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).     

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 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.     

Detecting Genomic Aberrations Using Products in a Multiscale Analysis

Summary Genomic instability, such as copy‐number losses and gains, occurs in many genetic diseases. Recent technology developments enable researchers to measure copy numbers at tens of thousands of markers simultaneously. In this article, we propose a nonparametric approach for detecting the locations of copy‐number changes and provide a measure of significance for each change point. The proposed test is based on seeking scale‐based changes in the sequence of copy numbers, which is ordered by the marker locations along the chromosome. The method leads to a natural way to estimate the null distribution for the test of a change point and adjusted p‐values for the significance of a change point using a step‐down maxT permutation algorithm to control the family‐wise error rate. A simulation study investigates the finite sample performance of the proposed method and compares it with a more standard sequential testing method. The method is illustrated using two real data sets.     

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.     

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]     

Identification of Genomic Alterations in Pancreatic Cancer Using Array-Based Comparative Genomic Hybridization

Background

Genomic aberration is a common feature of human cancers and also is one of the basic mechanisms that lead to overexpression of oncogenes and underexpression of tumor suppressor genes. Our study aims to identify frequent genomic changes in pancreatic cancer.

Materials and Methods

We used array comparative genomic hybridization (array CGH) to identify recurrent genomic alterations and validated the protein expression of selected genes by immunohistochemistry.

Results

Sixteen gains and thirty-two losses occurred in more than 30% and 60% of the tumors, respectively. High-level amplifications at 7q21.3–q22.1 and 19q13.2 and homozygous deletions at 1p33–p32.3, 1p22.1, 1q22, 3q27.2, 6p22.3, 6p21.31, 12q13.2, 17p13.2, 17q21.31 and 22q13.1 were identified. Especially, amplification of AKT2 was detected in two carcinomas and homozygous deletion of CDKN2C in other two cases. In 15 independent validation samples, we found that AKT2 (19q13.2) and MCM7 (7q22.1) were amplified in 6 and 9 cases, and CAMTA2 (17p13.2) and PFN1 (17p13.2) were homozygously deleted in 3 and 1 cases. AKT2 and MCM7 were overexpressed, and CAMTA2 and PFN1 were underexpressed in pancreatic cancer tissues than in morphologically normal operative margin tissues. Both GISTIC and Genomic Workbench software identified 22q13.1 containing APOBEC3A and APOBEC3B as the only homozygous deletion region. And the expression levels of APOBEC3A and APOBEC3B were significantly lower in tumor tissues than in morphologically normal operative margin tissues. Further validation showed that overexpression of PSCA was significantly associated with lymph node metastasis, and overexpression of HMGA2 was significantly associated with invasive depth of pancreatic cancer.

Conclusion

These recurrent genomic changes may be useful for revealing the mechanism of pancreatic carcinogenesis and providing candidate biomarkers.     

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 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.     

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.     

实时定量PCR法检测生物技术药物中宿主基因组DNA残留

利用基于SYBR GreenⅠ荧光染料的实时定量PCR方法检测酵母表达生物技术药物产品中宿主DNA残留量。该方法检测灵敏度可达到1.0 fg/μL, DNA浓度在1.0 fg/μL～1.0 ng/μL范围内线性良好,其标准曲线的相关系数为099以上。应用该方法对3批不同实验样本进行测定,宿主DNA残留量分别为8.635×105 fg/μL、6.265×102 fg/μL和1436 fg/μL 。实验表明该方法操作简便、灵敏度高,可用于生物技术药物产品中酵母DNA残留的定量测定。     

蜘蛛基因组DNA提取方法的比较

分别用SDS法,SK251基因组DNA小量抽提试剂盒法,自制试剂盒法,对蜘蛛组织的基因组DNA进行了提取,经比较,自制试剂盒法对提取蜘蛛基因组DNA最简便面又有效的方法。     

A Bayesian Mutation–Selection Framework for Detecting Site-Specific Adaptive Evolution in Protein-Coding Genes

In recent years, codon substitution models based on the mutation–selection principle have been extended for the purpose of detecting signatures of adaptive evolution in protein-coding genes. However, the approaches used to date have either focused on detecting global signals of adaptive regimes—across the entire gene—or on contexts where experimentally derived, site-specific amino acid fitness profiles are available. Here, we present a Bayesian site-heterogeneous mutation–selection framework for site-specific detection of adaptive substitution regimes given a protein-coding DNA alignment. We offer implementations, briefly present simulation results, and apply the approach on a few real data sets. Our analyses suggest that the new approach shows greater sensitivity than traditional methods. However, more study is required to assess the impact of potential model violations on the method, and gain a greater empirical sense its behavior on a broader range of real data sets. We propose an outline of such a research program.     

PM2RA: A Framework for Detecting and Quantifying Relationship Alterations in Microbial Community

The dysbiosis of gut microbiota is associated with the pathogenesis of human diseases.However, observing shifts in the microbe abundance cannot fully reveal underlying perturbations.Examining the relationship alterations(RAs) in the microbiome between health and disease statuses provides additional hints about the pathogenesis of human diseases, but no methods were designed to detect and quantify the RAs between different conditions directly. Here, we present profile monitoring for microbial relationship alteration(PM2 RA), an analysis framework to identify and quantify the microbial RAs. The performance of PM2 RA was evaluated with synthetic data, and it showed higher specificity and sensitivity than the co-occurrence-based methods. Analyses of real microbial datasets showed that PM2 RA was robust for quantifying microbial RAs across different datasets in several diseases. By applying PM2 RA, we identified several novel or previously reported microbes implicated in multiple diseases. PM2 RA is now implemented as a web-based application available at http://www.pm2 ra-xingyinliulab.cn/.     

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.