Long-Term and Short-Term Evolutionary Impacts of Transposable Elements on Drosophila

Transposable elements (TEs) are considered to be genomic parasites and their interactions with their hosts have been likened to the coevolution between host and other nongenomic, horizontally transferred pathogens. TE families, however, are vertically inherited as integral segments of the nuclear genome. This transmission strategy has been suggested to weaken the selective benefits of host alleles repressing the transposition of specific TE variants. On the other hand, the elevated rates of TE transposition and high incidences of deleterious mutations observed during the rare cases of horizontal transfers of TE families between species could create at least a transient process analogous to the influence of horizontally transmitted pathogens. Here, we formally address this analogy, using empirical and theoretical analysis to specify the mechanism of how host–TE interactions may drive the evolution of host genes. We found that host TE-interacting genes actually have more pervasive evidence of adaptive evolution than immunity genes that interact with nongenomic pathogens in Drosophila. Yet, both our theoretical modeling and empirical observations comparing Drosophila melanogaster populations before and after the horizontal transfer of P elements, which invaded D. melanogaster early last century, demonstrated that horizontally transferred TEs have only a limited influence on host TE-interacting genes. We propose that the more prevalent and constant interaction with multiple vertically transmitted TE families may instead be the main force driving the fast evolution of TE-interacting genes, which is fundamentally different from the gene-for-gene interaction of host–pathogen coevolution.     

Identification and categorization of horizontally transferred genes in prokaryotic genomes

Horizontal gene transfer （HGT）, a process through which genomes acquire genetic materials from distantly related organisms, is believed to be one of the major forces in prokaryotic genome evolution.However, systematic investigation is still scarce to clarify two basic issues about HGT： （1） what types of genes are transferred; and （2） what influence HGT events over the organization and evolution of biological pathways. Genome-scale investigations of these two issues will advance the systematical understanding of HGT in the context of prokaryotic genome evolution. Having investigated 82 genomes, we constructed an HGT database across broad evolutionary timescales. We identified four function categories containing a high proportion of horizontally transferred genes： cell envelope, energy metabolism, regulatory functions, and transport/binding proteins. Such biased function distribution indicates that HGT is not completely random;instead, it is under high selective pressure, required by function restraints in organisms. Furthermore, we mapped the transferred genes onto the connectivity structure map of organism-specific pathways listed in Kyoto Encyclopedia of Genes and Genomes （KEGG）. Our results suggest that recruitment of transferred genes into pathways is also selectively constrained because of the tuned interaction between original pathway members. Pathway organization structures still conserve well through evolution even with the recruitment of horizontally transferred genes. Interestingly, in pathways whose organization were significantly affected by HGT events, the operon-like arrangement of transferred genes was found to be prevalent. Such results suggest that operon plays an essential and directional role in the integration of alien genes into pathways.     

Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data

MOTIVATION: Biological processes in cells are properly performed by gene regulations, signal transductions and interactions between proteins. To understand such molecular networks, we propose a statistical method to estimate gene regulatory networks and protein-protein interaction networks simultaneously from DNA microarray data, protein-protein interaction data and other genome-wide data. RESULTS: We unify Bayesian networks and Markov networks for estimating gene regulatory networks and protein-protein interaction networks according to the reliability of each biological information source. Through the simultaneous construction of gene regulatory networks and protein-protein interaction networks of Saccharomyces cerevisiae cell cycle, we predict the role of several genes whose functions are currently unknown. By using our probabilistic model, we can detect false positives of high-throughput data, such as yeast two-hybrid data. In a genome-wide experiment, we find possible gene regulatory relationships and protein-protein interactions between large protein complexes that underlie complex regulatory mechanisms of biological processes.     

Discovering regulatory and signalling circuits in molecular interaction networks

MOTIVATION: In model organisms such as yeast, large databases of protein-protein and protein-DNA interactions have become an extremely important resource for the study of protein function, evolution, and gene regulatory dynamics. In this paper we demonstrate that by integrating these interactions with widely-available mRNA expression data, it is possible to generate concrete hypotheses for the underlying mechanisms governing the observed changes in gene expression. To perform this integration systematically and at large scale, we introduce an approach for screening a molecular interaction network to identify active subnetworks, i.e., connected regions of the network that show significant changes in expression over particular subsets of conditions. The method we present here combines a rigorous statistical measure for scoring subnetworks with a search algorithm for identifying subnetworks with high score. RESULTS: We evaluated our procedure on a small network of 332 genes and 362 interactions and a large network of 4160 genes containing all 7462 protein-protein and protein-DNA interactions in the yeast public databases. In the case of the small network, we identified five significant subnetworks that covered 41 out of 77 (53%) of all significant changes in expression. Both network analyses returned several top-scoring subnetworks with good correspondence to known regulatory mechanisms in the literature. These results demonstrate how large-scale genomic approaches may be used to uncover signalling and regulatory pathways in a systematic, integrative fashion.     

基于组学数据构建基因调控网络

在生命体内,基因以及其它分子间相互作用形成复杂调控网络,生命过程都是以调控网络的形式存在,如从代谢通路网络到转录调控网络,从信号转导网络到蛋白质相互作用网络等等。因此,网络现象是生命现象的复杂本质和主要特征。本文系统地介绍了基于表达谱数据构建基因调控网络的布尔网络模型,线性模型,微分方程模型和贝叶斯网络模型,并对各种网络构建模型进行了深入的分析和总结。同时,文章从基因组序列信息、蛋白质相互作用信息和生物医学文献信息等方面讨论了基因调控网络方面构建的研究,这对从系统生物学水平揭示生命复杂机制具有重要的参考价值。     

The effects of network neighbours on protein evolution

Interacting proteins may often experience similar selection pressures. Thus, we may expect that neighbouring proteins in biological interaction networks evolve at similar rates. This has been previously shown for protein-protein interaction networks. Similarly, we find correlated rates of evolution of neighbours in networks based on co-expression, metabolism, and synthetic lethal genetic interactions. While the correlations are statistically significant, their magnitude is small, with network effects explaining only between 2% and 7% of the variation. The strongest known predictor of the rate of protein evolution remains expression level. We confirmed the previous observation that similar expression levels of neighbours indeed explain their similar evolution rates in protein-protein networks, and showed that the same is true for metabolic networks. In co-expression and synthetic lethal genetic interaction networks, however, neighbouring genes still show somewhat similar evolutionary rates even after simultaneously controlling for expression level, gene essentiality and gene length. Thus, similar expression levels and related functions (as inferred from co-expression and synthetic lethal interactions) seem to explain correlated evolutionary rates of network neighbours across all currently available types of biological networks.     

Global approaches to protein-protein interactions

The availability of complete, annotated genome sequences for a variety of eukaryotic organisms has paved the way for a paradigm shift in biomedical research from the 'one gene-one hypothesis' approach to more global, systematic strategies that analyse genes or proteins on a genome- and proteome-wide scale. One daunting task in the post-genome era is to determine how the complement of expressed cellular proteins - the proteome - is organised into functional, higher-order networks, by mapping all constitutive and dynamic protein-protein interactions. Traditionally, reductionist approaches have typically focused on a few, selected gene products and their interactions in a particular physiological context. In contrast, more holistic strategies aim at understanding complex biological systems, for example global protein-protein interaction networks on a cellular or organismal level. Several large-scale proteomics technologies have been developed to generate comprehensive, cellular protein-protein interaction maps.     

Semantic integration to identify overlapping functional modules in protein interaction networks

Background  

The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms.     

The transferome of metabolic genes explored: analysis of the horizontal transfer of enzyme encoding genes in unicellular eukaryotes

Background  

Metabolic networks are responsible for many essential cellular processes, and exhibit a high level of evolutionary conservation from bacteria to eukaryotes. If genes encoding metabolic enzymes are horizontally transferred and are advantageous, they are likely to become fixed. Horizontal gene transfer (HGT) has played a key role in prokaryotic evolution and its importance in eukaryotes is increasingly evident. High levels of endosymbiotic gene transfer (EGT) accompanied the establishment of plastids and mitochondria, and more recent events have allowed further acquisition of bacterial genes. Here, we present the first comprehensive multi-species analysis of E/HGT of genes encoding metabolic enzymes from bacteria to unicellular eukaryotes.     

Using indirect protein-protein interactions for protein complex prediction

Protein complexes are fundamental for understanding principles of cellular organizations. As the sizes of protein-protein interaction (PPI) networks are increasing, accurate and fast protein complex prediction from these PPI networks can serve as a guide for biological experiments to discover novel protein complexes. However, it is not easy to predict protein complexes from PPI networks, especially in situations where the PPI network is noisy and still incomplete. Here, we study the use of indirect interactions between level-2 neighbors (level-2 interactions) for protein complex prediction. We know from previous work that proteins which do not interact but share interaction partners (level-2 neighbors) often share biological functions. We have proposed a method in which all direct and indirect interactions are first weighted using topological weight (FS-Weight), which estimates the strength of functional association. Interactions with low weight are removed from the network, while level-2 interactions with high weight are introduced into the interaction network. Existing clustering algorithms can then be applied to this modified network. We have also proposed a novel algorithm that searches for cliques in the modified network, and merge cliques to form clusters using a "partial clique merging" method. Experiments show that (1) the use of indirect interactions and topological weight to augment protein-protein interactions can be used to improve the precision of clusters predicted by various existing clustering algorithms; and (2) our complex-finding algorithm performs very well on interaction networks modified in this way. Since no other information except the original PPI network is used, our approach would be very useful for protein complex prediction, especially for prediction of novel protein complexes.     

基于MADS-box诱饵与蛋白质相互作用的拟南芥花瓣发育分子网络拓展

阐明花器官发育调控机理具重要的进化、发育和生态学意义。该文以拟南芥(Arabidopsis thaliana)花瓣发育为例, 整合蛋白质互作、亚细胞定位、基因芯片和基因功能注释等数据库, 通过组建蛋白质互作可信预测模型, 获得拟南芥花瓣蛋白质互作网络, 以含有MADS-box结构域蛋白为诱饵在网络中进行一级拓展, 得到含38个蛋白质和67对互作的拓展网络。基于拓展网络, DAVID基因功能注释表明, 多数蛋白质涉及的生物学过程与花发育调控相关; 提取到19个候选四元互作, 涉及ABCDE模型基因之外的8个基因, 其中含MADS-box结构域的AGL16可能是B类基因新成员或其冗余; SEU、LUH、CHR4、CHR11、CHR17和AT3G04960为拟南芥花瓣AP1-AP3-PI-SEP四聚体的候选靶标基因。研究结果为深入解析拟南芥花瓣发育分子调控网络奠定了基础。     

Divergence pattern of duplicate genes in protein-protein interactions follows the power law

The impact of the biological network structures on the divergence between the two copies of one duplicate gene pair involved in the networks has not been documented on a genome scale. Having analyzed the most recently updated Database of Interacting Proteins (DIP) by incorporating the information for duplicate genes of the same age in yeast, we find that there was a highly significantly positive correlation between the level of connectivity of ancient genes and the number of shared partners of their duplicates in the protein-protein interaction networks. This suggests that duplicate genes with a low ancestral connectivity tend to provide raw materials for functional novelty, whereas those duplicate genes with a high ancestral connectivity tend to create functional redundancy for a genome during the same evolutionary period. Moreover, the difference in the number of partners between two copies of a duplicate pair was found to follow a power-law distribution. This suggests that loss and gain of interacting partners for most duplicate genes with a lower level of ancestral connectivity is largely symmetrical, whereas the "hub duplicate genes" with a higher level of ancient connectivity display an asymmetrical divergence pattern in protein-protein interactions. Thus, it is clear that the protein-protein interaction network structures affect the divergence pattern of duplicate genes. Our findings also provide insights into the origin and development of biological networks.     

Constructing structural networks of signaling pathways on the proteome scale

Proteins function through their interactions, and the availability of protein interaction networks could help in understanding cellular processes. However, the known structural data are limited and the classical network node-and-edge representation, where proteins are nodes and interactions are edges, shows only which proteins interact; not how they interact. Structural networks provide this information. Protein-protein interface structures can also indicate which binding partners can interact simultaneously and which are competitive, and can help forecasting potentially harmful drug side effects. Here, we use a powerful protein-protein interactions prediction tool which is able to carry out accurate predictions on the proteome scale to construct the structural network of the extracellular signal-regulated kinases (ERK) in the mitogen-activated protein kinase (MAPK) signaling pathway. This knowledge-based method, PRISM, is motif-based, and is combined with flexible refinement and energy scoring. PRISM predicts protein interactions based on structural and evolutionary similarity to known protein interfaces.