A Phylogenetic Framework for the Aquaporin Family in Eukaryotes

A comprehensive evolutionary analysis of aquaporins, a family of intrinsic membrane proteins that function as water channels, was conducted to establish groups of homology (i.e., to identify orthologues and paralogues) within the family and to gain insights into the functional constraints acting on the structure of the aquaporin molecule structure. Aquaporins are present in all living organisms, and therefore, they provide an excellent opportunity to further our understanding of the broader biological significance of molecular evolution by gene duplication followed by functional and structural specialization. Based on the resulting phylogeny, the 153 channel proteins analyzed were classified into six major paralogous groups: (1) GLPs, or glycerol-transporting channel proteins, which include mammalian AQP3, AQP7, and AQP9, several nematode paralogues, a yeast paralogue, and Escherichia coli GLP; (2) AQPs, or aquaporins, which include metazoan AQP0, AQP1, AQP2, AQP4, AQP5, and AQP6; (3) PIPs, or plasma membrane intrinsic proteins of plants, which include PIP1 and PIP2; (4) TIPs, or tonoplast intrinsic proteins of plants, which include alphaTIP, gammaTIP, and deltaTIP; (5) NODs, or nodulins of plants; and (6) AQP8s, or metazoan aquaporin 8 proteins. Of these groups, AQPs, PIPs, and TIPs cluster together. According to the results, the capacity to transport glycerol shown by several members of the family was acquired only early in the history of the family. The new phylogeny reveals that several water channel proteins are misclassified and require reassignment, whereas several previously undetermined ones can now be classified with confidence. The deduced phylogenetic framework was used to characterize the molecular features of water channel proteins. Three motifs are common to all family members: AEF (Ala-Glu-Phe), which is located in the N-terminal domain; and two NPA (Asp-Pro-Ala) boxes, which are located in the center and C-terminal domains, respectively. Other residues are found to be conserved within the major groups but not among them. Overall, the PIP subfamily showed the least variation. In general, no radical amino acid replacements affecting tertiary structure were identified, with the exception of Ala-->Ser in the TIP subfamily. Constancy of rates of evolution was demonstrated within the different paralogues but rejected among several of them (GLP and NOD).     

A Stack-based Ensemble Framework for Detecting Cancer MicroRNA Biomarkers

MicroRNA(miRNA) plays vital roles in biological processes like RNA splicing and regulation of gene expression. Studies have revealed that there might be possible links between oncogenesis and expression pro?les of some miRNAs, due to their differential expression between normal and tumor tissues. However, the automatic classi?cation of miRNAs into different categories by considering the similarity of their expression values has rarely been addressed. This article proposes a solution framework for solving some real-life classi?cation problems related to cancer,miRNA, and mRNA expression datasets. In the ?rst stage, a multiobjective optimization based framework, non-dominated sorting genetic algorithm II, is proposed to automatically determine the appropriate classi?er type, along with its suitable parameter and feature combinations, pertinent for classifying a given dataset. In the second page, a stack-based ensemble technique is employed to get a single combinatorial solution from the set of solutions obtained in the ?rst stage. The performance of the proposed two-stage approach is evaluated on several cancer and RNA expression pro-?le datasets. Compared to several state-of-the-art approaches for classifying different datasets, our method shows supremacy in the accuracy of classi?cation.     

Comparative Genomic Hybridization As a New Method for Detection of Genomic Imbalance

Comparative Genomic Hybridization (CGH) is a molecular cytogenetic method for detecting chromosomal imbalances by comparing the copy number of DNA sequences in cells of tested tissue and the reference specimen. CGH is based on two-color fluorescence suppressive in situ hybridization of genomic test and reference DNAs, each labeled with a different fluorochrome, to metaphase chromosomes of a healthy individual. First described by Kallioniemi et al. in 1992, the CGH assay has been widely used for identification and characterization of both numerical and unbalanced structural chromosome abnormalities in cells of different tissues at various pathological conditions in humans, especially in tumor diseases. We discuss the specific features and quality control of comparative genomic hybridization, its advantages and limitations in detection of genomic imbalance and the prospects for development of this technology.     

A Statistical Framework for Improving Genomic Annotations of Prokaryotic Essential Genes

Large-scale systematic analysis of gene essentiality is an important step closer toward unraveling the complex relationship between genotypes and phenotypes. Such analysis cannot be accomplished without unbiased and accurate annotations of essential genes. In current genomic databases, most of the essential gene annotations are derived from whole-genome transposon mutagenesis (TM), the most frequently used experimental approach for determining essential genes in microorganisms under defined conditions. However, there are substantial systematic biases associated with TM experiments. In this study, we developed a novel Poisson model–based statistical framework to simulate the TM insertion process and subsequently correct the experimental biases. We first quantitatively assessed the effects of major factors that potentially influence the accuracy of TM and subsequently incorporated relevant factors into the framework. Through iteratively optimizing parameters, we inferred the actual insertion events occurred and described each gene’s essentiality on probability measure. Evaluated by the definite mapping of essential gene profile in Escherichia coli, our model significantly improved the accuracy of original TM datasets, resulting in more accurate annotations of essential genes. Our method also showed encouraging results in improving subsaturation level TM datasets. To test our model’s broad applicability to other bacteria, we applied it to Pseudomonas aeruginosa PAO1 and Francisella tularensis novicida TM datasets. We validated our predictions by literature as well as allelic exchange experiments in PAO1. Our model was correct on six of the seven tested genes. Remarkably, among all three cases that our predictions contradicted the TM assignments, experimental validations supported our predictions. In summary, our method will be a promising tool in improving genomic annotations of essential genes and enabling large-scale explorations of gene essentiality. Our contribution is timely considering the rapidly increasing essential gene sets. A Webserver has been set up to provide convenient access to this tool. All results and source codes are available for download upon publication at http://research.cchmc.org/essentialgene/.     

Searching for Sequence Directed Mutagenesis in Eukaryotes

Sequence directed mutagenesis is a mechanism by which imperfect repeats “repair” each other to become perfect, generating mutations. This process is known to be prevalent in prokaryotes and it has been implicated in several human genetic diseases. Here we test whether sequence directed mutagenesis occurs in the protein coding sequences of eukaryotes using extensive DNA sequence data from humans, mice, Drosophila, nematodes, yeast, and Arabidopsis. Using two tests we find little evidence of sequence directed mutagenesis. We conclude that sequence directed mutagenesis is not prevalent in eukaryotes and that the examples of human diseases, apparently caused by sequence directed mutagenesis, are probably coincidental. [Reviewing Editor: Dr. Richard Kliman]     

Structure-based Comparative Analysis and Prediction of N-linked Glycosylation Sites in Evolutionarily Distant Eukaryotes

The asparagine-X-serine/threonine (NXS/T) motif, where X is any amino acid except proline, is the consensus motif for N-linked glycosylation. Significant numbers of high-resolution crystal structures of glycosylated proteins allow us to carry out structural analysis of the N-linked glycosylation sites (NGS). Our analysis shows that there is enough structural information from diverse glycoproteins to allow the development of rules which can be used to predict NGS. A Python-based tool was developed to investigate asparagines implicated in N-glycosylation in five species: Homo sapiens, Mus musculus, Drosophila melanogaster, Arabidopsis thaliana and Saccharomyces cerevisiae. Our analysis shows that 78% of all asparagines of NXS/T motif involved in N-glycosylation are localized in the loop/turn conformation in the human proteome. Similar distribution was revealed for all the other species examined. Comparative analysis of the occurrence of NXS/T motifs not known to be glycosylated and their reverse sequence (S/TXN) shows a similar distribution across the secondary structural elements, indicating that the NXS/T motif in itself is not biologically relevant. Based on our analysis, we have defined rules to determine NGS. Using machine learning methods based on these rules we can predict with 93% accuracy if a particular site will be glycosylated. If structural information is not available the tool uses structural prediction results resulting in 74% accuracy. The tool was used to identify glycosylation sites in 108 human proteins with structures and 2247 proteins without structures that have acquired NXS/T site/s due to non-synonymous variation. The tool, Structure Feature Analysis Tool (SFAT), is freely available to the public at http://hive.biochemistry.gwu.edu/tools/sfat.     

Detecting and Characterizing Genomic Signatures of Positive Selection in Global Populations

Natural selection is a significant force that shapes the architecture of the human genome and introduces diversity across global populations. The question of whether advantageous mutations have arisen in the human genome as a result of single or multiple mutation events remains unanswered except for the fact that there exist a handful of genes such as those that confer lactase persistence, affect skin pigmentation, or cause sickle cell anemia. We have developed a long-range-haplotype method for identifying genomic signatures of positive selection to complement existing methods, such as the integrated haplotype score (iHS) or cross-population extended haplotype homozygosity (XP-EHH), for locating signals across the entire allele frequency spectrum. Our method also locates the founder haplotypes that carry the advantageous variants and infers their corresponding population frequencies. This presents an opportunity to systematically interrogate the whole human genome whether a selection signal shared across different populations is the consequence of a single mutation process followed subsequently by gene flow between populations or of convergent evolution due to the occurrence of multiple independent mutation events either at the same variant or within the same gene. The application of our method to data from 14 populations across the world revealed that positive-selection events tend to cluster in populations of the same ancestry. Comparing the founder haplotypes for events that are present across different populations revealed that convergent evolution is a rare occurrence and that the majority of shared signals stem from the same evolutionary event.     

Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants

Array CGH for the detection of genomic copy number variants has replaced G-banded karyotype analysis. This paper describes the technology and its application in a clinical diagnostic service laboratory. DNA extracted from a patient’s sample (blood, saliva or other tissue types) is labeled with a fluorochrome (either cyanine 5 or cyanine 3). A reference DNA sample is labeled with the opposite fluorochrome. There follows a cleanup step to remove unincorporated nucleotides before the labeled DNAs are mixed and resuspended in a hybridization buffer and applied to an array comprising ~60,000 oligonucleotide probes from loci across the genome, with high probe density in clinically important areas. Following hybridization, the arrays are washed, then scanned and the resulting images are analyzed to measure the red and green fluorescence for each probe. Software is used to assess the quality of each probe measurement, calculate the ratio of red to green fluorescence and detect potential copy number variants.     

Detecting the Genomic Signature of Divergent Selection in Presence of Gene Flow

The study of local adaptation is a main focus of evolutionary biology since it may contributeto explain the current species diversity. The genomic scan procedures permit for the first time to studythe connection between specific DNA patterns and processes as natural selection, genetic drift, recombination,mutation and gene flow. Accordingly, the information on genomes from non-model organismsincreases and the interest on detecting the signal of natural selection in the DNA sequences of differentpopulations also raises. The main goal of the present work is to explore a sequence-based methodfor detecting natural selection in divergent populations connected by migration. In doing so, we relyon a recently published statistic based upon th e definition of haplotype allelic classes (HAC). The originalmeasure was modified to be more sensitive to intermediate frequencies in non-model species. A linkage-disequilibrium-based method was also assayed and individual-based simulations were performed to test the methods. Theresults suggest that the HAC-based methods and, specifically, the new proposed method are quite powerful for detectingthe footprint of moderate divergent selection. They are also robust to reasonable model misspecification. One obvious advantageof the new algorithm is that it does not require knowledge of the allelic state.     

An Assessment Framework for Optimal FMS Effectiveness

Equipment failures in an FMS are significant to performance and can lead to costly, incorrect decisions. Fortunately, effectiveness measurement techniques can be mapped to clever modeling frameworks to help predict, track, and then improve upon the FMS performability or mission effectiveness, and improve maintenance. This article provides sources and guidelines for efficient and effective FMS modeling, a framework for applying the modeling to predict the impact on customers from their point of view, and a method for tying it all together for improving the FMS effectiveness. It is not enough to simply examine the working and failed states of an FMS or even to calculate common reliability metrics. It is necessary to consider the FMS as a whole, and that system includes the needs of the customer and the business. It is also necessary to be purposeful about the measures of performance selected and to support the measures of effectiveness. In this article, we present: a framework for considering customer needs in the measures of effectiveness for FMS; modeling approaches for solving for effectiveness measures; and an example to show how to apply it to an FMS, to improve it or plan for meeting specific customer needs.     

An Integrative Computational Approach for Prioritization of Genomic Variants

An essential step in the discovery of molecular mechanisms contributing to disease phenotypes and efficient experimental planning is the development of weighted hypotheses that estimate the functional effects of sequence variants discovered by high-throughput genomics. With the increasing specialization of the bioinformatics resources, creating analytical workflows that seamlessly integrate data and bioinformatics tools developed by multiple groups becomes inevitable. Here we present a case study of a use of the distributed analytical environment integrating four complementary specialized resources, namely the Lynx platform, VISTA RViewer, the Developmental Brain Disorders Database (DBDB), and the RaptorX server, for the identification of high-confidence candidate genes contributing to pathogenesis of spina bifida. The analysis resulted in prediction and validation of deleterious mutations in the SLC19A placental transporter in mothers of the affected children that causes narrowing of the outlet channel and therefore leads to the reduced folate permeation rate. The described approach also enabled correct identification of several genes, previously shown to contribute to pathogenesis of spina bifida, and suggestion of additional genes for experimental validations. The study demonstrates that the seamless integration of bioinformatics resources enables fast and efficient prioritization and characterization of genomic factors and molecular networks contributing to the phenotypes of interest.     

西瓜与近缘种亲缘关系的比较基因组原位杂交分析

物种间亲缘关系的研究是杂交育种的理论基础,野生西瓜在西瓜育种中具有重要作用,然而目前对西瓜属物种间亲缘关系的研究十分有限,而且对西瓜属物种的分类问题还存在分歧.比较基因组原位杂交是分析物种间亲缘关系的有效手段,本研究以西瓜基因组DNA作探针,分别对缺须西瓜、热迷西瓜、药西瓜和诺丹西瓜有丝分裂中期染色体进行了比较基因组原位杂交分析,揭示了西瓜属物种间的亲缘关系,同时对分类地位尚存在争议的诺丹西瓜的归属问题进行了分析,发现诺丹西瓜和甜瓜之间具有非常近的亲缘关系,本研究结果为西瓜与近缘种间的远缘杂交提供了重要的理论依据.     

Preferential Duplication of Intermodular Hub Genes: An Evolutionary Signature in Eukaryotes Genome Networks

Whole genome protein-protein association networks are not random and their topological properties stem from genome evolution mechanisms. In fact, more connected, but less clustered proteins are related to genes that, in general, present more paralogs as compared to other genes, indicating frequent previous gene duplication episodes. On the other hand, genes related to conserved biological functions present few or no paralogs and yield proteins that are highly connected and clustered. These general network characteristics must have an evolutionary explanation. Considering data from STRING database, we present here experimental evidence that, more than not being scale free, protein degree distributions of organisms present an increased probability for high degree nodes. Furthermore, based on this experimental evidence, we propose a simulation model for genome evolution, where genes in a network are either acquired de novo using a preferential attachment rule, or duplicated with a probability that linearly grows with gene degree and decreases with its clustering coefficient. For the first time a model yields results that simultaneously describe different topological distributions. Also, this model correctly predicts that, to produce protein-protein association networks with number of links and number of nodes in the observed range for Eukaryotes, it is necessary 90% of gene duplication and 10% of de novo gene acquisition. This scenario implies a universal mechanism for genome evolution.     

Detection of Aneuploidy in Spontaneous Abortions Using Comparative Genomic Hybridization

Comparative genomic hybridization (CGH) technique was used to examine a set of ten spontaneous abortions whose cell cultures were characterized by the lack of proliferation in vitro, and thereby, were not available for the analysis by means of routine cytogenetic methods. Five abortions (50%) had aneuploidy of autosomes, including trisomy 10, 14, 18, and 21, and monosomy 22. The latter variant of unbalanced chromosomal abnormalities is rarely detected in spontaneous abortions by use of conventional cytogenetic methods. The results were validated by using fluorescent in situ hybridization (FISH) analysis with centromere-specific DNA probes. Embryos with trisomy 10 and monosomy 22 displayed mosaicism with the frequencies of abnormal cell clones constituting 68 and 33% respectively. The advantages and limitations of the applying of CGH technique for detection of genomic abnormalities in both nonmosaic and mosaic forms are discussed.     

Preimplantation Genetic Diagnosis for Aneuploidy and Translocations Using Array Comparative Genomic Hybridization

At least 50% of human embryos are abnormal, and that increases to 80% in women 40 years or older. These abnormalities result in low implantation rates in embryos transferred during in vitro fertilization procedures, from 30% in women <35 years to 6% in women 40 years or older. Thus selecting normal embryos for transfer should improve pregnancy results. The genetic analysis of embryos is called Preimplantation Genetic Diagnosis (PGD) and for chromosome analysis it was first performed using FISH with up to 12 probes analyzed simultaneously on single cells. However, suboptimal utilization of the technique and the complexity of fixing single cells produced conflicting results. PGD has been invigorated by the introduction of microarray testing which allows for the analysis of all 24 chromosome types in one test, without the need of cell fixation, and with staggering redundancy, making the test much more robust and reliable. Recent data published and presented at scientific meetings has been suggestive of increased implantation rates and pregnancy rates following microarray testing, improvements in outcome that have been predicted for quite some time. By using markers that cover most of the genome, not only aneuploidy can be detected in single cells but also translocations. Our validation results indicate that array CGH has a 6Mb resolution in single cells, and thus the majority of translocations can be analyzed since this is also the limit of karyotyping. Even for translocations with smaller exchanged fragments, provided that three out of the four fragments are above 6Mb, the translocation can be detected.     

实验大鼠泰泽菌检测方法的比较研究

目的　比较实验大鼠泰泽菌检测方法─nested PCR、IFA、免疫抑制诱发试验 触片染色镜检和组织病理学诊断。方法　根据泰泽菌 16SrDNA合成引物 ,对 16个菌株作nested PCR扩增并进行特异性、敏感性试验、验证。将此PCR应用于 2 0只免疫抑制Wistar大鼠和 5只非免疫抑制SD大鼠泰泽菌检测 ,并作IFA、常规细菌学检测和组织病理学诊断。结果　nested PCR中仅有泰泽菌出现 196bp特异性扩增条带 ;而 15株非泰泽菌均未出现此扩增条带。该PCR能检出 10pg泰泽菌DNA。将此PCR应用于大鼠泰泽菌检测 ,结果未检出阳性样品。nested PCR与常规细菌学检测、组织病理学诊断结果相一致。采用IFA方法 ,以购得的大鼠泰泽菌抗原片对上述 2 5份大鼠血清进行检测 ,结果有 6份血清产生非特异性反应。结论　采用IFA对动物群进行筛查出现阳性结果 ,须采用免疫抑制诱发试验、PCR和组织病理学诊断技术组合进一步验证。本研究建立的nested PCR方法 ,特异、敏感、快速 ,结合组织病理学诊断技术对实验动物泰泽菌感染可做出精确诊断。     

使用极端样本构建一个基于熵的对数量性状位点关联研究的统计量

基于熵理论,Zhao等提出了一个对复杂疾病易感基因进行关联研究的统计量．本文拓展了这一理论到数量性状,利用熵理论。获得了一个对数量性状位点进行关联研究的采用群体极端样本及稠密标记的统计量．