Detecting functional interactions in a gene and signaling network by time-resolved somatic complementation analysis

Direct transfer of learned behaviour via cell fusion in non-neural organisms

Cell fusion is a fundamental phenomenon observed in all eukaryotes. Cells can exchange resources such as molecules or organelles during fusion. In this paper, we ask whether a cell can also transfer an adaptive response to a fusion partner. We addressed this question in the unicellular slime mould Physarum polycephalum, in which cell-cell fusion is extremely common. Slime moulds are capable of habituation, a simple form of learning, when repeatedly exposed to an innocuous repellent, despite lacking neurons and comprising only a single cell. In this paper, we present a set of experiments demonstrating that slime moulds habituated to a repellent can transfer this adaptive response by cell fusion to individuals that have never encountered the repellent. In addition, we show that a slime mould resulting from the fusion of a minority of habituated slime moulds and a majority of unhabituated ones still shows an adaptive response to the repellent. Finally, we further reveal that fusion must last a certain time to ensure an effective transfer of the behavioural adaptation between slime moulds. Our results provide strong experimental evidence that slime moulds exhibit transfer of learned behaviour during cell fusion and raise the possibility that similar phenomena may occur in other cell-cell fusion systems.

A next-generation sequencing approach to study the transcriptomic changes during the differentiation of physarum at the single-cell level

Physarum polycephalum is a unicellular eukaryote belonging to the amoebozoa group of organisms. The complex life cycle involves various cell types that differ in morphology, function, and biochemical composition. Sporulation, one step in the life cycle, is a stimulus-controlled differentiation response of macroscopic plasmodial cells that develop into fruiting bodies. Well-established Mendelian genetics and the occurrence of macroscopic cells with a naturally synchronous population of nuclei as source of homogeneous cell material for biochemical analyses make Physarum an attractive model organism for studying the regulatory control of cell differentiation. Here, we develop an approach using RNA-sequencing (RNA-seq), without needing to rely on a genome sequence as a reference, for studying the transcriptomic changes during stimulus-triggered commitment to sporulation in individual plasmodial cells. The approach is validated through the obtained expression patterns and annotations, and particularly the results from up- and downregulated genes, which correlate well with previous studies.

Automatic reconstruction of molecular and genetic networks from discrete time series data

We apply a mathematical algorithm which processes discrete time series data to generate a complete list of Petri net structures containing the minimal number of nodes required to reproduce the data set. The completeness of the list as guaranteed by a mathematical proof allows to define a minimal set of experiments required to discriminate between alternative network structures. This in principle allows to prove all possible minimal network structures by disproving all alternative candidate structures. The dynamic behaviour of the networks in terms of a switching rule for the transitions of the Petri net is part of the result. In addition to network reconstruction, the algorithm can be used to determine how many yet undetected components at least must be involved in a certain process. The algorithm also reveals all alternative structural modifications of a network that are required to generate a predefined behaviour.

Reconstructing the regulatory network controlling commitment and sporulation in Physarum polycephalum based on hierarchical Petri Net modelling and simulation

We reconstruct the regulatory network controlling commitment and sporulation of Physarum polycephalum from experimental results using a hierarchical Petri Net-based modelling and simulation framework. The stochastic Petri Net consistently describes the structure and simulates the dynamics of the molecular network as analysed by genetic, biochemical and physiological experiments within a single coherent model. The Petri Net then is extended to simulate time-resolved somatic complementation experiments performed by mixing the cytoplasms of mutants altered in the sporulation response, to systematically explore the network structure and to probe its dynamics. This reverse engineering approach presumably can be employed to explore other molecular or genetic signalling systems where the activity of genes or their products can be experimentally controlled in a time-resolved manner.

Use of the giant multinucleate plasmodium of Physarum polycephalum to study RNA interference in the myxomycete

The plasmodium of Physarum polycephalum harbors billions of synchronized nuclei in a single cell of complex structure. Due to its synchrony and extreme size, it is used as a model to study events on a single cell level, such as cell cycle and differentiation. We show here for the first time that this model, despite its enormous size and structural complexity, is accessible to RNA interference by simple injection of dsRNA or siRNA. The targeted gene is that of polymalatase, an intracellular adapter of poly(beta-l-malate) involved in the maintenance of the synchrony and functioning as an extracellular hydrolase of this polymer. Real-time reverse transcriptase polymerase chain reaction analysis revealed that the specific mRNA was knocked down to about 10% of the original level. The suppression of a single injection lasted for approximately 14 cell cycles (144 h) and could be prolonged for any time by repeated dsRNA injections. Western blots indicated that the knockdown of RNA was paralleled by a strong reduction in polymalatase synthesis. However, a change in the phenotype of the plasmodium could not be clearly observed. In principle, the plasmodium offers an easy system for studying gene knockdown by RNA interference.