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Locust density shapes energy metabolism and oxidative stress resulting in divergence of flight traits

Flight ability is essential for the enormous diversity and evolutionary success of insects. The migratory locusts exhibit flight capacity plasticity in gregarious and solitary individuals closely linked with different density experiences. However, the differential mechanisms underlying flight traits of locusts are largely unexplored. Here, we investigated the variation of flight capacity by using behavioral, physiological, and multiomics approaches.

 

Baozhen Du, Ding Ding, Chuan Ma, Wei Guo, and Le Kang

PNAS January 4, 2022 119 (1) e2115753118

Significance

Migratory locusts display striking phenotypical plasticity. Gregarious locusts at high density can migrate long distances and cause huge economic losses of crops. By contrast, solitary locusts at low density have limited ability in long-distance flight. However, the mechanisms underlying such flight capacity variation are poorly understood. Here, we found that the flight muscle of solitary locusts has a higher catabolic capacity that is associated with greater reactive oxygen species (ROS) generation during high-velocity flights. By contrast, a relatively lower catabolic capacity in gregarious locusts is associated with lower ROS generation during long-distance flights. This finding uncovers the metabolic mechanism of locust flight trait alteration in response to density changes and enhances our understanding of the biological processes enabling locust migration.

Abstract

Flight ability is essential for the enormous diversity and evolutionary success of insects. The migratory locusts exhibit flight capacity plasticity in gregarious and solitary individuals closely linked with different density experiences. However, the differential mechanisms underlying flight traits of locusts are largely unexplored. Here, we investigated the variation of flight capacity by using behavioral, physiological, and multiomics approaches. Behavioral assays showed that solitary locusts possess high initial flight speeds and short-term flight, whereas gregarious locusts can fly for a longer distance at a relatively lower speed. Metabolome–transcriptome analysis revealed that solitary locusts have more active flight muscle energy metabolism than gregarious locusts, whereas gregarious locusts show less evidence of reactive oxygen species production during flight. The repression of metabolic activity by RNA interference markedly reduced the initial flight speed of solitary locusts. Elevating the oxidative stress by paraquat injection remarkably inhibited the long-distance flight of gregarious locusts. In respective crowding and isolation treatments, energy metabolic profiles and flight traits of solitary and gregarious locusts were reversed, indicating that the differentiation of flight capacity depended on density and can be reshaped rapidly. The density-dependent flight traits of locusts were attributed to the plasticity of energy metabolism and degree of oxidative stress production but not energy storage. The findings provided insights into the mechanism underlying the trade-off between velocity and sustainability in animal locomotion and movement.

 

See https://www.pnas.org/content/119/1/e2115753118

Fig. 1.

Flight trait divergence between gregarious and solitary locusts. (A) Typical gregarious and solitary adult locusts. G and S represent gregarious and solitary locusts, respectively (n = 28 individuals, Student’s t test). (B and C) Morphological comparisons between G and S. FW and HW represent forewing and hindwing, respectively (n ≥ 20 individuals, Student’s t test). (D) Comparison of maximum flight speed between G and S. Each dot represents a single individual (Mann–Whitney U test). (EH) The measurement of flight parameters (E) average flight speed, (F) total flight distance, (G) total duration of flight, and (H) total duration of resting. (I) Comparison of flight muscle kinetic energy between G and S during flight. Flight performance for 0.25-, 1-, 2-, 4-, and 10-h tests were compared. Data for each time point was obtained independently, n ≥ 20 individuals at each time point (Mann–Whitney U test). The fitting curve was obtained by nonlinear regression. All data are presented as the mean ± SEM, *P < 0.05, **P < 0.01, and n.s, not significant.

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