Actin Genes in the Mediterranean Fruit Fly, Ceratitis Capitata

We have undertaken the study of actin gene organization and expression in the genome of the Mediterranean fruit fly (medfly), Ceratitis capitata. Actin genes have been extensively characterized previously in a wide range of eukaryotic organisms, and they have valuable properties for comparative studies. These genes are typically highly conserved in coding regions, represented in multiple copies per genome and regulated in expression during development. We have isolated a gene in the medfly using the cloned Drosophila melanogaster 5C actin gene as a probe. This medfly gene detects abundant messages present during late larval and late pupal development as well as in thoracic and leg tissue preparations from newly emerged adults. This pattern of expression is consistent with what has been seen for actin genes in other organisms. Using either the D. melanogaster 5C actin gene or the medfly gene as a probe identifies five common cross reacting EcoRI fragments in genomic DNA, but only under less than fully stringent hybridization conditions.     

Dynamics of Social Behavior in Fruit Fly Larvae

We quantified the extent and dynamics of social interactions among fruit fly larvae over time. Both a wild-type laboratory population and a recently-caught strain of larvae spontaneously formed social foraging groups. Levels of aggregation initially increased during larval development and then declined with the wandering stage before pupation. We show that larvae aggregated more on hard than soft food, and more at sites where we had previously broken the surface of the food. Groups of larvae initiated burrowing sooner than solitary individuals, indicating that one potential benefit of larval aggregations is an improved ability to dig and burrow into the food substrate. We also show that two closely related species, D. melanogaster and D. simulans, differ in their tendency to aggregate, which may reflect different evolutionary histories. Our protocol for quantifying social behavior in larvae uncovered robust social aggregations in this simple model, which is highly amenable to neurogenetic analyses, and can serve for future research into the mechanisms and evolution of social behavior.     

Phenotypic Heterogeneity and the Evolution of Bacterial Life Cycles

Most bacteria live in colonies, where they often express different cell types. The ecological significance of these cell types and their evolutionary origin are often unknown. Here, we study the evolution of cell differentiation in the context of surface colonization. We particularly focus on the evolution of a ‘sticky’ cell type that is required for surface attachment, but is costly to express. The sticky cells not only facilitate their own attachment, but also that of non-sticky cells. Using individual-based simulations, we show that surface colonization rapidly evolves and in most cases leads to phenotypic heterogeneity, in which sticky and non-sticky cells occur side by side on the surface. In the presence of regulation, cell differentiation leads to a remarkable set of bacterial life cycles, in which cells alternate between living in the liquid and living on the surface. The dominant life stage is formed by the surface-attached colony that shows many complex features: colonies reproduce via fission and by producing migratory propagules; cells inside the colony divide labour; and colonies can produce filaments to facilitate expansion. Overall, our model illustrates how the evolution of an adhesive cell type goes hand in hand with the evolution of complex bacterial life cycles.     

Independently Controlled Wing Stroke Patterns in the Fruit Fly Drosophila melanogaster

Flies achieve supreme flight maneuverability through a small set of miniscule steering muscles attached to the wing base. The fast flight maneuvers arise from precisely timed activation of the steering muscles and the resulting subtle modulation of the wing stroke. In addition, slower modulation of wing kinematics arises from changes in the activity of indirect flight muscles in the thorax. We investigated if these modulations can be described as a superposition of a limited number of elementary deformations of the wing stroke that are under independent physiological control. Using a high-speed computer vision system, we recorded the wing motion of tethered flying fruit flies for up to 12 000 consecutive wing strokes at a sampling rate of 6250 Hz. We then decomposed the joint motion pattern of both wings into components that had the minimal mutual information (a measure of statistical dependence). In 100 flight segments measured from 10 individual flies, we identified 7 distinct types of frequently occurring least-dependent components, each defining a kinematic pattern (a specific deformation of the wing stroke and the sequence of its activation from cycle to cycle). Two of these stroke deformations can be associated with the control of yaw torque and total flight force, respectively. A third deformation involves a change in the downstroke-to-upstroke duration ratio, which is expected to alter the pitch torque. A fourth kinematic pattern consists in the alteration of stroke amplitude with a period of 2 wingbeat cycles, extending for dozens of cycles. Our analysis indicates that these four elementary kinematic patterns can be activated mutually independently, and occur both in isolation and in linear superposition. The results strengthen the available evidence for independent control of yaw torque, pitch torque, and total flight force. Our computational method facilitates systematic identification of novel patterns in large kinematic datasets.     

Noninvasive Analysis of Microbiome Dynamics in the Fruit Fly Drosophila melanogaster

The diversity and structure of the intestinal microbial community has a strong influence on life history. To understand how hosts and microbes interact, model organisms with comparatively simple microbial communities, such as the fruit fly (Drosophila melanogaster), offer key advantages. However, studies of the Drosophila microbiome are limited to a single point in time, because flies are typically sacrificed for DNA extraction. In order to test whether noninvasive approaches, such as sampling of fly feces, could be a means to assess fly-associated communities over time on the same cohort of flies, we compared the microbial communities of fly feces, dissected fly intestines, and whole flies across three different Drosophila strains. Bacterial species identified in either whole flies or isolated intestines were reproducibly found in feces samples. Although the bacterial communities of feces and intestinal samples were not identical, they shared similarities and obviously the same origin. In contrast to material from whole flies and intestines, feces samples were not compromised by Wolbachia spp. infections, which are widespread in laboratory and wild strains. In a proof-of-principle experiment, we showed that simple nutritional interventions, such as a high-fat diet or short-term starvation, had drastic and long-lasting effects on the micobiome. Thus, the analysis of feces can supplement the toolbox for microbiome studies in Drosophila, unleashing the full potential of such studies in time course experiments where multiple samples from single populations are obtained during aging, development, or experimental manipulations.     

Predicting Phenotypic Diversity and the Underlying Quantitative Molecular Transitions

During development, signaling networks control the formation of multicellular patterns. To what extent quantitative fluctuations in these complex networks may affect multicellular phenotype remains unclear. Here, we describe a computational approach to predict and analyze the phenotypic diversity that is accessible to a developmental signaling network. Applying this framework to vulval development in C. elegans, we demonstrate that quantitative changes in the regulatory network can render ~500 multicellular phenotypes. This phenotypic capacity is an order-of-magnitude below the theoretical upper limit for this system but yet is large enough to demonstrate that the system is not restricted to a select few outcomes. Using metrics to gauge the robustness of these phenotypes to parameter perturbations, we identify a select subset of novel phenotypes that are the most promising for experimental validation. In addition, our model calculations provide a layout of these phenotypes in network parameter space. Analyzing this landscape of multicellular phenotypes yielded two significant insights. First, we show that experimentally well-established mutant phenotypes may be rendered using non-canonical network perturbations. Second, we show that the predicted multicellular patterns include not only those observed in C. elegans, but also those occurring exclusively in other species of the Caenorhabditis genus. This result demonstrates that quantitative diversification of a common regulatory network is indeed demonstrably sufficient to generate the phenotypic differences observed across three major species within the Caenorhabditis genus. Using our computational framework, we systematically identify the quantitative changes that may have occurred in the regulatory network during the evolution of these species. Our model predictions show that significant phenotypic diversity may be sampled through quantitative variations in the regulatory network without overhauling the core network architecture. Furthermore, by comparing the predicted landscape of phenotypes to multicellular patterns that have been experimentally observed across multiple species, we systematically trace the quantitative regulatory changes that may have occurred during the evolution of the Caenorhabditis genus.     

Rates of Phenotypic and Genomic Evolution during the Cambrian Explosion

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Evolution and Phenotypic Selection of Cancer Stem Cells

Cells of different organs at different ages have an intrinsic set of kinetics that dictates their behavior. Transformation into cancer cells will inherit these kinetics that determine initial cell and tumor population progression dynamics. Subject to genetic mutation and epigenetic alterations, cancer cell kinetics can change, and favorable alterations that increase cellular fitness will manifest themselves and accelerate tumor progression. We set out to investigate the emerging intratumoral heterogeneity and to determine the evolutionary trajectories of the combination of cell-intrinsic kinetics that yield aggressive tumor growth. We develop a cellular automaton model that tracks the temporal evolution of the malignant subpopulation of so-called cancer stem cells(CSC), as these cells are exclusively able to initiate and sustain tumors. We explore orthogonal cell traits, including cell migration to facilitate invasion, spontaneous cell death due to genetic drift after accumulation of irreversible deleterious mutations, symmetric cancer stem cell division that increases the cancer stem cell pool, and telomere length and erosion as a mitotic counter for inherited non-stem cancer cell proliferation potential. Our study suggests that cell proliferation potential is the strongest modulator of tumor growth. Early increase in proliferation potential yields larger populations of non-stem cancer cells(CC) that compete with CSC and thus inhibit CSC division while a reduction in proliferation potential loosens such inhibition and facilitates frequent CSC division. The sub-population of cancer stem cells in itself becomes highly heterogeneous dictating population level dynamics that vary from long-term dormancy to aggressive progression. Our study suggests that the clonal diversity that is captured in single tumor biopsy samples represents only a small proportion of the total number of phenotypes.     

The NMDA Receptor Promotes Sleep in the Fruit Fly,Drosophila melanogaster

Considerable evidence indicates that sleep is essential for learning and memory. Drosophila melanogaster has emerged as a novel model for studying sleep. We previously found a short sleeper mutant, fumin (fmn), and identified its mutation in the dopamine transporter gene. We reported similarities in the molecular basis of sleep and arousal regulation between mammals and Drosophila. In aversive olfactory learning tasks, fmn mutants demonstrate defective memory retention, which suggests an association between sleep and memory. In an attempt to discover additional sleep related genes in Drosophila, we carried out a microarray analysis comparing mRNA expression in heads of fmn and control flies and found that 563 genes are differentially expressed. Next, using the pan-neuronal Gal4 driver elav-Gal4 and UAS-RNA interference (RNAi) to knockdown individual genes, we performed a functional screen. We found that knockdown of the NMDA type glutamate receptor channel gene (Nmdar1) (also known as dNR1) reduced sleep. The NMDA receptor (NMDAR) plays an important role in learning and memory both in Drosophila and mammals. The application of the NMDAR antagonist, MK-801, reduced sleep in control flies, but not in fmn. These results suggest that NMDAR promotes sleep regulation in Drosophila.     

The genetics of cuticular hydrocarbon profiles in the Fruit Fly Drosophila simulans

Female mate choice is one mechanism of sexual selection and, provided there is adequate genetic variation in the male traits that are the target of this selection, they will evolve via female choice. Cuticular hydrocarbons (CHCs) are important in Drosophila mate choice, but relatively little is known about the underlying genetic architecture of CHC profiles in Drosophila simulans. Here, we used gas chromatography-mass spectrometry to investigate patterns of genetic variation in the CHC profiles of male and female D. simulans using isofemale lines. We found substantial genetic variation for CHC profiles and individual CHC components, and individual CHCs were frequently strongly genetically correlated, with a tendency for negative covariance between long- and short-chain CHCs in males. Intersexual genetic covariances were often weak and frequently differed in sign. These findings are novel and significant, highlighting the previously unexplored genetic architecture of CHCs in D. simulans and suggest that this architecture may facilitate sex-specific CHC evolution.     

Selection of Pupation Habitats by Oriental Fruit Fly Larvae in the Laboratory

We performed a series of laboratory experiments to determine the effects of shade, soil moisture, and soil compaction on the selection of pupation habitats by wandering late-instar Oriental fruit flies, Bactrocera dorsalis (Hendel). Larvae showed a strong preference toward pupating in shaded rather than brightly lit areas, in moist rather than dry soil, and in soil with larger particle sizes. These behavioral preferences are likely to lead to clumped distribution of Oriental fruit fly pupae in natural habitats. The implications of this for management of localized populations by chemical and biological methods are discussed.     

Gene Duplication and Phenotypic Changes in the Evolution of Mammalian Metabolic Networks

Metabolic networks attempt to describe the complete suite of biochemical reactions available to an organism. One notable feature of these networks in mammals is the large number of distinct proteins that catalyze the same reaction. While the existence of these isoenzymes has long been known, their evolutionary significance is still unclear. Using a phylogenetically-aware comparative genomics approach, we infer enzyme orthology networks for sixteen mammals as well as for their common ancestors. We find that the pattern of isoenzymes copy-number alterations (CNAs) in these networks is suggestive of natural selection acting on the retention of certain gene duplications. When further analyzing these data with a machine-learning approach, we found that that the pattern of CNAs is also predictive of several important phenotypic traits, including milk composition and geographic range. Integrating tools from network analyses, phylogenetics and comparative genomics both allows the prediction of phenotypes from genetic data and represents a means of unifying distinct biological disciplines.     

福建地区橘小实蝇田间种群动态监测研究

利用Steiner诱捕器内置诱捕剂Me或Cue与0.2%马拉硫磷混合物制成棉球诱芯,在福建省各个地区共设置50个监测点,诱捕监测橘小实蝇（Bactrocera dorsalis Hendel）种群动态。从2002～2005年,连续4年的诱捕结果表明,橘小实蝇在福建省的发生呈单峰特点,7～9月为发生盛期,高峰出现在8月份。瓜菜园生境、石榴芒果园生境和龙眼香蕉园生境中,石榴芒果园生境橘小实蝇种群密度最高。运用地理信息系统（MapInfo）对该虫监测数据进行地图分析表明,橘小实蝇种群密度的地域特征明显,橘小实蝇有不断向外扩散的趋势。     

Phenotypic Evolution: A Reaction Norm Perspective

By Carl D. Schlichting and Massimo Pigliucci. Sunderland, MA: Sinauer. 1998. 387 pp. ISBN 0-87893-799-4. $38.95 (paper).     

A Discrete Fruit Fly Optimization Algorithm for the Traveling Salesman Problem

The fruit fly optimization algorithm (FOA) is a newly developed bio-inspired algorithm. The continuous variant version of FOA has been proven to be a powerful evolutionary approach to determining the optima of a numerical function on a continuous definition domain. In this study, a discrete FOA (DFOA) is developed and applied to the traveling salesman problem (TSP), a common combinatorial problem. In the DFOA, the TSP tour is represented by an ordering of city indices, and the bio-inspired meta-heuristic search processes are executed with two elaborately designed main procedures: the smelling and tasting processes. In the smelling process, an effective crossover operator is used by the fruit fly group to search for the neighbors of the best-known swarm location. During the tasting process, an edge intersection elimination (EXE) operator is designed to improve the neighbors of the non-optimum food location in order to enhance the exploration performance of the DFOA. In addition, benchmark instances from the TSPLIB are classified in order to test the searching ability of the proposed algorithm. Furthermore, the effectiveness of the proposed DFOA is compared to that of other meta-heuristic algorithms. The results indicate that the proposed DFOA can be effectively used to solve TSPs, especially large-scale problems.