Sex determination in insects: a binary decision based on alternative splicing

The gene regulatory networks that control sex determination vary between species. Despite these differences, comparative studies in insects have found that alternative splicing is reiteratively used in evolution to control expression of the key sex-determining genes. Sex determination is best understood in Drosophila where activation of the RNA binding protein-encoding gene Sex-lethal is the central female-determining event. Sex-lethal serves as a genetic switch because once activated it controls its own expression by a positive feedback splicing mechanism. Sex fate choice in is also maintained by self-sustaining positive feedback splicing mechanisms in other dipteran and hymenopteran insects, although different RNA binding protein-encoding genes function as the binary switch. Studies exploring the mechanisms of sex-specific splicing have revealed the extent to which sex determination is integrated with other developmental regulatory networks.     

Recurrent adaptation in RNA interference genes across the Drosophila phylogeny

RNA interference (RNAi) is quickly emerging as a vital component of genome organization, gene regulation, and immunity in Drosophila and other species. Previous studies have suggested that, as a whole, genes involved in RNAi are under intense positive selection in Drosophila melanogaster. Here, we characterize the extent and patterns of adaptive evolution in 23 known Drosophila RNAi genes, both within D. melanogaster and across the Drosophila phylogeny. We find strong evidence for recurrent protein-coding adaptation at a large number of RNAi genes, particularly those involved in antiviral immunity and defense against transposable elements. We identify specific functional domains involved in direct protein-RNA interactions as particular hotspots of recurrent adaptation in multiple RNAi genes, suggesting that targeted coadaptive arms races may be a general feature of RNAi evolution. Our observations suggest a predictive model of how selective pressures generated by evolutionary arms race scenarios may affect multiple genes across protein interaction networks and other biochemical pathways.     

Efficient reverse-engineering of a developmental gene regulatory network

Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to discover whether there are rules or regularities governing development and evolution of complex multi-cellular organisms.     

Efficient parsimony-based methods for phylogenetic network reconstruction

MOTIVATION: Phylogenies--the evolutionary histories of groups of organisms-play a major role in representing relationships among biological entities. Although many biological processes can be effectively modeled as tree-like relationships, others, such as hybrid speciation and horizontal gene transfer (HGT), result in networks, rather than trees, of relationships. Hybrid speciation is a significant evolutionary mechanism in plants, fish and other groups of species. HGT plays a major role in bacterial genome diversification and is a significant mechanism by which bacteria develop resistance to antibiotics. Maximum parsimony is one of the most commonly used criteria for phylogenetic tree inference. Roughly speaking, inference based on this criterion seeks the tree that minimizes the amount of evolution. In 1990, Jotun Hein proposed using this criterion for inferring the evolution of sequences subject to recombination. Preliminary results on small synthetic datasets. Nakhleh et al. (2005) demonstrated the criterion's application to phylogenetic network reconstruction in general and HGT detection in particular. However, the naive algorithms used by the authors are inapplicable to large datasets due to their demanding computational requirements. Further, no rigorous theoretical analysis of computing the criterion was given, nor was it tested on biological data. RESULTS: In the present work we prove that the problem of scoring the parsimony of a phylogenetic network is NP-hard and provide an improved fixed parameter tractable algorithm for it. Further, we devise efficient heuristics for parsimony-based reconstruction of phylogenetic networks. We test our methods on both synthetic and biological data (rbcL gene in bacteria) and obtain very promising results.     

How the Dynamics and Structure of Sexual Contact Networks Shape Pathogen Phylogenies

The characteristics of the host contact network over which a pathogen is transmitted affect both epidemic spread and the projected effectiveness of control strategies. Given the importance of understanding these contact networks, it is unfortunate that they are very difficult to measure directly. This challenge has led to an interest in methods to infer information about host contact networks from pathogen phylogenies, because in shaping a pathogen''s opportunities for reproduction, contact networks also shape pathogen evolution. Host networks influence pathogen phylogenies both directly, through governing opportunities for evolution, and indirectly by changing the prevalence and incidence. Here, we aim to separate these two effects by comparing pathogen evolution on different host networks that share similar epidemic trajectories. This approach allows use to examine the direct effects of network structure on pathogen phylogenies, largely controlling for confounding differences arising from population dynamics. We find that networks with more heterogeneous degree distributions yield pathogen phylogenies with more variable cluster numbers, smaller mean cluster sizes, shorter mean branch lengths, and somewhat higher tree imbalance than networks with relatively homogeneous degree distributions. However, in particular for dynamic networks, we find that these direct effects are relatively modest. These findings suggest that the role of the epidemic trajectory, the dynamics of the network and the inherent variability of metrics such as cluster size must each be taken into account when trying to use pathogen phylogenies to understand characteristics about the underlying host contact network.     

Patterning the dorsal–ventral axis of the wasp Nasonia vitripennis

Regulatory networks composed of interacting genes are responsible for pattern formation and cell type specification in a wide variety of developmental contexts. Evolution must act on these regulatory networks in order to change the proportions, distribution, and characteristics of specified cells. Thus, understanding how these networks operate in homologous systems across multiple levels of phylogenetic divergence is critical for understanding the evolution of developmental systems. Among the most thoroughly characterized regulatory networks is the dorsal–ventral patterning system of the fly Drosophila melanogaster. Due to the thorough understanding of this system, it is an ideal starting point for comparative analyses. Here we report an analysis of the DV patterning system of the wasp, Nasonia vitripennis. This wasp undergoes a mode of long germ embryogenesis that is superficially nearly identical to that of Drosophila, but one that was likely independently derived. We have found that while the expression of genes just prior to the onset of gastrulation is almost identical in Nasonia and Drosophila, both the upstream network responsible for generating this pattern, and the downstream morphogenetic movements that it sets in motion, are significantly diverged. From this we conclude that many network structures are available to evolution to achieve particular developmental ends.     

Inferring general relations between network characteristics from specific network ensembles

Different network models have been suggested for the topology underlying complex interactions in natural systems. These models are aimed at replicating specific statistical features encountered in real-world networks. However, it is rarely considered to which degree the results obtained for one particular network class can be extrapolated to real-world networks. We address this issue by comparing different classical and more recently developed network models with respect to their ability to generate networks with large structural variability. In particular, we consider the statistical constraints which the respective construction scheme imposes on the generated networks. After having identified the most variable networks, we address the issue of which constraints are common to all network classes and are thus suitable candidates for being generic statistical laws of complex networks. In fact, we find that generic, not model-related dependencies between different network characteristics do exist. This makes it possible to infer global features from local ones using regression models trained on networks with high generalization power. Our results confirm and extend previous findings regarding the synchronization properties of neural networks. Our method seems especially relevant for large networks, which are difficult to map completely, like the neural networks in the brain. The structure of such large networks cannot be fully sampled with the present technology. Our approach provides a method to estimate global properties of under-sampled networks in good approximation. Finally, we demonstrate on three different data sets (C. elegans neuronal network, R. prowazekii metabolic network, and a network of synonyms extracted from Roget's Thesaurus) that real-world networks have statistical relations compatible with those obtained using regression models.     

Application of modular response analysis to medium- to large-size biological systems

The development of high-throughput genomic technologies associated with recent genetic perturbation techniques such as short hairpin RNA (shRNA), gene trapping, or gene editing (CRISPR/Cas9) has made it possible to obtain large perturbation data sets. These data sets are invaluable sources of information regarding the function of genes, and they offer unique opportunities to reverse engineer gene regulatory networks in specific cell types. Modular response analysis (MRA) is a well-accepted mathematical modeling method that is precisely aimed at such network inference tasks, but its use has been limited to rather small biological systems so far. In this study, we show that MRA can be employed on large systems with almost 1,000 network components. In particular, we show that MRA performance surpasses general-purpose mutual information-based algorithms. Part of these competitive results was obtained by the application of a novel heuristic that pruned MRA-inferred interactions a posteriori. We also exploited a block structure in MRA linear algebra to parallelize large system resolutions.     

Is My Network Module Preserved and Reproducible?

In many applications, one is interested in determining which of the properties of a network module change across conditions. For example, to validate the existence of a module, it is desirable to show that it is reproducible (or preserved) in an independent test network. Here we study several types of network preservation statistics that do not require a module assignment in the test network. We distinguish network preservation statistics by the type of the underlying network. Some preservation statistics are defined for a general network (defined by an adjacency matrix) while others are only defined for a correlation network (constructed on the basis of pairwise correlations between numeric variables). Our applications show that the correlation structure facilitates the definition of particularly powerful module preservation statistics. We illustrate that evaluating module preservation is in general different from evaluating cluster preservation. We find that it is advantageous to aggregate multiple preservation statistics into summary preservation statistics. We illustrate the use of these methods in six gene co-expression network applications including 1) preservation of cholesterol biosynthesis pathway in mouse tissues, 2) comparison of human and chimpanzee brain networks, 3) preservation of selected KEGG pathways between human and chimpanzee brain networks, 4) sex differences in human cortical networks, 5) sex differences in mouse liver networks. While we find no evidence for sex specific modules in human cortical networks, we find that several human cortical modules are less preserved in chimpanzees. In particular, apoptosis genes are differentially co-expressed between humans and chimpanzees. Our simulation studies and applications show that module preservation statistics are useful for studying differences between the modular structure of networks. Data, R software and accompanying tutorials can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/ModulePreservation.     

Phylogenetic Networks that Display a Tree Twice

In the last decade, the use of phylogenetic networks to analyze the evolution of species whose past is likely to include reticulation events, such as horizontal gene transfer or hybridization, has gained popularity among evolutionary biologists. Nevertheless, the evolution of a particular gene can generally be described without reticulation events and therefore be represented by a phylogenetic tree. While this is not in contrast to each other, it places emphasis on the necessity of algorithms that analyze and summarize the tree-like information that is contained in a phylogenetic network. We contribute to the toolbox of such algorithms by investigating the question of whether or not a phylogenetic network embeds a tree twice and give a quadratic-time algorithm to solve this problem for a class of networks that is more general than tree-child networks.     

Neutral evolution of the sex-determining gene transformer in Drosophila

The amino acid sequence of the transformer (tra) gene exhibits an extremely rapid rate of evolution among Drosophila species, although the gene performs a critical step in sex determination. These changes in amino acid sequence are the result of either natural selection or neutral evolution. To differentiate between selective and neutral causes of this evolutionary change, analyses of both intraspecific and interspecific patterns of molecular evolution of tra gene sequences are presented. Sequences of 31 tra alleles were obtained from Drosophila americana. Many replacement and silent nucleotide variants are present among the alleles; however, the distribution of this sequence variation is consistent with neutral evolution. Sequence evolution was also examined among six species representative of the genus Drosophila. For most lineages and most regions of the gene, both silent and replacement substitutions have accumulated in a constant, clock-like manner. In exon 3 of D. virilis and D. americana we find evidence for an elevated rate of nonsynonymous substitution, but no statistical support for a greater rate of nonsynonymous relative to synonymous substitutions. Both levels of analysis of the tra sequence suggest that, although the gene is evolving at a rapid pace, these changes are neutral in function.     

Retrogenes Moved Out of the Z Chromosome in the Silkworm

Previous studies on organisms with well-differentiated X and Y chromosomes, such as Drosophila and mammals, consistently detected an excess of genes moving out of the X chromosome and gaining testis-biased expression. Several selective evolutionary mechanisms were shown to be associated with this nonrandom gene traffic, which contributed to the evolution of the X chromosome and autosomes. If selection drives gene traffic, such traffic should also exist in species with Z and W chromosomes, where the females are the heterogametic sex. However, no previous studies on gene traffic in species with female heterogamety have found any nonrandom chromosomal gene movement. Here, we report an excess of retrogenes moving out of the Z chromosome in an organism with the ZW sex determination system, Bombyx mori. In addition, we showed that those "out of Z" retrogenes tended to have ovary-biased expression, which is consistent with the pattern of non-retrogene traffic recently reported in birds and symmetrical to the retrogene movement in mammals and fruit flies out of the X chromosome evolving testis functions. These properties of gene traffic in the ZW system suggest a general role for the heterogamety of sex chromosomes in determining the chromosomal locations and the evolution of sex-biased genes.     

基因逻辑网络研究进展

海量生物数据的涌现,使得通过数据分析和理论方法探索生物机理成为理论生物学研究的重要途径．特别是对于基因的复杂的功能系统,建立基因网络这种理论方法的意义更为突出．Bowers在蛋白质相互作用的分析中引入了高阶逻辑关系,从而建立了系统发生谱数据的逻辑分析（LAPP）的系统方法．LAPP和通常建立模型的方法不同,它给出了一个从复杂网络的元素（或部件）的表达数据出发,通过逻辑分析,找到元素之间逻辑关联性的建模方法．这种方法能够从蛋白质表达谱数据出发,利用信息熵的算法发现两种蛋白质对一种蛋白质的联合作用,对于发现蛋白质之间新的作用机理有重要意义．由于涉及功能的基因组通常是一个大的群体构成的系统,因此LAPP方法也是一个生成复杂的基因逻辑网络的方法．基因逻辑网络的建立,方便实现通过逻辑调控进行基因调控的目的．这种方法可以应用在很多方面,如物种进化、肿瘤诊疗等等．系统阐述并分析了LAPP方法,并指出其在方法和应用方面的新进展以及评述．     

Population structure promotes the evolution of costly sex in artificial gene networks

We build on previous observations that Hill–Robertson interference generates an advantage of sex that, in structured populations, can be large enough to explain the evolutionary maintenance of costly sex. We employed a gene network model that explicitly incorporates interactions between genes. Mutations in the gene networks have variable effects that depend on the genetic background in which they appear. Consequently, our simulations include two costs of sex—recombination and migration loads—that were missing from previous studies of the evolution of costly sex. Our results suggest a critical role for population structure that lies in its ability to align the long‐ and short‐term advantages of sex. We show that the addition of population structure favored the evolution of sex by disproportionately decreasing the equilibrium mean fitness of asexual populations, primarily by increasing the strength of Muller's Ratchet. Population structure also increased the ability of the short‐term advantage of sex to counter the primary limit to the evolution of sex in the gene network model—recombination load. On the other hand, highly structured populations experienced migration load in the form of Dobzhansky–Muller incompatibilities, decreasing the effective rate of migration between demes and, consequently, accelerating the accumulation of drift load in the sexual populations.     

Gene duplications, robustness and evolutionary innovations

Mutational robustness facilitates evolutionary innovations. Gene duplications are unique kinds of mutations, in that they generally increase such robustness. The frequent association of gene duplications in regulatory networks with evolutionary innovation is thus a special case of a general mechanism linking innovation to robustness. The potential power of this mechanism to promote evolutionary innovations on large time scales is illustrated here with several examples. These include the role of gene duplications in the vertebrate radiation, flowering plant evolution and heart development, which encompass some of the most striking innovations in the evolution of life.