Cannibalism Affects Core Metabolic Processes in Helicoverpa armigera Larvae—A 2D NMR Metabolomics Study

Vergara, Fredd; Shino, Amiu; Kikuchi, Jun

doi:10.3390/ijms17091470

Cited by 10 publications

(3 citation statements)

References 32 publications

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“…However, this effect may vary according to the insect species. Cannibal individuals of H. armigera have reduced size and impaired development [ 23 ]. S. frugiperda cannibal individuals had similar consumption rates, relative growth rates, and conversion efficiencies of ingested food compared to those fed only corn [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

Modeling the Resistance Evolution to Insecticides Driven by Lepidopteran Species Competition in Cotton, Soybean, and Corn Crops

Malaquias

Ferreira

Ramalho

et al. 2022

Biology

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Simple Summary Lepidopteran species commonly interact in the same niches in multiple crops. Interspecific competition has been neglected as a pressure selection agent in insecticide resistance studies. Our results showed that competition may act as an agent to speed up the evolution of diamide resistance in Spodoptera frugiperda and Helicoverpa armigera . Abstract Intra- and interspecific competition is considered a fundamental phenomenon in ecology. It acts as one of the most powerful selective forces that drives ecological diversity, the spatiotemporal distribution of organisms, fitness, and evolutionary aspects. Spodoptera frugiperda and Helicoverpa armigera are devastating pests and can co-occur in systems consisting of multiple agricultural crops and compete for food resources. Insecticide resistance in populations of these species has been a major threat to the sustainability of agroecosystems. No study to date has shown the effect of intra- and interspecific competition as a selective pressure agent on the evolution of insecticide resistance in lepidopteran pests in an experimental and theoretical way. Our study developed a parameterized computational model with experimental results for S. frugiperda and H. armigera competition. We simulated the behavior of heterozygous individuals with a competition capacity 100% equal to homozygous individuals resistant (100 RR) or susceptible to insecticides (00 RR), and intermediate between them (50 RR). Competition involving strains of these insect species can accelerate the evolution of their resistance to insecticides in agricultural crops. We found that competitive processes can result in a high probability of competitive exclusion for individuals with the susceptibility allele of these lepidopteran species. The results of this study are of paramount importance for understanding the impact of ecological factor competition on the evolution of insecticide resistance in lepidopteran pests, which until now has been neglected in these types of evolutionary dynamics studies.

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Section: Discussionmentioning

confidence: 99%

Modeling the Resistance Evolution to Insecticides Driven by Lepidopteran Species Competition in Cotton, Soybean, and Corn Crops

Malaquias

Ferreira

Ramalho

et al. 2022

Biology

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“…In the wild, their larval stage would be much longer, as they need to hibernate in the winter [ 35 ]. Another example of an aggressive Lepidopteran insect is Helicoverpa armigera (Lepidoptera: Noctuidae), the larva of which show aggressive and even cannibalistic behavior toward conspecifics [ 36 ]. Both insect larvae display aggressive behavior toward each other during encountered.…”

Section: Introductionmentioning

confidence: 99%

Transcriptome Analyses Provide Insights into the Aggressive Behavior toward Conspecific and Heterospecific in Thitarodes xiaojinensis (Lepidoptera: Hepialidae)

Rao

Cao

et al. 2021

Insects

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Aggressive behavior in animals is important for survival and reproduction. It is well studied in adult insects, such as flies, ants, honey bees, and crickets. However, the larvae of Lepidopteran insects are also aggressive, studies of which are still lacking. Here, RNA-seq was used to generate a high-quality database for the aggressive behavior of Thitarodes xiaojinensis toward conspecifics and heterospecifics. Although there was similar aggressive behavior between the conspecific group and heterospecific group, significant differences were identified at the transcriptional level. When there was aggressive behavior toward conspecifics, T. xiaojinensis trended toward higher expression at the respiratory chain, while cuticle development and metabolism may have interfered. On the other hand, when there was aggressive behavior toward H. armigera, genes related to neuron and cuticle development, cellular processes, and its regulated signaling pathways were significantly upregulated, while the genes associated with oxidation-reduction and metabolism were downregulated. Weighted gene co-expression networks analysis (WGCNA) was performed, and two modules with properties correlating to the aggressive behavior of T. xiaojinensis were identified. Several hub genes were predicted and confirmed by qRT-PCR, such as CLTC, MYH, IGF2BP1, and EMC. This study provides a global view and potential key genes for the aggressive behavior of T. xiaojinensis toward conspecifics and heterospecifics. Further investigation of the hub genes would help us to better understand the aggressive behavior of insects.

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“…MNR also provides accurate quantification 10 with inter-institution convertibility 11 . In previous studies, NMR-based metabolic profiling has been applied to plants [12][13][14][15] , animals [16][17][18][19][20] , and microbial [21][22][23][24] systems.…”

Section: Introduction * Junkikuchi@rikenjpmentioning

confidence: 99%

[Dedicated to Prof. T. Okada and Prof. T. Nishioka: data science in chemistry]Visualizing Individual and Region-specific Microbial–metabolite Relations by Important Variable Selection Using Machine Learning Approaches

Tsutsui

Date

Kikuchi

2017

J. Comput. Aided Chem.

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Data mining techniques such as machine learning have greatly advanced the chemical and biological sciences. Especially, technological advances in data mining are anticipated for analyzing big data derived from biological and environmental systems. From this perspective, we analyze the complex metabolic and microbial responses of human skin and the relations among these responses using advanced data mining techniques. To this end, metabolic profiles of human sweats were characterized via multiple NMR spectra, followed by an advanced analytical strategy based on data-driven and machine learning approaches. These methods extracted the important variables of the metabolites associated with microbial community variations. Moreover, the relation between the sweat metabolites and the skin microbes was successfully visualized by correlation-based networks. This analytical strategy promises a versatile and useful approach for big data analyses in various fields of science.

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Cannibalism Affects Core Metabolic Processes in Helicoverpa armigera Larvae—A 2D NMR Metabolomics Study

Cited by 10 publications

References 32 publications

Modeling the Resistance Evolution to Insecticides Driven by Lepidopteran Species Competition in Cotton, Soybean, and Corn Crops

Modeling the Resistance Evolution to Insecticides Driven by Lepidopteran Species Competition in Cotton, Soybean, and Corn Crops

Transcriptome Analyses Provide Insights into the Aggressive Behavior toward Conspecific and Heterospecific in Thitarodes xiaojinensis (Lepidoptera: Hepialidae)

[Dedicated to Prof. T. Okada and Prof. T. Nishioka: data science in chemistry]Visualizing Individual and Region-specific Microbial–metabolite Relations by Important Variable Selection Using Machine Learning Approaches

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