On the utility of linkage disequilibrium as a statistic for identifying targets of positive selection in nonequilibrium populations

A critically important challenge in empirical population genetics is distinguishing neutral nonequilibrium processes from selective forces that produce similar patterns of variation. We here examine the extent to which linkage disequilibrium (i.e., nonrandom associations between markers) improves this discrimination. We show that patterns of linkage disequilibrium recently proposed to be unique to hitchhiking models are replicated under nonequilibrium neutral models. We also demonstrate that jointly considering spatial patterns of association among variants alongside the site-frequency spectrum is nonetheless of value. Through a comparison of models of equilibrium neutrality, nonequilibrium neutrality, equilibrium hitchhiking, nonequilibrium hitchhiking, and recurrent hitchhiking, we evaluate a linkage disequilibrium (LD) statistic (omega(max)) that appears to have power to identify regions recently shaped by positive selection. Most notably, for demographic parameters relevant to non-African populations of Drosophila melanogaster, we demonstrate that selected loci are distinguishable from neutral loci using this statistic.     

Functional genomics: identifying drug targets for parasitic diseases

The genomic sequences of parasitic diseases are rapidly becoming available and, recently, the full sequence of Plasmodium falciparum has been published. Much has been promised from this genomic revolution including the identification of new drug targets and novel chemotherapeutic treatments for the control of parasitic diseases. The challenge to use this information efficiently will require functional genomics tools such as bioinformatics, microarrays, proteomics and chemical genomics to identify potential drug targets, and to allow the development of optimized lead compounds. The information generated from these tools will provide a crucial link from genomic analysis to drug discovery.     

Use of gene networks for identifying and validating drug targets

We propose a new method for identifying and validating drug targets by using gene networks, which are estimated from cDNA microarray gene expression profile data. We created novel gene disruption and drug response microarray gene expression profile data libraries for the purpose of drug target elucidation. We use two types of microarray gene expression profile data for estimating gene networks and then identifying drug targets. The estimated gene networks play an essential role in understanding drug response data and this information is unattainable from clustering methods, which are the standard for gene expression analysis. In the construction of gene networks, we use the Bayesian network model. We use an actual example from analysis of the Saccharomyces cerevisiae gene expression profile data to express a concrete strategy for the application of gene network information to drug discovery.     

Knowledge-based selection of targets for structural genomics

The problem of rational target selection for protein structure determination in structural genomics projects on microbes is addressed. A flexible computational procedure is described that directly incorporates the whole body of annotation available in the PEDANT genome database into the sequence clustering and selection process in order to identify proteins that are likely to possess currently unknown structural domains. Filtering out gene products based on predicted structural features, such as known three-dimensional structures and transmembrane regions, allows one to reduce the complexity of neighbor relationships between sequences and all but eliminates the need for further partitioning of single-linkage clusters into disjoint protein groups corresponding to homologous families. The results of a large-scale computation experiment in which exemplary target selection for 32 prokaryotic genomes was conducted are presented.     

Methods for selection of aptamers to protein targets

Aptamers are synthetic single-stranded RNA or DNA molecules capable of specific binding to other target molecules. In this review, the main aptamer properties are considered and methods for selection of aptamers against various protein targets are described. Special attention is given to the methods for directed selection of aptamers, which allow one to obtain ligands with specified properties.     

Current strategies for identifying and validating targets for new treatment-shortening drugs for TB

There is an urgent need for new drugs to treat tuberculosis. During the last forty years the only drugs to have been developed are variations on existing ones, but new drug candidates must offer improvements over existing agents. In particular, we require new drugs having novel mechanisms of action that are active against drug-resistant strains and also kill persistent bacilli, thus shortening the length of chemotherapy. Recent advances in our understanding of the biology of Mycobacterium tuberculosis, in particularly the availability of the genome sequence coupled with development of new genetic tools, have greatly contributed to the discovery of potential drug targets for new antituberculars. However, although many potential new drug targets have been identified, greater effort is required in target validation to show properly that they are essential for bacterial growth and survival. In this review, the current drug development pipeline and the strategies employed to identify and validate novel tuberculosis drug targets are presented.     

Surrogate antigens as targets for proteome-wide binder selection

In the past decade, many initiatives were taken for the development of antibodies for proteome-wide studies, as well as characterisation and validation of clinically relevant disease biomarkers. Phage display offers many advantages compared to antibody generation by immunisation because it is an unlimited resource of affinity reagents without batch-to-batch variation and is also amendable for high throughput in contrast to conventional hybridoma technology. One of the major bottlenecks to proteome-wide binder selection is the limited supply of suitable target antigens representative of the human proteome. Here, we provide proof of principle of using easily accessible, cancer-associated protein epitope signature tags (PrESTs), routinely generated within the Human Protein Atlas project, as surrogate antigens for full-length proteins in phage selections for the retrieval of target-specific binders. These binders were subsequently tested in western blot, immunohistochemistry and protein microarray application to demonstrate their functionality.     

The selection of aptamers specific for membrane molecular targets

A growing number of RNA aptamers have been selected experimentally using the SELEX combinatorial approach, and these aptamers have several advantages over monoclonal protein antibodies or peptides with respect to their applications in medicine and nanobiotechnology. Relatively few successful selections have been reported for membrane molecular targets, in contrast to the situation with non-membrane molecular targets. This review compares the procedures and techniques used in selections against membrane proteins and membrane lipids. In the case of membrane proteins, the selections were performed against soluble protein fragments, detergent-membrane protein mixed micelles, whole cells, vesicles derived from cellular membranes, and enveloped viruses. Liposomes were used as an experimental system for the selection of aptamers against membrane lipids. RNA structure-dependent aptamer binding for rafts in lipid vesicles was reported. Based on the selected aptamers against DOPC and the amino acid tryptophan, a specific passive membrane transporter composed of RNA was constructed. The determination of the selectivity of aptamers appears to be a crucial step in a selection, but has rarely been fully investigated. The selections, which use whole cells or vesicles derived from membranes, can yield aptamers not only against proteins but also against membrane lipids.     

Delineation of signals required for thymocyte positive selection

Peptide/MHC complexes capable of inducing positive selection in mouse fetal thymic organ cultures fail to do so in suspension culture. Furthermore, this type of culture does not promote initial stages of differentiation, such as coreceptor down-modulation, unless peptides used for stimulation have (at least) weak agonist activity. We show in this study that signals provided in suspension culture by nonagonist peptide/MHC complexes on the surface of macrophages, even though apparently silent, are sufficient to promote complete phenotypic differentiation when CD4+CD8+ thymocytes are subsequently placed in a proper anatomical setting. Furthermore, the synergistic actions of suboptimal concentrations of phorbol esters and nonagonist peptide/MHC complexes can make the initial stages of positive selection visible, without converting maturation into negative selection. Thus, the correlation between efficiency of positive selection and the degree of coreceptor down-modulation on CD4+CD8+ thymocytes is not linear. Furthermore, these results suggest that the unique role of thymic stromal cells in positive selection is related not to presentation of self-peptide/MHC complexes, but most likely to another ligand.     

动物 microRNA 靶基因的筛选与鉴定研究进展

miRNA（microRNA）是一类在生物体内广泛存在的长度约22nt的小分子非编码RNA,其在转录后水平调控靶基因的表达,在生物体生长发育过程中起重要的调控作用。近年来,miRNA的功能研究越来越受到人们的重视,而miRNA功能研究的关键在于其调控靶基因的确定。miRNA主要作用于靶基因mRNA的3’UTR区的结合位点．但由于miRNA和靶基因的作用位点并不完全匹配,没有明显的规律可寻,导致应用传统方法鉴定靶基因十分困难。近年来,人们开发了各种特异的、灵敏度高的高通量miRNA靶基因筛选与鉴定方法,极大地促进了miRNA的功能研究。     

A novel principle for selection of transgenic plant cells: positive selection

Summary A novel principle for selection of transgenic plant cells is presented. In contrast to traditional selection where the transgenic cells acquire the ability to survive on selective media while the non-transgenic cells are killed (negative selection), this selection method actively favours regeneration and growth of the transgenic cells while the non-transgenic cells are starved but not killed. Therefore, this selection strategy is termed positive selection. TheE. coli -glucuronidase gene was used as selectable (as well as screenable) gene and a glucuronide derivative of the cytokinin benzyladenine as selective agent which is inactive as cytokinin but, upon hydrolysis by GUS, active cytokinin is released stimulating the transformed cells to regenerate. Selection ofAgrobacterium tumefaciens inoculated of tobacco leaf discs on benzyladenine N-3-glucuronide (7.5–15 mg/l) resulted in 1.7–2.9 fold higher transformation frequencies compared to kanamycin selection. A significant advantage of this selection procedure is the elimination of the need for herbicide and antibiotic resistance genes.     

Stochastic search variable selection for identifying multiple quantitative trait loci

In this article, we utilize stochastic search variable selection methodology to develop a Bayesian method for identifying multiple quantitative trait loci (QTL) for complex traits in experimental designs. The proposed procedure entails embedding multiple regression in a hierarchical normal mixture model, where latent indicators for all markers are used to identify the multiple markers. The markers with significant effects can be identified as those with higher posterior probability included in the model. A simple and easy-to-use Gibbs sampler is employed to generate samples from the joint posterior distribution of all unknowns including the latent indicators, genetic effects for all markers, and other model parameters. The proposed method was evaluated using simulated data and illustrated using a real data set. The results demonstrate that the proposed method works well under typical situations of most QTL studies in terms of number of markers and marker density.     

Haplotype allelic classes for detecting ongoing positive selection

Background  

Natural selection eliminates detrimental and favors advantageous phenotypes. This process leaves characteristic signatures in underlying genomic segments that can be recognized through deviations in allelic or haplotypic frequency spectra. To provide an identifiable signature of recent positive selection that can be detected by comparison with the background distribution, we introduced a new way of looking at genomic polymorphisms: haplotype allelic classes.     

A systematic bioinformatics approach for selection of epitope-based vaccine targets

Epitope-based vaccines provide a new strategy for prophylactic and therapeutic application of pathogen-specific immunity. A critical requirement of this strategy is the identification and selection of T-cell epitopes that act as vaccine targets. This study describes current methodologies for the selection process, with dengue virus as a model system. A combination of publicly available bioinformatics algorithms and computational tools are used to screen and select antigen sequences as potential T-cell epitopes of supertype human leukocyte antigen (HLA) alleles. The selected sequences are tested for biological function by their activation of T-cells of HLA transgenic mice and of pathogen infected subjects. This approach provides an experimental basis for the design of pathogen specific, T-cell epitope-based vaccines that are targeted to majority of the genetic variants of the pathogen, and are effective for a broad range of differences in human leukocyte antigens among the global human population.     

Genomic insights into positive selection

The traditional way of identifying targets of adaptive evolution has been to study a few loci that one hypothesizes a priori to have been under selection. This approach is complicated because of the confounding effects that population demographic history and selection have on patterns of DNA sequence variation. In principle, multilocus analyses can facilitate robust inferences of selection at individual loci. The deluge of large-scale catalogs of genetic variation has stimulated many genome-wide scans for positive selection in several species. Here, we review some of the salient observations of these studies, identify important challenges ahead, consider the limitations of genome-wide scans for selection and discuss the potential significance of a comprehensive understanding of genomic patterns of selection for disease-related research.     

Hitchhiking under positive Darwinian selection

Positive selection can be inferred from its effect on linked neutral variation. In the restrictive case when there is no recombination, all linked variation is removed. If recombination is present but rare, both deterministic and stochastic models of positive selection show that linked variation hitchhikes to either low or high frequencies. While the frequency distribution of variation can be influenced by a number of evolutionary processes, an excess of derived variants at high frequency is a unique pattern produced by hitchhiking (derived refers to the nonancestral state as determined from an outgroup). We adopt a statistic, H, to measure an excess of high compared to intermediate frequency variants. Only a few high-frequency variants are needed to detect hitchhiking since not many are expected under neutrality. This is of particular utility in regions of low recombination where there is not much variation and in regions of normal or high recombination, where the hitchhiking effect can be limited to a small (<1 kb) region. Application of the H test to published surveys of Drosophila variation reveals an excess of high frequency variants that are likely to have been influenced by positive selection.     

In vivo biotinylated proteins as targets for phage-display selection experiments

Screening phage-displayed combinatorial libraries represents an attractive method for identifying affinity reagents to target proteins. Two critical components of a successful selection experiment are having a pure target protein and its immobilization in a native conformation. To achieve both of these requirements in a single step, we have devised cytoplasmic expression vectors for expression of proteins that are tagged at the amino- or carboxy-terminus (pMCSG16 and 15) via the AviTag, which is biotinylated in vivo with concurrent expression of the BirA biotin ligase. To facilitate implementation in high-throughput applications, the engineered vectors, pMCSG15 and pMCSG16, also contain a ligase-independent cloning site (LIC), which permits up to 100% cloning efficiency. The expressed protein can be purified from bacterial cell lysates with immobilized metal affinity chromatography or streptavidin-coated magnetic beads, and the beads used directly to select phage from combinatorial libraries. From selections using the N-terminally biotinylated version of one target protein, a peptide ligand (Kd= 9 microM) was recovered that bound in a format-dependent manner. To demonstrate the utility of pMCSG16, a set of 192 open reading frames were cloned, and protein was expressed and immobilized for use in high-throughput selections of phage-display libraries.     

Protein kinase C-theta is required for efficient positive selection

Protein kinase C-theta (PKCtheta) is critical for TCR-initiated signaling in mature T cells, but initial reports found no requirement for PKCtheta in thymocyte development. Thymocytes and peripheral T cells utilize many of the same signaling components and, given the significant role of PKCtheta in peripheral T cells, it was surprising that it was not involved at all in TCR signaling in thymocytes. We decided to re-evaluate the role of PKCtheta in thymocyte development using the well-characterized class II-restricted n3.L2 TCR-transgenic TCR model. Analysis of n3.L2 PKCtheta(-/-) mice revealed a defect in thymocyte-positive selection, resulting in a 50% reduction in the generation of n3.L2 CD4 single-positive thymocytes and n3.L2 CD4 mature T cells. Competition between n3.L2 WT and n3.L2 PKCtheta(-/-) thymocytes in bone marrow chimeras revealed a more dramatic defect, with a >80% reduction in generation of n3.L2 CD4 single-positive thymocytes derived from PKCtheta(-/-) mice. Inefficient positive selection of n3.L2 PKCtheta(-/-) CD4 single-positive cells resulted from "weaker" signaling through the TCR and correlated with diminished ERK activation. The defect in positive selection was not complete in the PKCtheta(-/-) mice, most likely accounted for by compensation by other PKC isoforms not evident in peripheral cells. Similar decreased positive selection of both CD4 and CD8 single-positive thymocytes was also seen in nontransgenic PKCtheta(-/-) mice. These findings now place PKCtheta as a key signaling molecule in the positive selection of thymocytes as well as in the activation of mature T cells.