Agrobacterium-Mediated Transient Gene Expression and Silencing: A Rapid Tool for Functional Gene Assay in Potato

Potato is the third most important food crop worldwide. However, genetic and genomic research of potato has lagged behind other major crops due to the autopolyploidy and highly heterozygous nature associated with the potato genome. Reliable and technically undemanding techniques are not available for functional gene assays in potato. Here we report the development of a transient gene expression and silencing system in potato. Gene expression or RNAi-based gene silencing constructs were delivered into potato leaf cells using Agrobacterium-mediated infiltration. Agroinfiltration of various gene constructs consistently resulted in potato cell transformation and spread of the transgenic cells around infiltration zones. The efficiency of agroinfiltration was affected by potato genotypes, concentration of Agrobacterium, and plant growth conditions. We demonstrated that the agroinfiltration-based transient gene expression can be used to detect potato proteins in sub-cellular compartments in living cells. We established a double agroinfiltration procedure that allows to test whether a specific gene is associated with potato late blight resistance pathway mediated by the resistance gene RB. This procedure provides a powerful approach for high throughput functional assay for a large number of candidate genes in potato late blight resistance.     

沉默Bmi-1基因表达稳定细胞株的建立及其生物学特性的研究

B细胞特异性莫洛尼鼠白血病病毒插入位点1(B-cell-specific moloney murine leukemia virus insertionsite 1,Bmi-1)基因是多梳基因家族成员,参与细胞增殖调控.研究发现Bmi-1基因可能参与肿瘤的形成,可能成为肿瘤潜在的治疗靶点.用RNA干扰(RNA interference,RNAi)沉默Bmi-1基因表达观察其对乳腺癌细胞株MCF-7侵袭和转移等生物学特性的影响,以探讨Bmi-1在乳腺癌发生发展中的作用.PT67细胞包装质粒后产生的逆转录病毒感染MCF-7细胞,嘌呤霉素筛选建立稳定细胞株,稳定抑制Bmi-1的细胞株命名为MCF-7/Bmi-1si.通过RT-PCR和Western blot分别从mRNA和蛋白水平检测Bmi-1的表达量;平板克隆形成实验检测细胞克隆形成能力;Transwell侵袭小室模型检测细胞体外侵袭和转移能力.MCF-7/Bmi-1si组与MCF-7和MCF-7/GFPsi组相比,Bmi-1 mRNA和蛋白表达量明显减少,克隆形成数及形成率也明显减少(P<0.05).侵袭和转移实验表明:与MCF-7和MCF-7/GFPsi组相比,MCF-7/Bmi-1si组细胞在Transwell侵袭小室中24 h穿膜细胞数明显减少(P<0.05).结果表明沉默Bmi-1基因表达稳定细胞株构建成功,Bmi-1基因表达的沉默能显著降低MCF-7细胞的体外增殖及侵袭转移能力.     

A Major Gene for Resting Metabolic Rate Unassociated with Body Composition: Results from the Québec Family Study

A major gene hypothesis for resting metabolic rate (RMR) was investigated using segregation analysis (POINTER) of data on families participating in Phase 2 of the Québec Family Study. Complete analyses were conducted on RMR adjusted for age, and also on RMR adjusted for age and other covariates, primarily fat mass (FM) and fat-free mass (FFM). Prior to adjustment for covariates, support for a major gene hypothesis was equivocal — i.e., there was evidence for either a major gene or a multifactorial component (i.e., polygenic and/or familial environment). The multifactorial model was preferred over the major gene model, although the latter did segregate according to Mendelian expectations. However, after the effects of FM and FFM were accounted for, a major gene effect was unambiguous and compelling. The putative locus accounted for 57% of the variance, affected 7% of the sample, and led to high values of RMR. The lack of a significant multifactorial effect suggested that the familial etiology of RMR adjusted for FM and FFM was likely to be entirely a function of the major locus. Comparing the RMR results from pre- and post-adjustment for FM and FFM suggests a plausible hypothesis. We know from earlier studies in this sample that there is a putative major gene for FM and a major non-Mendelian effect for FFM. The current study leads us to speculate that: (1) the gene(s) affecting body size and body composition also may have an effect on RMR, and further (2) removal of the effect of the major gene(s) for body size and composition allowed for detection of an additional major gene affecting only the RMR. Thus, RMR appears to be an oligogenic trait.     

Estimation of the Stationary Gene Migration Rate: A Cartographic Approach

A method of geographic mapping of the stationary (limiting) gene migration rate has been developed. The method is based on approximation of the empirical distribution of gene frequencies by a theoretical steady-state distribution. The maximum likelihood method and the ² minimization method are used to obtain consistent estimations of the gene migration rate as a parameter of the steady-state distribution. The new method makes it possible to determine the geographical distribution of the ratio between the properties of the population migration structure described by the stepping-stone and island models and to construct a geographical map of ² values. This map approximately reflects the distribution of natural selection pressure on the gene pool if genetic processes are quasisteady.     

RNA Interference (RNAi) Induced Gene Silencing: A Promising Approach of Hi-Tech Plant Breeding

RNA interference (RNAi) is a promising gene regulatory approach in functional genomics that has significant impact on crop improvement which permits down-regulation in gene expression with greater precise manner without affecting the expression of other genes. RNAi mechanism is expedited by small molecules of interfering RNA to suppress a gene of interest effectively. RNAi has also been exploited in plants for resistance against pathogens, insect/pest, nematodes, and virus that cause significant economic losses. Keeping beside the significance in the genome integrity maintenance as well as growth and development, RNAi induced gene syntheses are vital in plant stress management. Modifying the genes by the interference of small RNAs is one of the ways through which plants react to the environmental stresses. Hence, investigating the role of small RNAs in regulating gene expression assists the researchers to explore the potentiality of small RNAs in abiotic and biotic stress management. This novel approach opens new avenues for crop improvement by developing disease resistant, abiotic or biotic stress tolerant, and high yielding elite varieties.     

酿酒酵母中双链RNA介导基因沉默技术研究GRE3基因表达抑制

RNA沉默技术作为探索基因功能的实验手段应用于多种生物.以编码酿酒酵母NADPH依赖型醛糖还原酶的GRE3基因为对象,检测酿酒酵母双链RNA介导的基因沉默效应.以pESC-LEU为骨架,构建重组质粒psiLENT-GRE3并用于转化酿酒酵母YPH499.用RT-PCR检测到诱导1 kb RNA双螺旋和136 bp loop结构引起的GRE3基因表达下调.结果表明,双链RNA介导的基因沉默技术,能够用作降低酿酒酵母某一特定基因表达水平的工具.并有助于理解芽殖酵母的RNA干扰现象.     

5'-Triphosphate-Short Interfering RNA: Potent Inhibition of Influenza A Virus Infection by Gene Silencing and RIG-I Activation

Limited protection of current vaccines and antiviral drugs against influenza A virus infection underscores the urgent need for development of novel anti-influenza virus interventions. While short interfering RNA (siRNA) has been shown to be able to inhibit influenza virus infection in a gene-specific manner, activation of the retinoic acid-inducible gene I protein (RIG-I) pathway has an antiviral effect in a non-gene-specific mode. In this study, we designed and tested the anti-influenza virus effect of a short double-stranded RNA, designated 3p-mNP1496-siRNA, that possesses dual functions: an siRNA-targeting influenza NP gene and an agonist for RIG-I activation. This double-stranded siRNA possesses a triphosphate group at the 5' end of the sense strand and is blunt ended. Our study showed that 3p-mNP1496-siRNA could potently inhibit influenza A virus infection both in cell culture and in mice. The strong inhibition effect was attributed to its siRNA function as well as its ability to activate the RIG-I pathway. To the best of our knowledge, this is the first report that the combination of siRNA and RIG-I pathway activation can synergistically inhibit influenza A virus infection. The development of such dual functional RNA molecules will greatly contribute to the arsenal of tools to combat not only influenza viruses but also other important viral pathogens.     

The Prognostic Value of Epigenetic Silencing of p16 Gene in NSCLC Patients: A Systematic Review and Meta-Analysis

Background

The prognostic significance of p16 promoter hypermethylation in patients with non-small cell lung cancer (NSCLC) is still controversial. This analysis presents pooled estimates of the association to better elucidate whether p16 methylation has a prognostic role in NSCLC.

Methods

Relevant studies were identified by searching PubMed, Embase and Web of Science databases until June 2012. The association of p16 methylation with both overall survival (OS) and disease-free survival (DFS) was preformed. Studies were pooled and summary hazard ratios (HR) were calculated. Subgroup analyses, sensitivity analysis and publication bias were also conducted.

Results

A total of 18 studies containing 2432 patients met the inclusion criteria and had sufficient survival data for quantitative aggregation. The results showed that p16 methylation was an indicator of poor prognosis in NSCLC. The HR was 1.36 (95% CI: 1.08–1.73, I² = 56.7%) and 1.68 (95% CI: 1.12–2.52, I² = 38.7%) for OS and DFS, respectively. Subgroup analyses were carried out. The HRs of fresh and paraffin tissue were 1.50 (95% CI: 1.11–2.01) and 1.10 (95% CI: 0.77–1.57). The pooled HR was 1.40 (95% CI: 1.02–1.92) for methylation-specific PCR (MSP) and 1.26 (95% CI: 0.87–1.82) for quantitative MSP (Q-MSP). The combined HR of the 16 studies reporting NSCLC as a whole indicated that patients with p16 hypermethylation had poor prognosis. No significant association was found when adenocarcinoma subtype pooled. When seven studies on DFS were aggregated, the HR was 1.68 (95% CI: 1.12–2.52) without significant heterogeneity. Moreover, no obvious publication bias was detected on both OS and DFS.

Conclusion

The meta-analysis findings support the hypothesis that p16 methylation is associated with OS and DFS in NSCLC patients. Large well-designed prospective studies are now needed to confirm the clinical utility of p16 methylation as an independent prognostic marker.     

Decision Analysis Utilizing Data from Multiple Life-Cycle Impact Assessment Methods Part I: A Theoretical Basis

Numerous methodologies for the life-cycle impact assessment (LCIA) step of life-cycle assessment (LCA) are currently in popular use. These methods, which are based on a single method or level of analysis, are limited to the environmental fates, impact categories, damage functions, and stressors included in the method or model. Because of this, it has been suggested within the LCA community that LCIA data from multiple methods and/or levels of analysis, that is, end-point and midpoint indicators, be used in LCA-based decision analysis to facilitate better or, at least more informed, decision making. In this (two-part) series of articles, we develop and present a series of LCA-based decision analysis models, based on multiattribute value theory (MAVT), which utilize data from multiple LCIA methods and/or levels of analysis. The key to accomplishing this is the recognition of what LCIA damage indicators represent with respect to decision analysis, namely, decision attributes and, in most cases, proxy attributes. The use of proxy attributes in a decision model, however, poses certain challenges, such as the assessment of decision-maker preferences for actual consequences that are only known imprecisely because of inherent limits of both LCA and scientific knowledge. In this article (part I), we provide a brief overview of MAVT and examine some of the decision-theoretic issues and implications of current LCIA methods. We illustrate the application of MAVT to develop a decision model utilizing damage indicators from a single LCIA methodology; and, we identify the decision-theoretic issues that arise when attempting to combine LCIA indicators from multiple methods and/or levels of analysis in a single decision model. Finally, we introduce the use in our methodology of constructed attributes to combine related end-point damage indicators into single decision attributes and the concept and evaluation of proxy attributes.     

Isolation of the Ascobolus Immersus Spore Color Gene B2 and Study in Single Cells of Gene Silencing by Methylation Induced Premeiotically

The ascomycete Ascobolus immersus has been extensively used as a model system for the genetic study of meiotic recombination. More recently, an epigenetic process, known as methylation induced premeiotically (MIP), that acts on duplicated sequences has been discovered in A. immersus and has raised a new interest in this fungus. To try and extend these studies, we have now cloned the A. immersus spore color gene b2, a well characterized recombination hot-spot. Isolation of the whole gene was verified by physical mapping of four large b2 alterations, followed by transformation and mutant rescue of a null b2 allele. Transformation was also used to duplicate b2 and subject it to MIP. As a result, we were able for the first time to observe gene silencing as early as just after meiosis and in single cells. Furthermore, we have found evidence for a modulating effect of MIP on b2 expression, depending on the region of the gene that is duplicated and hence subjected to MIP.     

Quantitative Trait Loci Underlying Gene Product Variation: A Novel Perspective for Analyzing Regulation of Genome Expression

A methodology to dissect the genetic architecture of quantitative variation of numerous gene products simultaneously is proposed. For each individual of a segregating progeny, proteins extracted from a given organ are separated using two-dimensional electrophoresis, and their amounts are estimated with a computer-assisted system for spot quantification. Provided a complete genetic map is available, statistical procedures allow determination of the number, effects and chromosomal locations of factors controlling the amounts of individual proteins. This approach was applied to anonymous proteins of etiolated coleoptiles of maize, in an F(2) progeny between two distant lines. The genetic map included both restriction fragment length polymorphism and protein markers. Minimum estimates of one to five unlinked regulatory factors were found for 42 of the 72 proteins analyzed, with a large diversity of effects. Dominance and epistasis interactions were involved in the control of 38% and 14% of the 72 proteins, respectively. Such a methodology might help understanding the architecture of regulatory networks and the possible adaptive or phenotypic significance of the polymorphism of the genes involved.     

Three-Way Analysis of Spectrospatial Electromyography Data: Classification and Interpretation

Classifying multivariate electromyography (EMG) data is an important problem in prosthesis control as well as in neurophysiological studies and diagnosis. With modern high-density EMG sensor technology, it is possible to capture the rich spectrospatial structure of the myoelectric activity. We hypothesize that multi-way machine learning methods can efficiently utilize this structure in classification as well as reveal interesting patterns in it. To this end, we investigate the suitability of existing three-way classification methods to EMG-based hand movement classification in spectrospatial domain, as well as extend these methods by sparsification and regularization. We propose to use Fourier-domain independent component analysis as preprocessing to improve classification and interpretability of the results. In high-density EMG experiments on hand movements across 10 subjects, three-way classification yielded higher average performance compared with state-of-the art classification based on temporal features, suggesting that the three-way analysis approach can efficiently utilize detailed spectrospatial information of high-density EMG. Phase and amplitude patterns of features selected by the classifier in finger-movement data were found to be consistent with known physiology. Thus, our approach can accurately resolve hand and finger movements on the basis of detailed spectrospatial information, and at the same time allows for physiological interpretation of the results.     

Inference of Gene Regulatory Networks Using Time-Series Data: A Survey

The advent of high-throughput technology like microarrays has provided the platform for studying how different cellular components work together, thus created an enormous interest in mathematically modeling biological network, particularly gene regulatory network (GRN). Of particular interest is the modeling and inference on time-series data, which capture a more thorough picture of the system than non-temporal data do. We have given an extensive review of methodologies that have been used on time-series data. In realizing that validation is an impartible part of the inference paradigm, we have also presented a discussion on the principles and challenges in performance evaluation of different methods. This survey gives a panoramic view on these topics, with anticipation that the readers will be inspired to improve and/or expand GRN inference and validation tool repository.     

Detecting Periodic Genes from Irregularly Sampled Gene Expressions: A Comparison Study

Time series microarray measurements of gene expressions have been exploited to discover genes involved in cell cycles. Due to experimental constraints, most microarray observations are obtained through irregular sampling. In this paper three popular spectral analysis schemes, namely, Lomb-Scargle, Capon and missing-data amplitude and phase estimation (MAPES), are compared in terms of their ability and efficiency to recover periodically expressed genes. Based on in silico experiments for microarray measurements of Saccharomyces cerevisiae, Lomb-Scargle is found to be the most efficacious scheme. 149 genes are then identified to be periodically expressed in the Drosophila melanogaster data set.     

Bayesian Data Analysis: A Fresh Approach to Power Issues and Null Hypothesis Interpretation

One of the first things one learns in a basic psychology or statistics course is that you cannot prove the null hypothesis that there is no difference between two conditions such as a patient group and a normal control group. This remains true. However now, thanks to ongoing progress by a special group of devoted methodologists, even when the result of an inferential test is p?>?.05, it is now possible to rigorously and quantitatively conclude that (a) the null hypothesis is actually unlikely, and (b) that the alternative hypothesis of an actual difference between treatment and control is more probable than the null. Alternatively, it is also possible to conclude quantitatively that the null hypothesis is much more likely than the alternative. Without Bayesian statistics, we couldn’t say anything if a simple inferential analysis like a t-test yielded p?>?.05. The present, mostly non-quantitative article describes free resources and illustrative procedures for doing Bayesian analysis, with t-test and ANOVA examples.