Gram-Schmidt Orthogonalization of Multinormal Variates: Applications in Genetics

The computation of an N-variate normal density function requires the inversion of an N × N co-variance matrix. Furthermore, if each mean depends on u unobservable factors, a mixture of u^N N-variate normal densities must be computed, making likelihood calculations impractical even for moderate N. The Gram-Schmidt orthogonalization process is used to express a multinormal density as a product of univariate normal densities. When the pattern of the correlation matrix is taken into account the formulas may be considerably simplified. In some cases each of the orthogonal variates can be written as a linear combination of only a few of the original variates. Such results are crucial for applications of multinormal distributions and of mixtures of multinormal distributions. An intraclass correlation model and a genetic variance components model applicable to family data are discussed as examples.     

Overcoming Salinity Barriers to Crop Production Using Traditional Methods

Salinity is a major problem in arid and semi-arid regions, where irrigation is essential for crop production. Major sources of salinity in these regions are salt-rich irrigation water and improper irrigation management. The effects of salinity on crops include inhibition of growth and production, and ultimately, death. There are two main approaches to alleviating the adverse effects of salinity on agricultural crops: (i) development of salt-tolerant cultivars by screening, conventional breeding or genetic engineering, and (ii) the traditional approach dealing with treatments and management of the soil, plants, irrigation water, and plant environment. The success of the first approach is limited under commercial growing conditions, because salt-tolerance traits in plants are complex. The present paper reviews, analyzes, and discusses the following traditional approaches: (i) improving the plant environment, (ii) exploiting interactions between plant roots and bacteria and fungi, and (iii) treating the plant directly. With respect to improving the plant environment, we review the possibilities of decreasing salt content and concentration and improving the nutrient composition and concentration in the root zone, and controlling the plant's aerial environment. The interactions between salt-tolerant bacteria or mycorrhizal fungi and root systems, and their effects on salt-tolerance, are demonstrated and discussed. Discussed treatments aimed at alleviating salinity hazard by treating the plant directly include priming of seeds and young seedlings, using proper seed size, grafting onto tolerant rootstocks, applying non-enzymatic antioxidants, plant growth regulators or compatible solutes, and foliar application of nutrients. It can be concluded from the present review that the traditional approaches provide promising means for alleviating the adverse effects of salinity on agricultural crops.     

Overcoming Key Technological Challenges in Using Mass Spectrometry for Mapping Cell Surfaces in Tissues

Plasma membranes form a critical biological interface between the inside of every cell and its external environment. Their roles in multiple key cellular functions make them important drug targets. However the protein composition of plasma membranes in general is poorly defined as the inherent properties of lipid embedded proteins, such as their hydrophobicity, low abundance, poor solubility and resistance to digestion and extraction makes them difficult to isolate, solubilize, and identify on a large scale by traditional mass spectrometry methods. Here we describe some of the significant advances that have occurred over the past ten years to address these challenges including: i) the development of new and improved membrane isolation techniques via either subfractionation or direct labeling and isolation of plasma membranes from cells and tissues; ii) modification of mass spectrometry methods to adapt to the hydrophobic nature of membrane proteins and peptides; iii) improvements to digestion protocols to compensate for the shortage of trypsin cleavage sites in lipid-embedded proteins, particularly multi-spanning proteins, and iv) the development of numerous bioinformatics tools which allow not only the identification and quantification of proteins, but also the prediction of membrane protein topology, membrane post-translational modifications and subcellular localization. This review emphasis the importance and difficulty of defining cells in proper patho- and physiological context to maintain the in vivo reality. We focus on how key technological challenges associated with the isolation and identification of cell surface proteins in tissues using mass spectrometry are being addressed in order to identify and quantify a comprehensive plasma membrane for drug and target discovery efforts.Plasma membranes (PM)¹ and their associated proteins are part of a key biological interface between the outside and the inside of the cell. They are implicated in important cellular functions, such as small molecule transport, cell communication, and signaling. Such proteins are critical in sensing changes in the external environment and in transmitting signals into and out of the cell. Impaired cellular signaling, often involving PM proteins, is apparent in many cancers (1, 2). Membrane proteins represent one-third of the proteins encoded by the human genome (3) but represent more than two-thirds of the known protein targets for existing drugs (4). Thus, defining the proteome of PMs is critical for understanding cellular functions and fundamental biological processes and for finding new targets for drug discovery efforts.In an ideal world, the PM proteome of all cell types would be analyzed comprehensively to (i) better understand why and where different membrane proteins are expressed, (ii) reveal new functions for the PM in various cell types, (iii) identify cell-specific surface-accessible markers as targetable proteins for local drug delivery, and (iv) identify diagnostic or prognostic indicators in healthy and diseased cell or tissue states. This can be attempted relatively easily in cell culture using a homogenous cell population as compared with the heterogeneity of cells within any tissue or organ from which they are derived. Unfortunately, once cells are isolated from different organs and even more so when cultured and grown in vitro, the cells can change dramatically in appearance, structure, and responsiveness, and protein expression and distribution within the cell can change (5, 6). Biomarkers readily disappear, and the cultured cells do not reflect the in vivo reality. Most importantly, they lose expression of tissue-specific proteins and dedifferentiate into a more common phenotype. Mass spectrometry (MS) analysis revealed that as much as 40% of the proteins expressed by endothelial cells in vivo are not found in vitro (6). Thus, several groups have tried to address this problem by capturing the PMs in as close to an in vivo situation as possible as discussed below.PM proteins in general may not serve as suitable targets for drug treatment regimens mainly because of their inaccessibility to intravenously injected agents. Although most small molecule drugs can readily penetrate and accumulate in almost any tissue, often a high dose must be administered for the therapeutic dosage to accumulate in the diseased tissue of interest. However, major problems can arise when such drugs are toxic to both normal and healthy tissue, e.g. chemotherapeutic agents, which can accumulate in healthy tissue, resulting in the unwanted and often severe side effects associated with these drugs. Thus, even if a protein expressed on the surface of a cell is indicative of a disease state, it would be relatively impossible to target this cell specifically while avoiding the accumulation in healthy tissue using current drug delivery approaches, especially if the cell of interest is embedded within a tissue or organ. This is simply because multiple barriers, e.g. endothelium, epithelium, etc., must be crossed to access the cell regardless of the route of administration. As a result, great effort is now being focused on developing more targeted approaches where the toxic agents are specifically delivered to the organ or tissue of interest (7–9). With such targeting approaches, dosages can be significantly reduced, thus increasing the therapeutic efficacy while minimizing the side effects (10, 11). Consequently, proteins that are expressed on the surface of endothelial cells (ECs) that line the luminal surface of all vasculature are attractive targets for drugs and imaging agents as these proteins are in direct contact with the circulating blood and are thus inherently more accessible to intravenously administered agents than PM proteins of cells residing deep inside tissues and organs. Proteins expressed on the outer luminal EC surface can readily bind antibodies and other agents that are circulating in the blood. Thus, identifying and characterizing the proteins that line the vasculature of each organ and tissue is highly desirable for drug delivery and diagnostic imaging.PM proteins, regardless of the cell or tissue of origin, have generally been under-represented in proteomics analysis mainly because of their low abundance. In addition, the inherent insolubility of membrane proteins due to their hydrophobic nature has rendered them difficult to isolate and identify compared with their counterparts in the soluble cytosol and nuclear fractions. In many high throughput protein identification approaches, soluble proteomes are readily characterized, and it is fairly common for thousands of proteins to be identified in such samples. However, when more challenging proteomes, such as those of the PM, are of interest, the numbers of proteins identified are significantly lower. There are many technical reasons for such a dramatic decrease in protein identification when membrane proteomes are of interest. This review focuses on how we and other laboratories are overcoming key technological challenges associated with using traditional MS-based approaches, which were initially developed for the identification of more soluble proteins, for the mapping of membrane proteomes and in particular the in vivo cell surface proteome of endothelial cells.     

Catastrophic desert formation in Daisyworld

Feedback between life and its environment is ubiquitous but the strength of coupling and its global implications remain hotly debated. Abrupt changes in the abundance of life for small changes in forcing provide one indicator of regulation, for example, when vegetation-climate feedback collapses in the formation of a desert. Here we use a two-dimensional "Daisyworld" model with curvature to show that catastrophic collapse of life under gradual forcing provides a testable indicator of environmental feedback. When solar luminosity increases to a critical value, a desert forms across a wide band of the planet. The scale of collapse depends on the strength of feedback. The efficiency of temperature regulation is limited by mutation rate in an analogous manner to the limitation of adaptive fitness in evolutionary theories. The final state of the system emerging from single-site rules can be described by two global quantities: optimization of temperature regulation and maximization of diversity, which are mathematically analogous to energy and entropy in thermodynamics.     

Predicting Pharmacodynamic Drug-Drug Interactions through Signaling Propagation Interference on Protein-Protein Interaction Networks

As pharmacodynamic drug-drug interactions (PD DDIs) could lead to severe adverse effects in patients, it is important to identify potential PD DDIs in drug development. The signaling starting from drug targets is propagated through protein-protein interaction (PPI) networks. PD DDIs could occur by close interference on the same targets or within the same pathways as well as distant interference through cross-talking pathways. However, most of the previous approaches have considered only close interference by measuring distances between drug targets or comparing target neighbors. We have applied a random walk with restart algorithm to simulate signaling propagation from drug targets in order to capture the possibility of their distant interference. Cross validation with DrugBank and Kyoto Encyclopedia of Genes and Genomes DRUG shows that the proposed method outperforms the previous methods significantly. We also provide a web service with which PD DDIs for drug pairs can be analyzed at http://biosoft.kaist.ac.kr/targetrw.     

RNAi沉默淀粉分支酶基因SEEI对玉米直链淀粉合成的影响

淀粉分支酶(SBE)是淀粉合成的限速酶.为了研究SBEI沉默对直链淀粉合成的影响,克隆了玉米(Zea mays)淀粉分支酶SBEI基因片段,构建了S8EI的RNAi表达载体pBAC418,用基因枪将其导入玉米自交系幼胚愈伤组织,经木糖筛选获得了7株转化再生植株.利用FAD2 intron和xylA基因探针对T<,0>代再生玉米植株进行DNA dot blot和PCR-Southern检测,证实5株为阳性植株,其中4株正常结实.SBEI基因沉默对阳性再生玉米株系籽粒的含油量没有显著影响;蛋白质含量显著高于受体对照;总淀粉含量与对照相比无显著差异,转基因株系直链淀粉含量平均提高了9.8%.     

Inference of Boolean Networks Using Sensitivity Regularization

The inference of genetic regulatory networks from global measurements of gene expressions is an important problem in computational biology. Recent studies suggest that such dynamical molecular systems are poised at a critical phase transition between an ordered and a disordered phase, affording the ability to balance stability and adaptability while coordinating complex macroscopic behavior. We investigate whether incorporating this dynamical system-wide property as an assumption in the inference process is beneficial in terms of reducing the inference error of the designed network. Using Boolean networks, for which there are well-defined notions of ordered, critical, and chaotic dynamical regimes as well as well-studied inference procedures, we analyze the expected inference error relative to deviations in the networks'' dynamical regimes from the assumption of criticality. We demonstrate that taking criticality into account via a penalty term in the inference procedure improves the accuracy of prediction both in terms of state transitions and network wiring, particularly for small sample sizes.     

Fast Fragmentation of Networks Using Module-Based Attacks

In the multidisciplinary field of Network Science, optimization of procedures for efficiently breaking complex networks is attracting much attention from a practical point of view. In this contribution, we present a module-based method to efficiently fragment complex networks. The procedure firstly identifies topological communities through which the network can be represented using a well established heuristic algorithm of community finding. Then only the nodes that participate of inter-community links are removed in descending order of their betweenness centrality. We illustrate the method by applying it to a variety of examples in the social, infrastructure, and biological fields. It is shown that the module-based approach always outperforms targeted attacks to vertices based on node degree or betweenness centrality rankings, with gains in efficiency strongly related to the modularity of the network. Remarkably, in the US power grid case, by deleting 3% of the nodes, the proposed method breaks the original network in fragments which are twenty times smaller in size than the fragments left by betweenness-based attack.     

Effective Treatment of Respiratory Alphaherpesvirus Infection Using RNA Interference

Background

Equine herpesvirus type 1 (EHV-1), a member of the Alphaherpesvirinae, is spread via nasal secretions and causes respiratory disease, neurological disorders and abortions. The virus is a significant equine pathogen, but current EHV-1 vaccines are only partially protective and effective metaphylactic and therapeutic agents are not available. Small interfering RNAs (siRNA''s), delivered intranasally, could prove a valuable alternative for infection control. siRNA''s against two essential EHV-1 genes, encoding the viral helicase (Ori) and glycoprotein B, were evaluated for their potential to decrease EHV-1 infection in a mouse model.

Methodology/Principal Fndings

siRNA therapy in vitro significantly reduced virus production and plaque size. Viral titers were reduced 80-fold with 37.5 pmol of a single siRNA or with as little as 6.25 pmol of each siRNA when used in combination. siRNA therapy in vivo significantly reduced viral replication and clinical signs. Intranasal treatment did not require a transport vehicle and proved effective when given up to 12 h before or after infection.

Conclusions/Significance

siRNA treatment has potential for both prevention and early treatment of EHV-1 infections.     

Multiple Quantitative Trait Analysis Using Bayesian Networks

Models for genome-wide prediction and association studies usually target a single phenotypic trait. However, in animal and plant genetics it is common to record information on multiple phenotypes for each individual that will be genotyped. Modeling traits individually disregards the fact that they are most likely associated due to pleiotropy and shared biological basis, thus providing only a partial, confounded view of genetic effects and phenotypic interactions. In this article we use data from a Multiparent Advanced Generation Inter-Cross (MAGIC) winter wheat population to explore Bayesian networks as a convenient and interpretable framework for the simultaneous modeling of multiple quantitative traits. We show that they are equivalent to multivariate genetic best linear unbiased prediction (GBLUP) and that they are competitive with single-trait elastic net and single-trait GBLUP in predictive performance. Finally, we discuss their relationship with other additive-effects models and their advantages in inference and interpretation. MAGIC populations provide an ideal setting for this kind of investigation because the very low population structure and large sample size result in predictive models with good power and limited confounding due to relatedness.     

A Transient RNA Interference Assay System Using Arabidopsis Protoplasts

Double-stranded RNA (dsRNA) induces sequence-specific gene silencing in eukaryotes through a process known as RNA interference (RNAi). RNAi is now used as a powerful tool for functional genomics in many eukaryotes, including plants. We herein report a dsRNA-mediated transient RNAi assay system using protoplasts from Arabidopsis mesophyll cells and suspension-cultured cells (cell line T87). Introduction of dsRNA into protoplasts led to marked silencing of target transgenes. Our assay system would provide a convenient and efficient way to induce RNAi in protoplasts of the model plant Arabidopsis thaliana.     

Understanding Social Contagion in Adoption Processes Using Dynamic Social Networks

There are many studies in the marketing and diffusion literature of the conditions in which social contagion affects adoption processes. Yet most of these studies assume that social interactions do not change over time, even though actors in social networks exhibit different likelihoods of being influenced across the diffusion period. Rooted in physics and epidemiology theories, this study proposes a Susceptible Infectious Susceptible (SIS) model to assess the role of social contagion in adoption processes, which takes changes in social dynamics over time into account. To study the adoption over a span of ten years, the authors used detailed data sets from a community of consumers and determined the importance of social contagion, as well as how the interplay of social and non-social influences from outside the community drives adoption processes. Although social contagion matters for diffusion, it is less relevant in shaping adoption when the study also includes social dynamics among members of the community. This finding is relevant for managers and entrepreneurs who trust in word-of-mouth marketing campaigns whose effect may be overestimated if marketers fail to acknowledge variations in social interactions.     

Mapping Cellular Polarity Networks Using Mass Spectrometry-based Strategies

Cell polarity is a vital biological process involved in the building, maintenance and normal functioning of tissues in invertebrates and vertebrates. Unsurprisingly, molecular defects affecting polarity organization and functions have a strong impact on tissue homeostasis, embryonic development and adult life, and may directly or indirectly lead to diseases. Genetic studies have demonstrated the causative effect of several polarity genes in diseases; however, much remains to be clarified before a comprehensive view of the molecular organization and regulation of the protein networks associated with polarity proteins is obtained. This challenge can be approached head-on using proteomics to identify protein complexes involved in cell polarity and their modifications in a spatio-temporal manner. We review the fundamental basics of mass spectrometry techniques and provide an in-depth analysis of how mass spectrometry has been instrumental in understanding the complex and dynamic nature of some cell polarity networks at the tissue (apico-basal and planar cell polarities) and cellular (cell migration, ciliogenesis) levels, with the fine dissection of the interconnections between prototypic cell polarity proteins and signal transduction cascades in normal and pathological situations. This review primarily focuses on epithelial structures which are the fundamental building blocks for most metazoan tissues, used as the archetypal model to study cellular polarity. This field offers broad perspectives thanks to the ever-increasing sensitivity of mass spectrometry and its use in combination with recently developed molecular strategies able to probe in situ proteomic networks.     

Dimensionality of Social Networks Using Motifs and Eigenvalues

We consider the dimensionality of social networks, and develop experiments aimed at predicting that dimension. We find that a social network model with nodes and links sampled from an m-dimensional metric space with power-law distributed influence regions best fits samples from real-world networks when m scales logarithmically with the number of nodes of the network. This supports a logarithmic dimension hypothesis, and we provide evidence with two different social networks, Facebook and LinkedIn. Further, we employ two different methods for confirming the hypothesis: the first uses the distribution of motif counts, and the second exploits the eigenvalue distribution.