Transcriptional profiling of Dictyostelium development reveals significant conservation of transcriptional profiles between evolutionarily divergent species.

Cooperation is central to many major transitions in evolution, including the emergence of eukaryotic cells, multicellularity and eusociality. Cooperation can be destroyed by the spread of cheater mutants that do not cooperate but gain the benefits of cooperation from others. However, cooperation can be preserved if cheaters are facultative, cheating others but cooperating among themselves. Several cheater mutants have been studied before, but no study has attempted a genome-scale investigation of the genetic opportunities for cheating. Here we describe such a screen in a social amoeba and show that cheating is multifaceted by revealing cheater mutations in well over 100 genes of diverse types. Many of these mutants cheat facultatively, producing more than their fair share of spores in chimaeras, but cooperating normally when clonal. These findings indicate that phenotypically stable cooperative systems may nevertheless harbour genetic conflicts. The opportunities for evolutionary moves and countermoves in such conflicts may select for the involvement of multiple pathways and numerous genes.

BackgroundThe social amoebae (Dictyostelia) are a diverse group of Amoebozoa that achieve multicellularity by aggregation and undergo morphogenesis into fruiting bodies with terminally differentiated spores and stalk cells. There are four groups of dictyostelids, with the most derived being a group that contains the model species Dictyostelium discoideum.ResultsWe have produced a draft genome sequence of another group dictyostelid, Dictyostelium purpureum, and compare it to the D. discoideum genome. The assembly (8.41 × coverage) comprises 799 scaffolds totaling 33.0 Mb, comparable to the D. discoideum genome size. Sequence comparisons suggest that these two dictyostelids shared a common ancestor approximately 400 million years ago. In spite of this divergence, most orthologs reside in small clusters of conserved synteny. Comparative analyses revealed a core set of orthologous genes that illuminate dictyostelid physiology, as well as differences in gene family content. Interesting patterns of gene conservation and divergence are also evident, suggesting function differences; some protein families, such as the histidine kinases, have undergone little functional change, whereas others, such as the polyketide synthases, have undergone extensive diversification. The abundant amino acid homopolymers encoded in both genomes are generally not found in homologous positions within proteins, so they are unlikely to derive from ancestral DNA triplet repeats. Genes involved in the social stage evolved more rapidly than others, consistent with either relaxed selection or accelerated evolution due to social conflict.ConclusionsThe findings from this new genome sequence and comparative analysis shed light on the biology and evolution of the Dictyostelia.

Background Amoebae and bacteria interact within predator/prey and host/pathogen relationships, but the general response of amoeba to bacteria is not well understood. The amoeba Dictyostelium discoideum feeds on, and is colonized by diverse bacterial species including Gram-positive [Gram(+)] and Gram-negative [Gram(−)] bacteria, two major groups of bacteria that differ in structure and macromolecular composition. Results Transcriptional profiling of D. discoideum revealed sets of genes whose expression is enriched in amoebae interacting with different species of bacteria, including sets that appear specific to amoebae interacting with Gram(+), or with Gram(−) bacteria. In a genetic screen utilizing the growth of mutant amoebae on a variety of bacteria as a phenotypic readout, we identified amoebal genes that are only required for growth on Gram(+) bacteria, including one that encodes the cell surface protein gp130, as well as several genes that are only required for growth on Gram(−) bacteria including one that encodes a putative lysozyme, AlyL. These genes are required for parts of the transcriptional response of wild-type amoebae, and this allowed their classification into potential response pathways. Conclusions We have defined genes that are critical for amoebal survival during feeding on Gram(+), or Gram(−), bacteria which we propose form part of a regulatory network that allows D. discoideum to elicit specific cellular responses to different species of bacteria in order to optimize survival.

Concurrent learning (CL) is a recently developed adaptive update scheme that can be used to guarantee parameter convergence without requiring persistent excitation. However, this technique requires knowledge of state derivatives, which are usually not directly sensed and therefore must be estimated. A novel integral CL method is developed in this paper that removes the need to estimate state derivatives while maintaining parameter convergence properties. Data recorded online is exploited in the adaptive update law, and numerical integration is used to circumvent the need for state derivatives. The novel adaptive update law results in negative definite parameter error terms in the Lyapunov analysis, provided an online-verifiable finite excitation condition is satisfied. A Monte Carlo simulation illustrates improved robustness to noise compared to the traditional derivative formulation. The result is also extended to Euler-Lagrange systems, and simulations on a two-link planar robot demonstrate the improved performance compared to gradient-based adaptation laws. PARIKH ET AL. verified online, in general, for nonlinear systems. However, all current CL methods require that the output data include the state derivatives, which may not be available for all systems. Since the naive approach of finite difference of the state measurements leads to noise amplification, and since only past recorded data, opposed to real-time data, is needed for CL, techniques such as online state derivative estimation or smoothing have been employed, eg, the works of Mühlegg et al 9 and Kamalapurkar et al. 10 However, these methods typically require tuning parameters such as an observer gain and switching threshold in the case of the online derivative estimator and basis, basis order, covariance, and time window in the case of smoothing, to produce satisfactory results.In this note, we reformulate the derivative-based CL method (DCL) in terms of an integral, removing the need to estimate state derivatives. Other methods such as composite adaptive control also use integration-based terms to improve parameter convergence (see, eg, the works of Slotine and Li, 11 Volyanskyy et al, 12 and Pan et al 13 ); however, they still require PE to ensure exponential convergence. Recently, results in other works 14-18 have shown convergence using an interval or finite excitation condition, although they either require measurements of state derivatives (see, eg, the work of Pan and Yu 15 ), require determining the analytical Jacobian of the regressor (see, eg, the work of Pan et al 14 ), or are developed in a model reference adaptive control context, 16,17,[19][20][21] which essentially assume that desired trajectories are generated from an LTI system and may rely on a matching condition, rather than the general nonlinear systems considered here without such assumptions. Some results 22-27 have theoretical analogs to those presented here, and some use filtering techniques to avoid data storage requirements, although we show how the parameter estimation i...