Implications of cytokine gene polymorphisms on the orchestration of the immune response: Lessons learned from oral diseases

Over the past 10 years, a plethora of information concerning the influence of gene polymorphisms on cytokine expression has been made available in the literature. Significant contribution to this field has come from studies of oral diseases, one of the widest spread health problems in the world, affecting hundreds of millions worldwide. Here we will discuss the importance of studies of gene polymorphism towards the identification of susceptible groups or prognostic indicators of oral disease. Additionally, we will highlight the differences in data obtained from genetically diverse populations and review the application of cytokine gene polymorphisms studies in oral diseases in autoimmune processes and parasitic infections.     

A look to future directions in gene therapy research for monogenic diseases

The concept of gene therapy has long appealed to biomedical researchers and clinicians because it promised to treat certain diseases at their origins. In the last several years, there have been several trials in which patients have benefited from gene therapy protocols. This progress, however, has revealed important problems, including the problem of insertional oncogenesis. In this review, which focuses on monogenic diseases, we discuss the problem of insertional oncogenesis and identify areas for future research, such as developing more quantitative assays for risk and efficacy, and ways of minimizing the genotoxic effects of gene therapy protocols, which will be important if gene therapy is to fulfill its conceptual promise.     

基因表达谱聚类/分类技术研究及展望

随着人类及多种模式生物全基因组测序基本完成,人类基因组计划的研究进入后基因组时代.后基因组时代研究的焦点已经从测序转向功能研究。聚类/分类技术作为分析基因表达谱和识别基因功能的重要工具之一,近年来获得很大的发展。对目前基因表达谱聚类/分类技术及它们的发展,进行了综述性的研究,分析了它们的优缺点,结合我们的研究,提出了解决问题的思路和方法,为基因表达谱的进一步研究提供了新的途径。     

New RNAi strategy for selective suppression of a mutant allele in polyglutamine disease

In gene therapy of dominantly inherited diseases with small interfering RNA (siRNA), mutant allele specific suppression may be necessary for diseases in which the defective gene normally has an important role. It is difficult, however, to design a mutant allele-specific siRNA for trinucleotide repeat diseases in which the difference of sequences is only repeat length. To overcome this problem, we use a new RNA interference (RNAi) strategy for selective suppression of mutant alleles. Both mutant and wild-type alleles are inhibited by the most effective siRNA, and wild-type protein is restored using the wild-type mRNA modified to be resistant to the siRNA. Here, we applied this method to spinocerebellar ataxia type 6 (SCA6). We discuss its feasibility and problems for future gene therapy.     

Variations on RNA folding and alignment: lessons from Benasque

Dynamic programming algorithms solve many standard problems of RNA bioinformatics in polynomial time. In this contribution we discuss a series of variations on these standard methods that implement refined biophysical models, such as a restriction of RNA folding to canonical structures, and an extension of structural alignments to an explicit scoring of stacking propensities. Furthermore, we demonstrate that a local structural alignment can be employed for ncRNA gene finding. In this context we discuss scanning variants for folding and alignment algorithms.     

Gene therapy for cystic fibrosis

Cystic fibrosis (CF) is associated with significant morbidity and mortality, despite significant advances in conventional treatment. The field of gene therapy has progressed rapidly since the cystic fibrosis transmembrane conductance regulator (CFTR) gene was cloned. In this review we discuss current knowledge on the underlying molecular defect in CF, and the progress in gene transfer studies from the early in vitro work through to clinical trials, including the development of endpoints to assess efficacy. We highlight the problems encountered, and likely future directions of the field.     

Irrational exuberance for resolved species trees

Phylogenomics has largely succeeded in its aim of accurately inferring species trees, even when there are high levels of discordance among individual gene trees. These resolved species trees can be used to ask many questions about trait evolution, including the direction of change and number of times traits have evolved. However, the mapping of traits onto trees generally uses only a single representation of the species tree, ignoring variation in the gene trees used to construct it. Recognizing that genes underlie traits, these results imply that many traits follow topologies that are discordant with the species topology. As a consequence, standard methods for character mapping will incorrectly infer the number of times a trait has evolved. This phenomenon, dubbed “hemiplasy,” poses many problems in analyses of character evolution. Here we outline these problems, explaining where and when they are likely to occur. We offer several ways in which the possible presence of hemiplasy can be diagnosed, and discuss multiple approaches to dealing with the problems presented by underlying gene tree discordance when carrying out character mapping. Finally, we discuss the implications of hemiplasy for general phylogenetic inference, including the possible drawbacks of the widespread push for “resolved” species trees.     

Robust method for detecting differential gene expression in twin studies

MOTIVATION: A steadily increasing number of experiments with microarrays stimulate the further development of the statistical methods of the analysis of gene expression data. One of the central problems in this area is detecting differential gene expression under two or more conditions. Unfortunately, up to now it has not been studied how the correlations between related individuals, such as twins influence the estimates of differential gene expression. RESULTS: In this paper, we discuss this problem and propose a new method that is robust with respect to correlations of gene expression data for twins.     

Methods of induced pluripotent stem cells for clinical application

Reprograming somatic cells using exogenetic gene expression represents a groundbreaking step in regenerative medicine. Induced pluripotent stem cells(i PSCs) are expected to yield novel therapies with the potential to solve many issues involving incurable diseases. In particular, applying i PSCs clinically holds the promise of addressing the problems of immune rejection and ethics that have hampered the clinical applications of embryonic stem cells. However, as i PSC research has progressed, new problems have emerged that need to be solved before the routine clinical application of i PSCs can become established. In this review, we discuss the current technologies and future problems of human i PSC generation methods for clinical use.     

A hybrid SOM-SVM approach for the zebrafish gene expression analysis

Microarray technology can be employed to quantitatively measure the expression of thousands of genes in a single experiment. It has become one of the main tools for global gene expression analysis in molecular biology research in recent years. The large amount of expression data generated by this technology makes the study of certain complex biological problems possible, and machine learning methods are expected to play a crucial role in the analysis process. In this paper, we present our results from integrating the self-organizing map （SOM） and the support vector machine （SVM） for the analysis of the various functions of zebrafish genes based on their expression. The most distinctive characteristic of our zebrafish gene expression is that the number of samples of different classes is imbalanced. We discuss how SOM can be used as a data-filtering tool to improve the classification performance of the SVM on this data set.     

高等植物叶绿体RNA编辑研究进展

RNA编辑普遍存在于陆生植物中,在高等植物叶绿体中以C→U的替换为主,可能是叶绿体产生功能蛋白的重要方式。近年来,使用体外分析、叶绿体转化和紫外交联等技术,使叶绿体RNA编辑机制的研究取得较大进展。本文对这些新的进展进行了概述,并对高等植物叶绿体RNA编辑研究中有待解决的问题进行了展望。     

Bioinformatics models in drug abuse and Neuro-AIDS: Using and developing databases

The magnitude of the problems of drug abuse and Neuro-AIDS warrants the development of novel approaches for testing hypotheses in diagnosis and treatment ranging from cell culture models to developing databases. In this study, cultured neurons were treated with/without HIV-TAT, ENV, or cocaine in a 2x2x2 expression study design. RNA was purified, labeled, and expression data were produced and analyzed using ANOVA. Thus, we identified 35 genes that were significantly expressed across treatment conditions. A diagram is presented showing examples of molecular relationships involving a significantly expressed gene in the current study (SOX2). Also, we use this information to discuss examples of gene expression interactions as a means to portray significance and complexity of gene expression studies in Drug Abuse and Neuro-AIDS. Furthermore, we discuss here that critical interactions remain undetected, which may be unravelled by developing robust database systems containing large datasets and gleaned information from collaborating scientists . Hence, we are developing a public domain database we named The Agora database , that will served as a shared infrastructure to query, deposit, and review information related to drug abuse and dementias including Neuro-AIDS. A workflow of this database is also outlined in this paper.     

Statistical inferences in phylogeography

In conventional phylogeographic studies, historical demographic processes are elucidated from the geographical distribution of individuals represented on an inferred gene tree. However, the interpretation of gene trees in this context can be difficult as the same demographic/geographical process can randomly lead to multiple different genealogies. Likewise, the same gene trees can arise under different demographic models. This problem has led to the emergence of many statistical methods for making phylogeographic inferences. A popular phylogeographic approach based on nested clade analysis is challenged by the fact that a certain amount of the interpretation of the data is left to the subjective choices of the user, and it has been argued that the method performs poorly in simulation studies. More rigorous statistical methods based on coalescence theory have been developed. However, these methods may also be challenged by computational problems or poor model choice. In this review, we will describe the development of statistical methods in phylogeographic analysis, and discuss some of the challenges facing these methods.     

Engineering microbial systems to explore ecological and evolutionary dynamics

A major goal of biological research is to provide a mechanistic understanding of diverse biological processes. To this end, synthetic biology offers a powerful approach, whereby biological questions can be addressed in a well-defined framework. By constructing simple gene circuits, such studies have generated new insights into the design principles of gene regulatory networks. Recently, this strategy has been applied to analyze ecological and evolutionary questions, where population-level interactions are critical. Here, we highlight recent development of such systems and discuss how they were used to address problems in ecology and evolutionary biology. As illustrated by these examples, synthetic ecosystems provide a unique platform to study ecological and evolutionary phenomena that are challenging to study in their natural contexts.     

Comparative microarray analysis

Microarrays enable high-throughput parallel gene expression analysis, and their use has grown exponentially during the past decade. We are now in a position where individual experiments could benefit from using the swelling public data repositories to allow microarrays to progress from being a hypothesis-generating tool to a powerful resource that can be used to test hypothesis about biology. Comparative microarray analysis could better distinguish phenotypes from associated phenotypes; identify valid differentially expressed genes by combining many studies; test new hypothesis; and discover fundamental patterns of gene regulation. This review aims to describe the additional methodology needed for such comparative microarray analysis, and we identify and discuss a number of problems such as loss of published data, lack of annotations, and variable array quality, which need to be solved before comparative microarray analysis can be used in a more systematic and powerful manner.     

Predicting gene expression level from codon usage bias

The "expression measure" of a gene, E(g), is a statistic devised to predict the level of gene expression from codon usage bias. E(g) has been used extensively to analyze prokaryotic genome sequences. We discuss 2 problems with this approach. First, the formulation of E(g) is such that genes with the strongest selected codon usage bias are not likely to have the highest predicted expression levels; indeed the correlation between E(g) and expression level is weak among moderate to highly expressed genes. Second, in some species, highly expressed genes do not have unusual codon usage, and so codon usage cannot be used to predict expression levels. We outline a simple approach, first to check whether a genome shows evidence of selected codon usage bias and then to assess the strength of bias in genes as a guide to their likely expression level; we illustrate this with an analysis of Shewanella oneidensis.     

植物RNA病毒载体构建策略

作为对传统的植物转化载体的补充,植物ＲＮＡ病毒载体具有受体植物广泛,且转化需要的时间短,特别适宜于大量表达外源基因．虽然外源基因的稳定性存在一定的问题,但大量的事例说明,作为一种新型的外源基因在植物中表达的载体,它具有其独到的特点．本文就最新利用ＲＮＡ病毒转化植物的报道进行了综述