Reanalysis suggests that genomic islands of speciation are due to reduced diversity,not reduced gene flow

The metaphor of ‘genomic islands of speciation’ was first used to describe heterogeneous differentiation among loci between the genomes of closely related species. The biological model proposed to explain these differences was that the regions showing high levels of differentiation were resistant to gene flow between species, while the remainder of the genome was being homogenized by gene flow and consequently showed lower levels of differentiation. However, the conditions under which such differentiation can occur at multiple unlinked loci are restrictive; additionally, essentially, all previous analyses have been carried out using relative measures of divergence, which can be misleading when regions with different levels of recombination are compared. Here, we test the model of differential gene flow by asking whether absolute divergence is also higher in the previously identified ‘islands’. Using five species pairs for which full sequence data are available, we find that absolute measures of divergence are not higher in genomic islands. Instead, in all cases examined, we find reduced diversity in these regions, a consequence of which is that relative measures of divergence are abnormally high. These data therefore do not support a model of differential gene flow among loci, although islands of relative divergence may represent loci involved in local adaptation. Simulations using the program IMa2 further suggest that inferences of any gene flow may be incorrect in many comparisons. We instead present an alternative explanation for heterogeneous patterns of differentiation, one in which postspeciation selection generates patterns consistent with multiple aspects of the data.     

Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection

Yeasts are known to have versatile metabolic traits, while how these metabolic traits have evolved has not been elucidated systematically. We performed integrative evolution analysis to investigate how genomic evolution determines trait generation by reconstructing genome‐scale metabolic models (GEMs) for 332 yeasts. These GEMs could comprehensively characterize trait diversity and predict enzyme functionality, thereby signifying that sequence‐level evolution has shaped reaction networks towards new metabolic functions. Strikingly, using GEMs, we can mechanistically map different evolutionary events, e.g. horizontal gene transfer and gene duplication, onto relevant subpathways to explain metabolic plasticity. This demonstrates that gene family expansion and enzyme promiscuity are prominent mechanisms for metabolic trait gains, while GEM simulations reveal that additional factors, such as gene loss from distant pathways, contribute to trait losses. Furthermore, our analysis could pinpoint to specific genes and pathways that have been under positive selection and relevant for the formulation of complex metabolic traits, i.e. thermotolerance and the Crabtree effect. Our findings illustrate how multidimensional evolution in both metabolic network structure and individual enzymes drives phenotypic variations.     

Use of genetically engineered microorganisms (GEMs) for the bioremediation of contaminants

This paper presents a critical review of the literature on the application of genetically engineered microorganisms (GEMs) in bioremediation. The important aspects of using GEMs in bioremediation, such as development of novel strains with desirable properties through pathway construction and the modification of enzyme specificity and affinity, are discussed in detail. Particular attention is given to the genetic engineering of bacteria using bacterial hemoglobin (VHb) for the treatment of aromatic organic compounds under hypoxic conditions. The application of VHb technology may advance treatment of contaminated sites, where oxygen availability limits the growth of aerobic bioremediating bacteria, as well as the functioning of oxygenases required for mineralization of many organic pollutants. Despite the many advantages of GEMs, there are still concerns that their introduction into polluted sites to enhance bioremediation may have adverse environmental effects, such as gene transfer. The extent of horizontal gene transfer from GEMs in the environment, compared to that of native organisms including benefits regarding bacterial bioremediation that may occur as a result of such transfer, is discussed. Recent advances in tracking methods and containment strategies for GEMs, including several biological systems that have been developed to detect the fate of GEMs in the environment, are also summarized in this review. Critical research questions pertaining to the development and implementation of GEMs for enhanced bioremediation have been identified and posed for possible future research.     

Use of Genetically Engineered Microorganisms (GEMs) for the Bioremediation of Contaminants

ABSTRACT

This paper presents a critical review of the literature on the application of genetically engineered microorganisms (GEMs) in bioremediation. The important aspects of using GEMs in bioremediation, such as development of novel strains with desirable properties through pathway construction and the modification of enzyme specificity and affinity, are discussed in detail. Particular attention is given to the genetic engineering of bacteria using bacterial hemoglobin (VHb) for the treatment of aromatic organic compounds under hypoxic conditions. The application of VHb technology may advance treatment of contaminated sites, where oxygen availability limits the growth of aerobic bioremediating bacteria, as well as the functioning of oxygenases required for mineralization of many organic pollutants. Despite the many advantages of GEMs, there are still concerns that their introduction into polluted sites to enhance bioremediation may have adverse environmental effects, such as gene transfer. The extent of horizontal gene transfer from GEMs in the environment, compared to that of native organisms including benefits regarding bacterial bioremediation that may occur as a result of such transfer, is discussed. Recent advances in tracking methods and containment strategies for GEMs, including several biological systems that have been developed to detect the fate of GEMs in the environment, are also summarized in this review. Critical research questions pertaining to the development and implementation of GEMs for enhanced bioremediation have been identified and posed for possible future research.     

基于基因互作网络熵量化细胞分化状态

细胞动态过程的研究表明,细胞在动态过程中会发生状态变化,主要由细胞内部的基因表达情况控制.随着高通量测序技术的发展,大量的基因表达数据能够在单细胞水平上获得细胞真实的基因表达信息.然而,现有大多数研究方法需要使用除基因表达以外其他的信息,带来了额外的复杂度和不确定性.此外,普遍存在的"缺失值"事件更是影响了对细胞动态发...     

Discovering biological progression underlying microarray samples

In biological systems that undergo processes such as differentiation, a clear concept of progression exists. We present a novel computational approach, called Sample Progression Discovery (SPD), to discover patterns of biological progression underlying microarray gene expression data. SPD assumes that individual samples of a microarray dataset are related by an unknown biological process (i.e., differentiation, development, cell cycle, disease progression), and that each sample represents one unknown point along the progression of that process. SPD aims to organize the samples in a manner that reveals the underlying progression and to simultaneously identify subsets of genes that are responsible for that progression. We demonstrate the performance of SPD on a variety of microarray datasets that were generated by sampling a biological process at different points along its progression, without providing SPD any information of the underlying process. When applied to a cell cycle time series microarray dataset, SPD was not provided any prior knowledge of samples' time order or of which genes are cell-cycle regulated, yet SPD recovered the correct time order and identified many genes that have been associated with the cell cycle. When applied to B-cell differentiation data, SPD recovered the correct order of stages of normal B-cell differentiation and the linkage between preB-ALL tumor cells with their cell origin preB. When applied to mouse embryonic stem cell differentiation data, SPD uncovered a landscape of ESC differentiation into various lineages and genes that represent both generic and lineage specific processes. When applied to a prostate cancer microarray dataset, SPD identified gene modules that reflect a progression consistent with disease stages. SPD may be best viewed as a novel tool for synthesizing biological hypotheses because it provides a likely biological progression underlying a microarray dataset and, perhaps more importantly, the candidate genes that regulate that progression.     

An improved method for scoring protein-protein interactions using semantic similarity within the gene ontology

Background  

Semantic similarity measures are useful to assess the physiological relevance of protein-protein interactions (PPIs). They quantify similarity between proteins based on their function using annotation systems like the Gene Ontology (GO). Proteins that interact in the cell are likely to be in similar locations or involved in similar biological processes compared to proteins that do not interact. Thus the more semantically similar the gene function annotations are among the interacting proteins, more likely the interaction is physiologically relevant. However, most semantic similarity measures used for PPI confidence assessment do not consider the unequal depth of term hierarchies in different classes of cellular location, molecular function, and biological process ontologies of GO and thus may over-or under-estimate similarity.     

Duplication models for biological networks.

Are biological networks different from other large complex networks? Both large biological and nonbiological networks exhibit power-law graphs (number of nodes with degree k, N(k) approximately k(-beta)), yet the exponents, beta, fall into different ranges. This may be because duplication of the information in the genome is a dominant evolutionary force in shaping biological networks (like gene regulatory networks and protein-protein interaction networks) and is fundamentally different from the mechanisms thought to dominate the growth of most nonbiological networks (such as the Internet). The preferential choice models used for nonbiological networks like web graphs can only produce power-law graphs with exponents greater than 2. We use combinatorial probabilistic methods to examine the evolution of graphs by node duplication processes and derive exact analytical relationships between the exponent of the power law and the parameters of the model. Both full duplication of nodes (with all their connections) as well as partial duplication (with only some connections) are analyzed. We demonstrate that partial duplication can produce power-law graphs with exponents less than 2, consistent with current data on biological networks. The power-law exponent for large graphs depends only on the growth process, not on the starting graph.     

基因工程微生物的环境监测及生物防御体系研究进展

随着生物技术的发展, 研究人员构建出了大量具有特定功能的基因工程微生物, 这些基因工程微生物在实际应用时常受到限制, 因为它们释放到环境中有可能带来新的污染。为了减少或消除其对环境的潜在危害, 有必要采取措施对这些基因工程微生物进行监测和安全控制。通常要求这类基因工程微生物带有便于监测的检测标记以及能进行自消亡的主动生物防御体系。对基因工程微生物的检测标记以及主动生物防御体系的研究现状进行了综述。     

同源重组法构建多功能农药降解基因工程菌研究

构建遗传稳定的多功能农药降解基因工程菌可以为农药污染的生物修复提供良好的菌种资源,然而,构建遗传稳定且不带入外源抗性的基因工程菌是一个难点。通过以受体菌的16S rDNA为同源重组指导序列、sacB基因为双交换正筛选标记构建同源重组载体,二亲结合的方法将甲基对硫磷水解酶基因（mpd）整合到呋喃丹降解菌Sphingomonas sp.CDS1染色体的16S rDNA位点,分别成功构建了含1个和2个mpd基因插入到rDNA位点且不带入外源抗性的基因工程菌株CDSmpd和CDS-2mpd。同源重组单交换的效率为3.7×10^－7～6.8×10^－7。通过PCR和Southern杂交的方法验证了同源重组事件。基因工程菌遗传稳定,能同时降解甲基对硫磷和呋喃丹。甲基对硫磷水解酶（MPH）的比活在各生长时期均高于原始出发菌株,比活最高达6.22 mu/μg。     

Use of green fluorescent protein to monitor survival of genetically engineered bacteria in aquatic environments.

Many methods for detecting model genetically engineered microorganisms (GEMs) in experimental ecosystems rely on cultivation of introduced cells. In this study, survival of Escherichia coli was monitored with the green fluorescent protein (GFP) gene. This approach allowed enumeration of GEMs by both plating and microscopy. Use of the GFP-marked GEMs revealed that E. coli persisted in stream water at higher densities as determined microscopically than as determined by CFU enumeration. The GFP gene did not negatively impact the fitness of the host strain.     

基因工程微生物的环境监测及生物防御体系研究进展

随着生物技术的发展, 研究人员构建出了大量具有特定功能的基因工程微生物, 这些基因工程微生物在实际应用时常受到限制, 因为它们释放到环境中有可能带来新的污染。为了减少或消除其对环境的潜在危害, 有必要采取措施对这些基因工程微生物进行监测和安全控制。通常要求这类基因工程微生物带有便于监测的检测标记以及能进行自消亡的主动生物防御体系。对基因工程微生物的检测标记以及主动生物防御体系的研究现状进行了综述。     

Identifying the biologically relevant gene categories based on gene expression and biological data: an example on prostate cancer

MOTIVATION: Most gene-expression based studies aim to identify genes with the capability of distinguishing different phenotypes. Although analysis at the genomic level is important, results of the molecular/cellular level are essential for understanding biological mechanisms. To deliver molecular/cellular-level results, a two-stage scheme is widely employed. This scheme just evaluates biological processes/molecular activities individually, totally overlooking the relationship between processes/activities. This treatment conflicts with the fact that most biological processes/molecular activities do not work alone. In order to deliver improved results, this shortcoming should be addressed. RESULTS: We design a selection model from a novel perspective to directly detect important gene functional categories (each category represents a cellular process or a molecular activity). More importantly, the correlations between gene categories are considered. Contributed by this capability, the proposed method shows its advantages over others. AVAILABILITY: the source code in Matlab is accessible via http://www.ee.cityu.edu.hk/~twschow/category_selection/category_selection.htm     

Identifying functional gene sets from hierarchically clustered expression data: map of abiotic stress regulated genes in Arabidopsis thaliana

We present MultiGO, a web-enabled tool for the identification of biologically relevant gene sets from hierarchically clustered gene expression trees (http://ekhidna.biocenter.helsinki.fi/poxo/multigo). High-throughput gene expression measuring techniques, such as microarrays, are nowadays often used to monitor the expression of thousands of genes. Since these experiments can produce overwhelming amounts of data, computational methods that assist the data analysis and interpretation are essential. MultiGO is a tool that automatically extracts the biological information for multiple clusters and determines their biological relevance, and hence facilitates the interpretation of the data. Since the entire expression tree is analysed, MultiGO is guaranteed to report all clusters that share a common enriched biological function, as defined by Gene Ontology annotations. The tool also identifies a plausible cluster set, which represents the key biological functions affected by the experiment. The performance is demonstrated by analysing drought-, cold- and abscisic acid-related expression data sets from Arabidopsis thaliana. The analysis not only identified known biological functions, but also brought into focus the less established connections to defense-related gene clusters. Thus, in comparison to analyses of manually selected gene lists, the systematic analysis of every cluster can reveal unexpected biological phenomena and produce much more comprehensive biological insights to the experiment of interest.