Identifying Transport Behavior of Single-Molecule Trajectories

Models of biological diffusion-reaction systems require accurate classification of the underlying diffusive dynamics (e.g., Fickian, subdiffusive, or superdiffusive). We use a renormalization group operator to identify the anomalous (non-Fickian) diffusion behavior from a short trajectory of a single molecule. The method provides quantitative information about the underlying stochastic process, including its anomalous scaling exponent. The classification algorithm is first validated on simulated trajectories of known scaling. Then it is applied to experimental trajectories of microspheres diffusing in cytoplasm, revealing heterogeneous diffusive dynamics. The simplicity and robustness of this classification algorithm makes it an effective tool for analysis of rare stochastic events that occur in complex biological systems.     

Proteomics meets microbiology: technical advances in the global mapping of protein expression and function

The availability of complete genome sequences for a large number of pathogenic organisms has opened the door for large-scale proteomic studies to dissect both protein expression/regulation and function. This review highlights key proteomic methods including two-dimensional gel electrophoresis, reference mapping, protein expression profiling and recent advances in gel-free separation techniques that have made a significant impact on the resolution of complex proteomes. In addition, we highlight recent developments in the field of chemical proteomics, a branch of proteomics aimed at functionally profiling a proteome. These techniques include the development of activity-based probes and activity-based protein profiling methods as well as the use of synthetic small molecule libraries to screen for pharmacological tools to perturb basic biological processes. This review will focus on the applications of these technologies to the field of microbiology.     

Differential proteomic analysis during the vegetative phase change and the floral transition in Malus domestica

In order to identify the proteomic changes of apple (Malus domestica Borkh.) during the vegetative phase change and the floral transition, leaf protein of juvenile, adult vegetative and reproductive phase in a seedling ('Jonathan' × 'Golden Delicious') was extracted and analyzed by 2-D electrophoresis and Matrix-assisted laser desorption/ionization-time of flight mass spectrometry. Seventy two gel spots with significant expression differences between ontogenetic phases were obtained. Five protein spots were only detected in leaves of juvenile phase and 11 were not; 17 spots were found exclusively in adult vegetative leaves; and only one spot solely appeared in reproductive leaves while 12 did not. Twenty six of the differentially expressed proteins identified were involved in photosynthesis. Seven enzymes were related to respiration and carbohydrate metabolism. Fifteen other proteins also presented qualitative or quantitative differences among developmental phases. The spatial distribution of one differentially expressed protein, serine hydroxymethyltransferase, was confirmed by enzyme linked immunosorbent assay and immunohistochemistry. These results strongly support the idea that the vegetative phase change and the floral transition are regulated independently during developmental process.     

Nonparametric functional mapping of quantitative trait loci

Summary .  Functional mapping is a useful tool for mapping quantitative trait loci (QTL) that control dynamic traits. It incorporates mathematical aspects of biological processes into the mixture model-based likelihood setting for QTL mapping, thus increasing the power of QTL detection and the precision of parameter estimation. However, in many situations there is no obvious functional form and, in such cases, this strategy will not be optimal. Here we propose to use nonparametric function estimation, typically implemented with B-splines, to estimate the underlying functional form of phenotypic trajectories, and then construct a nonparametric test to find evidence of existing QTL. Using the representation of a nonparametric regression as a mixed model, the final test statistic is a likelihood ratio test. We consider two types of genetic maps: dense maps and general maps, and the power of nonparametric functional mapping is investigated through simulation studies and demonstrated by examples.     

Microarray analysis of vegetative phase change in maize

Vegetative phase change is the developmental transition from the juvenile phase to the adult phase in which a plant becomes competent for sexual reproduction. The gain of ability to flower is often accompanied by changes in patterns of differentiation in newly forming vegetative organs. In maize, juvenile leaves differ from adult leaves in morphology, anatomy and cell wall composition. Whereas the normal sequence of juvenile followed by adult is repeated with every sexual generation, this sequence can be altered in maize by the isolation and culture of the shoot apex from an adult phase plant: an 'adult' meristem so treated reverts to forming juvenile vegetative organs. To begin to unravel the as-yet poorly understood molecular mechanisms underlying phase change in maize, we compared gene expression in two juvenile sample types, leaf 4 and culture-derived leaves 3 or 4, with an adult sample type (leaf 9) using cDNA microarrays. All samples were leaf primordia at plastochron 6. A gene was scored as 'phase induced' if it was up- or downregulated in both juvenile sample types, compared with the adult sample type, with at least a twofold change in gene expression at a P-value of < or =0.005. Some 221 expressed sequence tags (ESTs) were upregulated in juveniles, and 28 ESTs were upregulated in adults. The largest class of juvenile-induced genes was comprised of those involved in photosynthesis, suggesting that maize plants are primed for energy production early in vegetative growth by the developmental induction of photosynthetic genes.     

ER/PR阳性和阴性乳腺癌的定量蛋白质组学和生物信息学比较研究

目的:建立雌/孕激素受体(ER/PR)阴性和阳性乳腺癌的蛋白质表达谱,寻找ER/PR阴性和阳性乳腺癌中差异表达蛋白,为乳腺癌患者提供新的预后预测指标和治疗新靶点。方法:应用蛋白质组学i TRAQ技术建立ER/PR阳性和阴性乳腺癌的蛋白质差异表达谱,鉴定两组乳腺癌的差异表达蛋白,对部分差异表达蛋白进行生物信息学分析,包括蛋白功能注释和分类GO分析和KEGG通路分析。结果:应用i TRAQ蛋白质组学技术对乳腺癌组织进行了蛋白组学分析,鉴定出ER/PR阳性和阴性组间有差异表达的蛋白4999种,以ER/PR阳性:ER/PR阴性≥3为上调标准,确定ER/PR阳性组上调蛋白101种。以ER/PR阳性:ER/PR阴性≤0.5为下调标准,ER/PR阳性组下调蛋白122种。GO分析结果显示ER/PR受体阴性和阳性乳腺癌的差异表达蛋白的分子功能、生物过程、细胞定位较为复杂,并且在上调蛋白和下调蛋白上存在分布差异。KEGG通路分析发现部分差异表达蛋白涉及201条信号通路。结论:ER/PR阳性和阴性乳腺癌间存在差异表达蛋白,这些蛋白涉及复杂的分子功能、生物过程和信号通路。     

Regulation of lipid metabolism by Dicer revealed through SILAC mice

Dicer is a ribonuclease whose major role is to generate mature microRNAs, although additional functions have been proposed. Deletion of Dicer leads to embryonic lethality in mice. To study the role of Dicer in adults, we generated mice in which administration of tamoxifen induces deletion of Dicer. Surprisingly, disruption of Dicer in adult mice induced lipid accumulation in the small intestine. To dissect the underlying mechanisms, we carried out miRNA, mRNA, and proteomic profiling of the small intestine. The proteomic analysis was done using mice metabolically labeled with heavy lysine (SILAC mice) for an in vivo readout. We identified 646 proteins, of which 80 were up-regulated >2-fold and 75 were down-regulated. Consistent with the accumulation of lipids, Dicer disruption caused a marked decrease of microsomal triglyceride transfer protein, long-chain fatty acyl-CoA ligase 5, fatty acid binding protein, and very-long-chain fatty acyl-CoA dehydrogenase, among others. We validated these results using multiple reaction monitoring (MRM) experiments by targeting proteotypic peptides. Our data reveal a previously unappreciated role of Dicer in lipid metabolism. These studies demonstrate that a systems biology approach by integrating mouse models, metabolic labeling, gene expression profiling, and quantitative proteomics can be a powerful tool for understanding complex biological systems.     

A Computational Algorithm for Functional Clustering of Proteome Dynamics During Development

Phenotypic traits, such as seed development, are a consequence of complex biochemical interactions among genes, proteins and metabolites, but the underlying mechanisms that operate in a coordinated and sequential manner remain elusive. Here, we address this issue by developing a computational algorithm to monitor proteome changes during the course of trait development. The algorithm is built within the mixture-model framework in which each mixture component is modeled by a specific group of proteins that display a similar temporal pattern of expression in trait development. A nonparametric approach based on Legendre orthogonal polynomials was used to fit dynamic changes of protein expression, increasing the power and flexibility of protein clustering. By analyzing a dataset of proteomic dynamics during early embryogenesis of the Chinese fir, the algorithm has successfully identified several distinct types of proteins that coordinate with each other to determine seed development in this forest tree commercially and environmentally important to China. The algorithm will find its immediate applications for the characterization of mechanistic underpinnings for any other biological processes in which protein abundance plays a key role.     

Functional mapping of growth and development

Understanding how an organism develops into a fully functioning adult from a mass of undifferentiated cells may reveal different strategies that allow the organism to survive under limiting conditions. Here, we review an analytical model for characterizing quantitative trait loci (QTLs) that underlie variation in growth trajectories and developmental timing. This model, called functional mapping, incorporates fundamental principles behind biological processes or networks that are bridged with mathematical functions into a statistical mapping framework. Functional mapping estimates parameters that determine the shape and function of a particular biological process, thus providing a flexible platform to test biologically meaningful hypotheses regarding the complex relationships between gene action and development.     

Differential network biology

Protein and genetic interaction maps can reveal the overall physical and functional landscape of a biological system. To date, these interaction maps have typically been generated under a single condition, even though biological systems undergo differential change that is dependent on environment, tissue type, disease state, development or speciation. Several recent interaction mapping studies have demonstrated the power of differential analysis for elucidating fundamental biological responses, revealing that the architecture of an interactome can be massively re‐wired during a cellular or adaptive response. Here, we review the technological developments and experimental designs that have enabled differential network mapping at very large scales and highlight biological insight that has been derived from this type of analysis. We argue that differential network mapping, which allows for the interrogation of previously unexplored interaction spaces, will become a standard mode of network analysis in the future, just as differential gene expression and protein phosphorylation studies are already pervasive in genomic and proteomic analysis.     

Wavelet-based parametric functional mapping of developmental trajectories with high-dimensional data

The biological and statistical advantages of functional mapping result from joint modeling of the mean-covariance structures for developmental trajectories of a complex trait measured at a series of time points. While an increased number of time points can better describe the dynamic pattern of trait development, significant difficulties in performing functional mapping arise from prohibitive computational times required as well as from modeling the structure of a high-dimensional covariance matrix. In this article, we develop a statistical model for functional mapping of quantitative trait loci (QTL) that govern the developmental process of a quantitative trait on the basis of wavelet dimension reduction. By breaking an original signal down into a spectrum by taking its averages (smooth coefficients) and differences (detail coefficients), we used the discrete Haar wavelet shrinkage technique to transform an inherently high-dimensional biological problem into its tractable low-dimensional representation within the framework of functional mapping constructed by a Gaussian mixture model. Unlike conventional nonparametric modeling of wavelet shrinkage, we incorporate mathematical aspects of developmental trajectories into the smooth coefficients used for QTL mapping, thus preserving the biological relevance of functional mapping in formulating a number of hypothesis tests at the interplay between gene actions/interactions and developmental patterns for complex phenotypes. This wavelet-based parametric functional mapping has been statistically examined and compared with full-dimensional functional mapping through simulation studies. It holds great promise as a powerful statistical tool to unravel the genetic machinery of developmental trajectories with large-scale high-dimensional data.     

Antibody microarrays: current status and key technological advances

Antibody-based microarrays are among the novel classes of rapidly evolving proteomic technologies that holds great promise in biomedicine. Miniaturized microarrays (< 1 cm2) can be printed with thousands of individual antibodies carrying the desired specificities, and with biological sample (e.g., an entire proteome) added, virtually any specifically bound analytes can be detected. While consuming only minute amounts (< microL scale) of reagents, ultra- sensitive assays (zeptomol range) can readily be performed in a highly multiplexed manner. The microarray patterns generated can then be transformed into proteomic maps, or detailed molecular fingerprints, revealing the composition of the proteome. Thus, protein expression profiling and global proteome analysis using this tool will offer new opportunities for drug target and biomarker discovery, disease diagnostics, and insights into disease biology. Adopting the antibody microarray technology platform, several biomedical applications, ranging from focused assays to proteome-scale analysis will be rapidly emerging in the coming years. This review will discuss the current status of the antibody microarray technology focusing on recent technological advances and key issues in the process of evolving the methodology into a high-performing proteomic research tool.     

Cypermethrin exposure leads to regulation of proteins expression involved in neoplastic transformation in mouse skin

Cypermethrin, a synthetic pyrethroid insecticide is shown to exert carcinogenic effects in rodents; however, its underlying mechanism remains elusive. Here, we showed the effect of cypermethrin on protein expression involved in neoplastic transformation in mouse skin. Comparative protein expression profiles between untreated control and cypermethrin-treated mouse skin were explored using 2-DE. A total of 27 spots that were statistically significant (p<0.05) and differentially expressed in response to cypermethrin exposure were identified by MALDI-TOF/TOF and LC-MS/MS. Among them, six up-regulated proteins (carbonic anhydrase 3 (Ca 3), Hsp-27, S100A6, galectin-7, S100A9, S100A11) and one down-regulated protein (superoxide dismutase [Cu-Zn] (Sod 1)) are associated with cancer-related key processes. These selected dysregulated proteins were further validated in 2-DE gels of mouse skin treated with known tumorigens (benzo-[a]-pyrene, 12-O-tetradecanoyl-phorbol-13-acetate and mezerein), respectively. Comparative studies showed that Ca 3, S100A6, S100A9, S100A11 and Sod 1 are specific for stages of development and progression of tumors whereas Hsp-27 and galectin-7 are specific for tumor promotion stage by cypermethrin in mouse skin. Furthermore, these chosen proteins confirmed by Western blotting and immunofluorescence staining were consistent with changes in 2-DE check. This proteomic investigation for the first time provides key proteins that will contribute in understanding the mechanism behind cypermethrin-induced neoplastic transformation.