Cuticular competing endogenous RNAs regulate insecticide penetration and resistance in a major agricultural pest.

BACKGROUND: The continuously developing pesticide resistance is a great threat to agriculture and human health. Understanding the mechanisms of insecticide resistance is a key step in dealing with the phenomenon. Insect cuticle is recently documented to delay xenobiotic penetration which breaks the previous stereotype that cuticle is useless in insecticide resistance, while the underlying mechanism remains scarce. RESULTS: Here, we find the integument contributes over 40.0% to insecticide resistance via different insecticide delivery strategies in oriental fruit fly. A negative relationship exists between cuticle thickening and insecticide penetration in resistant/susceptible, also in field strains of oriental fruit fly which is a reason for integument-mediated resistance. Our investigations uncover a regulator of insecticide penetration that miR-994 mimic treatment causes cuticle thinning and increases susceptibility to malathion, whereas miR-994 inhibitor results in opposite phenotypes. The target of miR-994 is a most abundant cuticle protein (CPCFC) in resistant/susceptible integument expression profile, which possesses capability of chitin-binding and influences the cuticle thickness-mediated insecticide penetration. Our analyses find an upstream transcriptional regulatory signal of miR-994 cascade, long noncoding RNA (lnc19419), that indirectly upregulates CPCFC in cuticle of the resistant strain by sponging miR-994. Thus, we elucidate the mechanism of cuticular competing endogenous RNAs for regulating insecticide penetration and demonstrate it also exists in field strain of oriental fruit fly. CONCLUSIONS: We unveil a regulatory axis of lnc19419 ~ miR-994 ~ CPCFC on the cuticle thickness that leads to insecticide penetration resistance. These findings indicate that competing endogenous RNAs regulate insecticide resistance by modulating the cuticle thickness and provide insight into the resistance mechanism in insects.

Genome-wide identification of long non-coding RNAs (lncRNAs) associated with malathion resistance in Bactrocera dorsalis.

BACKGROUND: Long non-coding RNAs (lncRNAs) play important roles in the regulation of biological processes and have been identified in many species including insects. However, the association between lncRNAs and pesticide resistance in insect species such as Bactrocera dorsalis is unknown. RESULTS: RNA-seq was performed on malathion resistant (MR1) and susceptible (MS) strains of B. dorsalis and a total of 6171 lncRNAs transcripts were identified. These included 3728 lincRNAs, 653 antisense lncRNAs, 1402 intronic lncRNAs, and 388 sense lncRNAs. A total of 40 and 52 upregulated lncRNAs were found in females and males of the MR1 strain compared to 54 and 49 in the same sexes of the MS strain, respectively. Twenty-seven of these lncRNAs showed the same trend of expression in both females and males in the MR1 strain, in which 15 lncRNAs were upregulated and 12 were downregulated. RT-qPCR results indicated that the differentially expressed lncRNAs were associated with malathion resistance. The lnc15010.10 and lnc3774.2 were highly expressed in the cuticle of the MR1 strain, indicating that these two lncRNAs may be related to malathion resistance. RNAi of lnc3774.2 and a bioassay showed that malathion resistance was possibly influenced by changes in the B. dorsalis cuticle. CONCLUSION: LncRNAs of B. dorsalis potentially related to the malathion resistance were identified. Two lncRNAs appear to influence malathion resistance via modulating the structure, or components, of the cuticle. © 2021 Society of Chemical Industry.

A glutathione S-transferase (BdGSTd9) participates in malathion resistance via directly depleting malathion and its toxic oxide malaoxon in Bactrocera dorsalis (Hendel).

BACKGROUND: The oriental fruit fly, Bactrocera dorsalis (Hendel), is a widespread agricultural pest that has evolved resistance to many commonly used insecticides including malathion. Glutathione S-transferases (GSTs) are multifunctional enzymes that metabolize insecticides directly or indirectly. The specific mechanism used by GSTs to confer malathion resistance in B. dorsalis is unclear. RESULTS: BdGSTd9 was identified from B. dorsalis and was expressed at twice the level in a malathion-resistant strain (MR) than in a susceptible strain (MS). By using RNAi of BdGSTd9, the toxicity of malathion against MR was increased. Protein modelling and docking of BdGSTd9 with malathion and malaoxon indicated key amino acid residues for direct binding in the active site. In vitro assays with engineered Sf9 cells overexpressing BdGSTd9 demonstrated lower cytotoxicity of malathion. High performance liquid chromatography (HPLC) analysis indicated that malathion could be broken down significantly by BdGSTd9, and it also could deplete the malathion metabolite malaoxon, which possesses a higher toxicity to B. dorsalis. Taken together, the BdGSTd9 of B. dorsalis could not only deplete malathion, but also react with malaoxon and therefore enhance malathion resistance. CONCLUSION: BdGSTd9 is a component of malathion resistance in B. dorsalis. It acts by depleting both malathion and malaoxon. © 2020 Society of Chemical Industry.

Geographical networks evolving with an optimal policy.

In this article we propose a growing network model based on an optimal policy involving both topological and geographical measures. In this model, at each time step, a node, having randomly assigned coordinates in a 1x1 square, is added and connected to a previously existing node i, which minimizes the quantity ri2/kialpha, where ri is the geographical distance, ki the degree, and alpha a free parameter. The degree distribution obeys a power-law form when alpha=1, and an exponential form when alpha=0. When alpha is in the interval (0, 1), the network exhibits a stretched exponential distribution. We prove that the average topological distance increases in a logarithmic scale of the network size, indicating the existence of the small-world property. Furthermore, we obtain the geographical edge length distribution, the total geographical length of all edges, and the average geographical distance of the whole network. Interestingly, we found that the total edge length will sharply increase when alpha exceeds the critical value alphac=1, and the average geographical distance has an upper bound independent of the network size. All the results are obtained analytically with some reasonable approximations, which are well verified by simulations.

Behaviors of susceptible-infected epidemics on scale-free networks with identical infectivity.

In this paper, we propose a susceptible-infected model with identical infectivity, in which, at every time step, each node can only contact a constant number of neighbors. We implemented this model on scale-free networks, and found that the infected population grows in an exponential form with the time scale proportional to the spreading rate. Furthermore, by numerical simulation, we demonstrated that the targeted immunization of the present model is much less efficient than that of the standard susceptible-infected model. Finally, we investigate a fast spreading strategy when only local information is available. Different from the extensively studied path-finding strategy, the strategy preferring small-degree nodes is more efficient than that preferring large-degree nodes. Our results indicate the existence of an essential relationship between network traffic and network epidemic on scale-free networks.

Continuous extremal optimization for Lennard-Jones clusters.

We explore a general-purpose heuristic algorithm for finding high-quality solutions to continuous optimization problems. The method, called continuous extremal optimization (CEO), can be considered as an extension of extremal optimization and consists of two components, one which is responsible for global searching and the other which is responsible for local searching. The CEO's performance proves competitive with some more elaborate stochastic optimization procedures such as simulated annealing, genetic algorithms, and so on. We demonstrate it on a well-known continuous optimization problem: the Lennard-Jones cluster optimization problem.