Chakrabarty, S. | Search bioRxiv

bioRxiv2019

High-depth resequencing reveals hybrid population and insecticide resistance characteristics of fall armyworm (Spodoptera frugiperda) invading China

The rapid wide-scale spread of fall armyworm (Spodoptera frugiperda) has caused serious crop losses globally. However, differences in the genetic background of subpopulations and the mechanisms of rapid adaptation behind the invasion are still not well understood. Here we report a 393.25-M chromosome-level genome assembly of fall armyworm with scaffold N50 of 13.3 M consisting of 23281 annotated protein-coding genes. Genome-wide resequencing of 105 samples from 16 provinces in China revealed that the fall armyworm population comprises a complex inter-strain hybrid, mainly with the corn-strain genetic background and less of the rice-strain genetic background, which highlights the inaccuracy of strain identification using mitochondrial or Tpi genes. An analysis of genes related to pesticide- and Bt-resistance showed that the risk of fall armyworm developing resistance to conventional pesticides is very high, while remaining currently susceptible to Bt toxins. Laboratory bioassay results showed that insects invading China carry resistance to organophosphate and pyrethroid pesticides, but are sensitive to genetically modified maize expressing Cry1Ab in field experiments. Additionally, we found that two mitochondrial fragments are inserted into the nuclear genome, and the insertion event occurred after the differentiation of the two strains. This study represents a valuable advancement toward the analysis of genetic differences among subpopulations and improving management strategies for fall armyworm.

genomics↗

bioRxiv2019

Muscles Recruited During an Isometric Knee Extension Task is Defined by Proprioceptive Feedback

The influence of proprioceptive feedback on muscle activity during isometric tasks is the subject of conflicting studies. To better understand the relationship, we performed an isometric knee extension task experiment at four pre-set angles of the knee, recording from five muscles, and for two different hip positions. We applied muscle synergy analysis using NMF on the sEMG recordings to identify structure in the data which changed with internal knee angle, suggesting a link between proprioception and muscle activity. We hypothesised that such patterns in the data arise from the way proprioceptive and cortical signals are integrated in neural circuits of the spinal cord. Using the MIIND neural simulation platform, we developed a computational model based on current understanding of spinal circuits with an adjustable proprioceptive input. The model produces the same synergy patterns as observed in the experimental data indicating that such synergies are indeed encoded in the neural connectivity of the spinal cord and modulated by the proprioceptive input. When matching the proprioceptive input to the knee angles of the experiment, the model predicts the need for three distinct inputs: two to control the normal reciprocity between the agonist and antagonist muscles, and an additional to match the non-linear trend towards the limit of extension. Finally, we discuss the risks of using NMF for synergy analysis and demonstrate how to increase confidence in its results. Future modelling of human motor outputs should include interneuronal spinal circuits such as this to distinguish the modulatory role of supraspinal and peripheral afferent inputs to the spinal cord, during both passive and dynamic tasks. Significance statementSensory feedback from muscles has a significant role in motor control, but its role in tasks where limbs are held in a fixed position is disputed, because the effect is reduced when muscles are not stretched. Here, we first identified patterns of muscle activity during such tasks which changed with different leg positions. We developed a computational spinal motor circuit model with adjustable muscle stretch input, which reproduced the same patterns of activity as observed experimentally. The model predicts three distinct muscle stretch signals required to produce the activity patterns for all leg positions. Because the connections in the model are based on well known spinal circuits, it is likely the observed activity patterns are generated in the spinal cord.

neuroscience↗