunosorbent assay (ELISA), and green fluorescence protein (GFP) fusion proteins is that intact recombinant protein can be quantified without the requirement of additional chemicals or modifications of the recombinant protein. This DSF assay can be performed using widely available equipment such as a real-time polymerase chain reaction (RT–PCR) instrument, microplates or microtubes, and fluorescent dye. This simple but powerful method can be easily applied in a wide range of research areas that require quantification of expressed recombinant proteins.     

Basic models for differential inhibition of enzymes

The possible preferential action exerted by an inhibitor on the transformation of one of two agonist substrates catalyzed by the same enzyme has recently been reported in studies on aldose reductase inhibition. This event was defined as “intra-site differential inhibition” and the molecules able to exert this action as “differential inhibitors”. This work presents some basic kinetic models describing differential inhibition. Using a simple analytic approach, the results show that differential inhibition can occur through either competitive or mixed type inhibition in which the inhibitor prevalently targets the free enzyme. The results may help in selecting molecules whose differential inhibitory action could be advantageous in controlling the activity of enzymes acting on more than one substrate.     

Computational modeling of epithelial–mesenchymal transformations

An epithelial–mesenchymal transformation (EMT) involves alterations in cell–cell and cell–matrix adhesion, the detachment of epithelial cells from their neighbors, the degradation of the basal lamina and acquisition of mesenchymal phenotype. Here we present Monte Carlo simulations for a specific EMT in early heart development: the formation of cardiac cushions. Cell rearrangements are described in accordance with Steinberg's differential adhesion hypothesis, which states that cells possess a type-dependent adhesion apparatus and are sufficiently motile to give rise to the tissue conformation with the largest number of strong bonds. We also implement epithelial and mesenchymal cell proliferation, cell type change and extracellular matrix production by mesenchymal cells. Our results show that an EMT is promoted more efficiently by an increase in cell–substrate adhesion than by a decrease in cell–cell adhesion. In addition to cushion tissue formation, the model also accounts for the phenomena of matrix invasion and mesenchymal condensation. We conclude that in order to maintain epithelial integrity during EMT the number of epithelial cells must increase at a controlled rate. Our model predictions are in qualitative agreement with available experimental data.