Cell-selective modulation of the Drosophila neuromuscular system by a neuropeptide

Neuropeptides can modulate physiological properties of neurons in a cell-specific manner. The present work examines whether a neuropeptide can also modulate muscle tissue in a cell-specific manner, using identified muscle cells in third instar larvae of fruit flies. DPKQDFMRFa, a modulatory peptide in the fruit fly Drosophila melanogaster, has been shown to enhance transmitter release from motor neurons and to elicit contractions by a direct effect on muscle cells. We report that DPKQDFMRFa causes a nifedipine-sensitive drop in input resistance in some muscle cells (6 and 7) but not others (12 and 13). The peptide also increased the amplitude of nerve-evoked contractions and compound excitatory junctional potentials (EJPs) to a greater degree in muscle cells 6 and 7 than 12 and 13. Knocking down FMRFa receptor (FR) expression separately in nerve and muscle indicate that both presynaptic and postsynaptic FR expression contributed to the enhanced contractions, but EJP enhancement was due mainly to presynaptic expression. Muscle-ablation showed that DPKQDFMRFa induced contractions and enhanced nerve-evoked contractions more strongly in muscle cells 6 and 7 than cells 12 and 13. In situ hybridization indicated that FR expression was significantly greater in muscle cells 6 and 7 than 12 and 13. Taken together, these results indicate that DPKQDFMRFa can elicit cell-selective effects on muscle fibres. The ability of neuropeptides to work in a cell-selective manner on neurons and muscle cells may help explain why so many peptides are encoded in invertebrate and vertebrate genomes.

Assessing the Reproducibility of Clustering of Molecular Dynamics Conformations on Self-Organizing Maps

The goal of most clustering algorithms is to find the optimal number of clusters (i.e. fewest number of clusters). However, analysis of molecular conformations of biological macromolecules obtained from computer simulations may benefit from a larger array of clusters. The Self-Organizing Map (SOM) clustering method has the advantage of generating large numbers of clusters, but often gives ambiguous results. In this work, SOMs have been shown to be reproducible when the same conformational dataset is independently clustered multiple times (~100), with the help of the Cramérs V-index (C_v). The ability of C_v to determine which SOMs are reproduced is generalizable across different SOM source codes. The conformational ensembles produced from MD (molecular dynamics) and REMD (replica exchange molecular dynamics) simulations of the penta peptide Met-enkephalin (MET) and the 34 amino acid protein human Parathyroid Hormone (hPTH) were used to evaluate SOM reproducibility. The training length for the SOM has a huge impact on the reproducibility. Analysis of MET conformational data definitively determined that toroidal SOMs cluster data better than bordered maps due to the fact that toroidal maps do not have an edge effect. For the source code from MATLAB, it was determined that the learning rate function should be LINEAR with an initial learning rate factor of 0.05 and the SOM should be trained by a sequential algorithm. The trained SOMs can be used as a supervised classification for another dataset. The toroidal 10×10 hexagonal SOMs produced from the MATLAB program for hPTH conformational data produced three sets of reproducible clusters (27%, 15%, and 13% of 100 independent runs) which find similar partitionings to those of smaller 6×6 SOMs. The χ^2 values produced as part of the C_v calculation were used to locate clusters with identical conformational memberships on independently trained SOMs, even those with different dimensions. The χ^2 values could relate the different SOM partitionings to each other.