DGGE/TGGE技术在土壤微生物分子生态学研究中的应用

传统的微生物生态学研究方法只限于环境样品中极少部分(0.1%￣1%)可培养的微生物类群,极大程度地限制了对土壤微生物群落结构的研究。综述了以16S rDNA为主要研究对象的DGGE/TGGE(Denaturing gradientgel electrophoresis,DGGE/Temperature gradient gel electrophoresis,TGGE)技术原理,以其为主要手段结合PCR扩增、克隆建库、序列测定以及种系分析对土壤微生物的群落结构和多样性研究的最新动态。DGGE/TGGE技术极大地推动了土壤微生物分子生态学的发展,同时也为实际问题的诊断、作物生长跟踪监测等提供了技术支撑,在土壤微生物分子生态学研究和生产实践中起着越来越重要的作用。     

排除法PCR加变性梯度凝胶电泳鉴别未知基因

通过鉴别未知的cry4亚组基因来确定排除法PCR加变性梯度凝脉电泳新方法。方法：应用排除法PCR的组基因和亚组基因引物扩增杀蚊毒素基因,cry4。这些引物是根据已知的cyr4基因的共有或特有DNA片段来设计的。组基因引物扩增产物被用于变性梯度凝胶电泳。平行变性梯度凝胶是由8％的聚丙烯酰胺加上20％到80％的变性剂组成。结果：已知的和未知的cry4来组基因被组基因引物扩增的产物在变性梯度凝胶电泳被分离开。尽管它们之间仅有两个碱基对不同（T在位置224和G在位置394）。应用组基因和亚组基因引物扩增,新发现的基因可被分类到次亚组基因水平。从5个苏云金芽孢杆菌亚菌中发现三个未发表的cry4亚组基因。结论：排除法PCR加变性梯度凝胶电泳是一个高度敏感、特异性和可靠性强的新方法,可用于鉴别各种未知的亚组基因。     

Ranking analysis of microarray data: a powerful method for identifying differentially expressed genes

Microarray technology provides a powerful tool for the expression profile of thousands of genes simultaneously, which makes it possible to explore the molecular and metabolic etiology of the development of a complex disease under study. However, classical statistical methods and technologies fail to be applicable to microarray data. Therefore, it is necessary and motivating to develop powerful methods for large-scale statistical analyses. In this paper, we described a novel method, called Ranking Analysis of Microarray Data (RAM). RAM, which is a large-scale two-sample t-test method, is based on comparisons between a set of ranked T statistics and a set of ranked Z values (a set of ranked estimated null scores) yielded by a "randomly splitting" approach instead of a "permutation" approach and a two-simulation strategy for estimating the proportion of genes identified by chance, i.e., the false discovery rate (FDR). The results obtained from the simulated and observed microarray data show that RAM is more efficient in identification of genes differentially expressed and estimation of FDR under undesirable conditions such as a large fudge factor, small sample size, or mixture distribution of noises than Significance Analysis of Microarrays.     

'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns

Background  

Large gene expression studies, such as those conducted using DNA arrays, often provide millions of different pieces of data. To address the problem of analyzing such data, we describe a statistical method, which we have called 'gene shaving'. The method identifies subsets of genes with coherent expression patterns and large variation across conditions. Gene shaving differs from hierarchical clustering and other widely used methods for analyzing gene expression studies in that genes may belong to more than one cluster, and the clustering may be supervised by an outcome measure. The technique can be 'unsupervised', that is, the genes and samples are treated as unlabeled, or partially or fully supervised by using known properties of the genes or samples to assist in finding meaningful groupings.     

An integrative method for identifying the over-annotated protein-coding genes in microbial genomes

The falsely annotated protein-coding genes have been deemed one of the major causes accounting for the annotating errors in public databases. Although many filtering approaches have been designed for the over-annotated protein-coding genes, some are questionable due to the resultant increase in false negative. Furthermore, there is no webserver or software specifically devised for the problem of over-annotation. In this study, we propose an integrative algorithm for detecting the over-annotated protein-coding genes in microorganisms. Overall, an average accuracy of 99.94% is achieved over 61 microbial genomes. The extremely high accuracy indicates that the presented algorithm is efficient to differentiate the protein-coding genes from the non-coding open reading frames. Abundant analyses show that the predicting results are reliable and the integrative algorithm is robust and convenient. Our analysis also indicates that the over-annotated protein-coding genes can cause the false positive of horizontal gene transfers detection. The webserver of the proposed algorithm can be freely accessible from www.cbi.seu.edu.cn/RPGM.     

A method for identifying a proposed carbohydrate-binding motif of proteins.

An examination of the binding sites of four carbohydrate binding proteins (Escherichia coli lactose repressor, E. coli arabinose-binding protein, yeast hexokinase A and Concanavalin A) revealed certain similarities of amino acid sequences and residues forming hydrogen bonds and hydrophobic interactions with the bound carbohydrate. These were: (i) Asx-Asx, hydrogen bonding to the pyranose ring oxygen and anomeric-OH group; (ii) Arg-X-X-X-(Ser/Thr), or the reverse sequence, with the Arg hydrogen bonding to the pyranose ring oxygen; (iii) Lys-(Ser/Thr)-X-X-Asp, or the reverse sequence and with interchange of the Lys-(Ser/Thr) positions, with hydrogen bonding of either or both the Lys and Asp residues to the -OH groups at carbons 2, 3, 4 or 6; (iv) a diaromatic sequence with possible hydrophobic interactions to the faces of the pyranose ring structure. An algorithm was devised to search the amino acid sequences of a large number of proteins, those known to bind carbohydrates as well as those without known carbohydrate-binding activities, for the four amino acid sequence criteria. The algorithm incorporated a weighted distance value (WDV) to assess the approximate distance between any two criteria, with the WDV being based on the predicted secondary structure of the protein amino acid sequence. When the algorithm using criteria 1 and 2 plus the WDV was applied to the sequences of 125 proteins, the method indicated the presence of the potential carbohydrate-binding site motif for 42% of proteins with known carbohydrate binding, only 8% of proteins were predicted as false positives, and the accuracy of the method was calculated to be 61.6%.(ABSTRACT TRUNCATED AT 250 WORDS)     

Research priorities for natural ecosystems in a changing global climate

Climate change poses significant emerging risks to biodiversity, ecosystem function and associated socioecological systems. Adaptation responses must be initiated in parallel with mitigation efforts, but resources are limited. As climate risks are not distributed equally across taxa, ecosystems and processes, strategic prioritization of research that addresses stakeholder‐relevant knowledge gaps will accelerate effective uptake into adaptation policy and management action. After a decade of climate change adaptation research within the Australian National Climate Change Adaptation Research Facility, we synthesize the National Adaptation Research Plans for marine, terrestrial and freshwater ecosystems. We identify the key, globally relevant priorities for ongoing research relevant to informing adaptation policy and environmental management aimed at maximizing the resilience of natural ecosystems to climate change. Informed by both global literature and an extensive stakeholder consultation across all ecosystems, sectors and regions in Australia, involving thousands of participants, we suggest 18 priority research topics based on their significance, urgency, technical and economic feasibility, existing knowledge gaps and potential for cobenefits across multiple sectors. These research priorities provide a unified guide for policymakers, funding organizations and researchers to strategically direct resources, maximize stakeholder uptake of resulting knowledge and minimize the impacts of climate change on natural ecosystems. Given the pace of climate change, it is imperative that we inform and accelerate adaptation progress in all regions around the world.     

FSSA: a novel method for identifying functional signatures from structural alignments

MOTIVATION: It is commonly believed that sequence determines structure, which in turn determines function. However, the presence of many proteins with the same structural fold but different functions suggests that global structure and function do not always correlate well. RESULTS: We propose a method for accurate functional annotation, based on identification of functional signatures from structural alignments (FSSA) using the Structural Classification of Proteins (SCOP) database. The FSSA method is superior at function discrimination and classification compared with several methods that directly inherit functional annotation information from homology inference, such as Smith-Waterman, PSI-BLAST, hidden Markov models and structure comparison methods, for a large number of structural fold families. Our results indicate that the contributions of amino acid residue types and positions to structure and function are largely separable for proteins in multi-functional fold families.     

Novel method for identifying sequence-specific DNA-binding proteins.

We developed a general method for the enrichment and identification of sequence-specific DNA-binding proteins. A well-characterized protein-DNA interaction is used to isolate from crude cellular extracts or fractions thereof proteins which bind to specific DNA sequences; the method is based solely on this binding property of the proteins. The DNA sequence of interest, cloned adjacent to the lac operator DNA segment is incubated with a lac repressor-beta-galactosidase fusion protein which retains full operator and inducer binding properties. The DNA fragment bound to the lac repressor-beta-galactosidase fusion protein is precipitated by the addition of affinity-purified anti-beta-galactosidase immobilized on beads. This forms an affinity matrix for any proteins which might interact specifically with the DNA sequence cloned adjacent to the lac operator. When incubated with cellular extracts in the presence of excess competitor DNA, any protein(s) which specifically binds to the cloned DNA sequence of interest can be cleanly precipitated. When isopropyl-beta-D-thiogalactopyranoside is added, the lac repressor releases the bound DNA, and thus the protein-DNA complex consisting of the specific restriction fragment and any specific binding protein(s) is released, permitting the identification of the protein by standard biochemical techniques. We demonstrate the utility of this method with the lambda repressor, another well-characterized DNA-binding protein, as a model. In addition, with crude preparations of the yeast mitochondrial RNA polymerase, we identified a 70,000-molecular-weight peptide which binds specifically to the promoter region of the yeast mitochondrial 14S rRNA gene.     

A framework for community and ecosystem genetics: from genes to ecosystems

Can heritable traits in a single species affect an entire ecosystem? Recent studies show that such traits in a common tree have predictable effects on community structure and ecosystem processes. Because these 'community and ecosystem phenotypes' have a genetic basis and are heritable, we can begin to apply the principles of population and quantitative genetics to place the study of complex communities and ecosystems within an evolutionary framework. This framework could allow us to understand, for the first time, the genetic basis of ecosystem processes, and the effect of such phenomena as climate change and introduced transgenic organisms on entire communities.     

Sample size for identifying differentially expressed genes in microarray experiments.

Microarray technology allows simultaneous comparison of expression levels of thousands of genes under each condition. This paper concerns sample size calculation in the identification of differentially expressed genes between a control and a treated sample. In a typical experiment, only a fraction of genes (altered genes) is expected to be differentially expressed between two samples. Sample size determination depends on a number of factors including the specified significance level (alpha), the desired statistical power (1-beta), the fraction (eta) of truly altered genes out of the total g genes studied, and the effect sizes (Delta) for the altered genes. This paper proposes a method to calculate the number of arrays required to detect at least 100lambda % (where 0 < lambda < or = 1) of the truly altered genes under the model of an equal effect size for all altered genes. The required numbers of arrays are tabulated for various values of alpha, beta, Delta, eta, and lambda for the one-sample and two-sample t-tests for g = 10,000. Based on the proposed approach, to identify up to 90% of truly altered genes among the unknown number of truly altered genes, the estimated numbers of arrays needed appear to be manageable. For instance, when the standardized effect size is at least 2.0, the number of arrays needed is less than or equal to 14 for the two-sample t-test and is less than or equal to 10 for the one-sample t-test. As the cost per array declines, such array numbers become practical. The proposed method offers a simple, intuitive, and practical way to determine the number of arrays needed in microarray experiments in which the true correlation structure among the genes under investigation cannot be reasonably assumed. An example dataset is used to illustrate the use of the proposed approach to plan microarray experiments.     

Ranking analysis for identifying differentially expressed genes

Microarrays allow researchers to examine the expression of thousands of genes simultaneously. However, identification of genes differentially expressed in microarray experiments is challenging. With an optimal test statistic, we rank genes and estimate a threshold above which genes are considered to be differentially expressed genes (DE). This overcomes the embarrassing shortcoming of many statistical methods to determine the cut-off values in ranking analysis. Experiments demonstrate that our method is a good performance and avoids the problems with graphical examination and multiple hypotheses testing that affect alternative approaches. Comparing to those well known methods, our method is more sensitive to data sets with small differentially expressed values and not biased in favor of data sets based on certain distribution models.     

The microbial carbon pump: from genes to ecosystems

The majority of marine dissolved organic carbon (DOC) is resistant to biological degradation and thus can remain in the water column for thousands of years, constituting carbon sequestration in the ocean. To date the origin of such recalcitrant DOC (RDOC) is unclear. A recently proposed conceptual framework, the microbial carbon pump (MCP), emphasizes the microbial transformation of organic carbon from labile to recalcitrant states. The MCP is concerned with both microbial uptakes and outputs of DOC compounds, covering a wide range from gene to ecosystem levels. In this minireview, the ATP binding cassette (ABC) transporter is used as an example for the microbial processing of DOC at the genetic level. The compositions of the ABC transporter genes of the two major marine bacterial clades Roseobacter and SAR11 demonstrate that they have distinct patterns in DOC utilization: Roseobacter strains have the advantage of taking up carbohydrate DOC, while SAR11 bacteria prefer nitrogen-containing DOC. At the ecosystem level, bacterially derived RDOC based on d-amino acid biomarkers is reported to be responsible for about a quarter of the total marine RDOC pool. Under future global warming scenarios, partitioning of primary production into DOC could be enhanced, and thus the MCP could play an even more important role in carbon sequestration by the ocean. Joint efforts to study the MCP from multiple disciplines are required to obtain a better understanding of ocean carbon cycle and its coupling with global change.     

Mapping and identifying genes for asthma and psoriasis

Susceptibility genes for complex diseases are characterized by reduced penetrance, caused by the influence of other genes, the environment or stochastic events. Recently, positional cloning efforts have yielded several candidate susceptibility genes in different complex disorders such as Crohn's disease and asthma. Within a genetic locus, however, the identification of the effector gene may pose further challenges and require functional studies. I review two examples of such challenges: the cloning of GPR154 (GPRA) and AAA1 on chromosome 7p14 at a susceptibility locus for atopy and asthma, and the study of HLA-Cw6, CCHCR1 (HCR) and CDSN on chromosome 6p21 at PSORS1, the major susceptibility locus for psoriasis. The susceptibility locus for atopy and asthma contains two genes and only one of them is protein coding. We studied its isoform-specific expression in bronchial biopsies and in a mouse model of ovalbumin-induced inflammation of bronchial epithelia. In the PSORS1 locus, strong linkage disequilibrium between genes has made it difficult to distinguish the effects of the three nearby genes. We engineered transgenic mice with either a HCR non-risk allele or the HCR*WWCC risk allele controlled by the cytokeratin-14 promoter. The results suggested that the overexpression of HCR in mouse skin was insufficient to induce a psoriasiform phenotype, but it appeared to induce allele-specific gene expression changes that were similar to those observed in psoriatic skin.