Non-targeted metabolomics reveals differences in the gut metabolic profile of the fall armyworm strains when feeding different food sources
Graphical abstract
Introduction
Spodoptera frugiperda (Lepidoptera, Noctuidae), the fall armyworm (FAW), feeds on approximately 350 different species from 76 families (Montezano et al., 2018). Despite this wide range of host plants, FAW is best known as one of the most important agricultural pests of grasses (maize, millet, rice and sorghum) and some cultivated dicots such as cotton (Barros et al., 2010). The FAW is native to the New World, but in the last few years has invaded Africa and further spread to Asia and Oceania (Goergen et al., 2016, Johnson, 1987, Otim et al., 2018, Piggott et al., 2021, Suby et al., 2020). Therefore, FAW is currently considered of a global concern due its polyphagy and capacity for rapid evolution of resistance to pesticides and Bt crops (Huang, 2021, Jakka et al., 2016), representing an imminent threat to food security and a source of significant economic losses.
The FAW is the only species of Spodoptera that usually feeds on grasses without having adapted, suitable mandibles. Larvae that feed on grasses typically have specialized mandibles with chisel-like edges adapted to the consumption of silica-rich leaves, which cause wear to larval mandibles (Djamin and Pathak, 1967, Pogue, 2002, Smith, 2005). Mandibles of the FAW have serrate-like processes adapted to the consumption of dicots or monocots that do not accumulate silica (Pogue, 2002). FAW is primitively polyphagous, but because of the mandible-type it is thought to have started exploiting cultivated grasses as host plants only recently (Kergoat et al., 2012, Kergoat et al., 2021).
Another interesting aspect of the FAW is the identification of two distinct strains known as the rice (RS) and corn (CS) strains (Gouin et al., 2017, Pashley, 1986). There are indications that this divergence occurred about 2 Myr ago (Kergoat et al., 2012, Kergoat et al., 2021). FAW strains differ in their performance and preference for host plants, and the correct classification of these two strains of the FAW is still controversial. Some authors refer to them as “sibling species” (Drès and Mallet, 2002, Dumas et al., 2015), “host strains” (Pashley, 1986, Prowell et al., 2004), “host form” (Juárez et al., 2014) and “morphocryptic strains” (Sarr et al., 2021).
The lack of consensus is due to the fact these strains co-exist in sympatry and still hybridize, but also due to inconsistencies in the associations with the named host plants. At the adult stage, both corn and rice strains showed weak evidence of preference for their expected host plant in choice and non-choice laboratory experiments (Orsucci et al., 2022). Even though the corn strain is often associated with maize, sorghum, and cotton, and the rice strain with rice and pasture grasses, some reports show the rice strain larvae developed better on corn and sorghum than the corn strain larvae (Meagher et al., 2011). Moreover, both strains performed poorly when feeding on rice (Silva-Brandão et al., 2017). Therefore, further studies are still needed to understand how the process of host-plant adaptation is taking place in FAW.
Every novel acquisition of a host plant by herbivores constitutes a new niche adaptation program that opens several evolutionary possibilities, but not without associated costs. Insects have to become adapted to deal with new defensive secondary metabolites, such as phenolics and terpenoids, and the nutritional quality to successfully exploit new host plants (Singer, 2008). However, the mechanisms behind the best performance of a given host-adapted strain on a given plant are poorly understood so far. Different approaches can be used to address this question. One alternative is to access the insect metabolome, the set of all low-molecular-weight metabolites that are produced during cell metabolism (Sun and Hu, 2016). Ultimately, the metabolome is a product of genomic, transcriptomic, and/or proteomic processes (Johnson and Gonzalez, 2012). Non-targeted metabolomics provides a holistic view of the insect metabolic profile. It makes no assumptions about which metabolites are important in distinguishing sample types (Sévin et al., 2015). This approach provides a direct functional measurement of cellular activity and physiological state, reflecting environmental changes such as new host plants as well as aspects related to their genome, as different host-adapted strains. Therefore, the non-targeted study of metabolomes is a good tool to highlight candidate metabolites involved in insect-plant interactions (Maag et al., 2015).The assessment of the insect midgut may be particularly useful, bearing in mind that it is a selectively permeable and metabolically active tissue, in which most digestion and almost all nutrient absorption takes place (Dow, 1987). However, approaches focused on the assessment of the gut metabolomics of insect herbivores are not common, and little is known on how host plants impact the metabolic profile of the herbivore gut.
The gut microbiome is also a key player in the metabolic processes of their hosts. Gut microbes can play important roles in several metabolic functions, including vitamin production (Salem et al., 2014, Chen et al., 2016), amino acid synthesis (Ayayee et al., 2016, Xia et al., 2017), and detoxification of secondary plant compounds and synthetic insecticides (Ceja-Navarro et al., 2015, Almeida et al., 2017). Among the numerous factors that influence the gut microbiota (Dillon and Dillon, 2004, Yun et al., 2014), diet has received considerable attention due to its strong effect on the composition of the microbial community (Wongsiri and Randolph, 1962, Yun et al., 2014, Mason et al., 2020). Diet provides the substrates to produce a plethora of small molecules that can be converted by the gut microbiota, and which are not produced by the host (Krishnan et al., 2015, Wang et al., 2020). Therefore, the gut microbiota may also facilitate adaptation to new host plants by regulating or participating in the host's metabolic processes (Hammer and Bowers, 2015, Zhang et al., 2020). Microbial contribution will depend on substrate availability and on microbial gene diversity and activity (Wu et al., 2016). Thus, taxonomic or metagenomic information of the gut microbiota is limited in predicting the metabolome of a microbial community, as it may under- or overestimate the functional contribution of associated gut microbiota depending on the nutritional conditions the host is exposed to (Wu et al., 2016).
The FAW is a good model to study adaptation of phytophagous insects to agricultural plants. Moreover, the metabolic processes underlying host shifts or differentiation in this species are not well understood. In terms of metabolome, we would expect different metabolic profiles to reflect new adaptations. We predict differences in larval adaptation to host plants to be reflected in the metabolome of their gut. These differences might highlight adaptations in response to plant chemistry, changes in metabolic pathways, and/or new roles for microbial symbionts. Enterococcal symbionts of FAW were recently shown to improve FAW larval performance when feeding on a suboptimal artificial diet (Chen et al., 2022). The aim of the present research is to investigate if the gut metabolome of FAW is determined by the diet and/or by host genotype. Highlighting the metabolic differences in the midgut of the FAW strains may represent the starting point for future research that aims to clarify: 1) how different host plants affect insect nutritional metabolism and 2) how the digestive physiology of FAW strains changes when exploiting different host plants during the evolution of the host-plant adaptation process.
Section snippets
Insect rearing and strains identification
Colonies of FAW were initiated in the laboratory from field-collected populations. The RS was originally obtained from rice fields in Santa Maria, RS, Brazil (29°68′68″S, 53°81′49″W) and the CS from a maize field in Piracicaba, SP, Brazil (22°43′30″S, 47°38′56″O). Field-collected larvae were individualized into plastic cups containing an artificial diet based on wheat germ, beans and brewer's yeast (Burton and Perkins, 1972, Kasten et al., 1978), brought to the laboratory and reared under
Result
The host plants on which FAW larvae fed significantly affected the midgut metabolome. The midgut metabolomes of RS and CS larvae also differed when feeding on the same diet. However, the food source had a greater impact shaping the gut metabolome of FAW than the host strain (see Table 1). Metabolomic analyses led to the identification of two major clusters of metabolites, allowing the clear separation of larvae fed on artificial diet when compared to those fed on natural diets (Fig. 1A). The
Discussion
The metabolic profile of the FAW larvae midgut is largely influenced by the food source used, and the two strains differ in every food source analyzed. Our data demonstrates the RS and CS interact differently with the substrate on which they are feeding, potentially due to differential metabolism of plant chemistry (Silva-Brandão et al., 2017). Differences at the genomic level are reported for these strains (Dumas et al., 2015), particularly with the large variation they have in the number of
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
We are grateful to the technician at the Insect Interactions Laboratory, Marcele Coelho for her help in the rearing of S. frugiperda and the host plant management. We also thank the technicians of the multi-User Proteomics, Metabolomics and Lipidomics laboratory, Thais Cataldi and Monica Labate for their help with the gas chromatography and mass spectrometry analyses
Funding
This work was supported by the São Paulo Research Foundation (FAPESP) [process 2011/50877-0]; the Ministry of Science, Technology and Innovation (Conselho Nacional de Desenvolvimento Científico e Tecnológico – CNPq [process 462140-2014/8]; and the FAPESP for the PhD student fellowship [2017/24377-7] provided to the first author. This manuscript is one of the chapters of the PhD Dissertation of the first author.
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