Next Article in Journal
Does Plant Size Influence Leaf Elements in an Arborescent Cycad?
Previous Article in Journal
Therapeutic Approaches Targeting Inflammation in Cardiovascular Disorders
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Meta-Omics Tools in the World of Insect-Microorganism Interactions

Antonino Malacrinò
Department of Physics, Chemistry and Biology (IFM), Linköping University, 58183 Linköping, Sweden
Biology 2018, 7(4), 50;
Submission received: 25 October 2018 / Revised: 16 November 2018 / Accepted: 22 November 2018 / Published: 27 November 2018


Microorganisms are able to influence several aspects of insects’ life, and this statement is gaining increasing strength, as research demonstrates it daily. At the same time, new sequencing technologies are now available at a lower cost per base, and bioinformatic procedures are becoming more user-friendly. This is triggering a huge effort in studying the microbial diversity associated to insects, and especially to economically important insect pests. The importance of the microbiome has been widely acknowledged for a wide range of animals, and also for insects this topic is gaining considerable importance. In addition to bacterial-associates, the insect-associated fungal communities are also gaining attention, especially those including plant pathogens. The use of meta-omics tools is not restricted to the description of the microbial world, but it can be also used in bio-surveillance, food safety assessment, or even to bring novelties to the industry. This mini-review aims to give a wide overview of how meta-omics tools are fostering advances in research on insect-microorganism interactions.

1. Introduction

It is widely acknowledged that microorganisms are the main drivers of several fundamental physical, chemical and biological phenomena [1]. As scientific techniques and instruments evolve, the role of microorganisms in shaping the lifestyle of other organisms becomes clearer. Indeed, the study of microbial ecology is widely spreading all around the scientific community, starting from human microbiology and expanding within other research topics, from marine ecology to food science and insect science [2,3,4].
Insect-associated microbial communities are attracting increasing interest, mainly because of their ecological and economical importance. Microorganisms have been investigated for the effects on their host partner, by directly mediating interactions with other species, or indirectly by impacting the host genetic diversity, with effects visible at community level [5]. Moreover, microorganisms can help insects to counteract plant defenses, provide protection from natural enemies, influence the reproductive system [5] and help to thrive on nutritionally marginal diets. These are just few examples of the plasticity of insect-microorganisms relationship.
More recently, the study of host-microorganism interactions has been boosted by the introduction of meta-omics techniques. A wide range of research described the insect-associated microbial community using meta-omics tools, spanning several host taxa: Pentalonia nigronervosa (Coquerel) [6], Lutzomyia longipalpis (Lutz & Neiva) [7], Lutzomyia intermedia (Lutz & Neiva) [8], Rhynchophorus ferrugineus (Olivier) [9], Apis mellifera L. [10,11], Formica exsecta Nylander [12] and Dendroctonus bark beetles [13] to name a few. This mini-review briefly introduces the techniques and tools currently available and highlights some of the developments these techniques are facilitating in insect science.

2. Meta-Omics Techniques: An Overview

Peršoh [14] provides a thorough overview of the development of the term ‘meta-omics’, which currently indicates a defined group of techniques used to characterize communities of organisms: meta-genomics, meta-transcriptomics, meta-proteomics and metabolomics. Metagenomics tools are helpful to identify the pool of genomes in a sample. This term has been used for the first time by Handelsman et al. [15] to indicate the set of microbial genomes in a soil sample. Indeed, the metagenome represents the complex of the genomes of all the organisms that are present in a specific sample. A more targeted version of metagenomics, called metabarcoding [16], aims at the taxonomical reconstruction of biological communities in a specific sample, using a short nucleotide fragment called barcode (e.g., 16S, 18S, ITS, COI) as proxy for identification. Both metagenomic and metabarcoding approaches are useful to qualitatively evaluate the diversity of organisms in a sample, but also to inform on the relative taxonomical abundance and on the presence of specific genes in that sample. However, these approaches just tell us who is in there (taxonomic reconstruction), and what potentially is doing (gene identification). The likely functional role of reconstructed communities can be predicted using tools like PICRUSt [17] for bacteria, or FUNGuild for fungi [18].
A metatranscriptomic approach can also be used for taxonomical reconstruction, provided that we have enough information on transcriptomes, but also tells us which genes the community is expressing in that sample [19]. Therefore, in addition to knowing who is inhabiting our sample, we are able to get information on which biological processes are active. However, changes in gene expression are not always followed by phenotypical responses, so meta-proteomics and metabolomics can help us to understand what the microbial community is actually doing [19,20,21].

3. Insect Pests

The intensification of agriculture and the simplification of agroecosystems heightened the damage to crops by pests. To counteract these losses, farmers have applied physical, biological and chemical measures. Synthetic pesticides boosted agricultural production with their striking effects, and the practice quickly became widely used worldwide. Besides the beneficial effects on agriculture, we are fully aware of the health and environmental impact of pesticides [22].
Microorganisms are key actor in pests’ life and interaction with the environment, and their manipulation could mitigate the impact of pests on plant productivity, thus leading to a reduction of chemical inputs. Indeed, microbes are able to affect insects’ fitness, to improve their resistance to stress and to affect gene flow [23,24]. The psyllid Bactericerca cockerelli (Sulc) that exploits its symbiont to modulate plant defensive gene expression [25], bark and ambrosia beetles that exploit fungi for dietary needs but also to overcome plant defenses [26], and the maintenance of leaf green islands by symbiotic bacteria which is fundamental for leaf miners’ survival [27] are just few examples of the plasticity of these interactions [28].
Meta-omics techniques revealed useful information in this field. Sequencing of diamondback moth (Plutella xylostella L.) metagenome, together with the functional profile of its microbiota, revealed a major role of bacterial taxa in the adaptation to detoxify plant defense compounds [29]. Saha et al. [30] used metagenomics to study the endosymbiont diversity of Diaphorina citri Kuwayama, vector of Candidatus Liberibacter asiaticus know causal agent of citrus greening disease. A metatranscriptomic approach has been used by Cox-Foster et al. [31] to investigate the causal agent of colony collapse disorder in honeybees, suggesting a correlation between the disease and the detection of Israeli acute paralysis virus. A similar approach allowed for the identification of candidate pathogens responsible for the decline of the invasive yellow crazy ant (Anoplolepis gracilipes Smith) in Australia, providing pivotal information that can be developed into pest management programs [32].
The use of meta-omics techniques to unveil the diversity of viruses spreading different ecosystems is providing more insights into the ecological importance of viruses [33,34,35] and can serve as basis to enhance future biocontrol programs. For example, a metagenomics approach has been used to study the viral community of different species of mosquitoes, revealing a very diverse community of animal, plant, insect and bacterial viruses [36].

4. Gut Microbes

Microorganisms are able to play a fundamental role in insects’ nutrition, sometimes in a symbiotic way, increasing the availability several nutrients or regulating their allocation [37]. Evolution led insects to exploit microorganisms in order to adapt to nutrient-limited environments, feeding directly on them or relying on them to pre-digest refractory diets [38]. These microbial symbionts could be vertically transferred from one generation to the next, horizontally among individuals of the same species, but also can be acquired directly from the environment [39,40]. Sometimes, this association is so strict that experimental replacement of gut microbial communities in insect herbivores can lead to a decrease in their performance [39]. An important role of gut microbial community is performed by detoxifying plant secondary metabolites and by helping their host to better exploit their diet [41]. The analysis of the gut microbiota of Hyles euphorbiae (L.) and Brithys crini (F.), for example, revealed a community dominated by Enterococcus, with a likely role in helping these insects to feed on toxic plants [42].
More recent studies highlighted the effects of host plant species on the microbiota associated with polyphagous species. This has been observed to occur, for example, in Acyrthosiphon pisum Harris [43], Helicoverpa armigera (Hübner) [44], Phylloxera notabilis Pergande [23], Melitaea cinxia (L.) [45], Thaumetopoea pytiocampa (Denis & Schiff.) [46] and Ceratitis capitata (Wied.) [47]. It has been argued, therefore, that the main drivers that shape insect gut microbial community are diet, life stage and environment [47,48]. It has been also shown that the microbial communities of insects reared on artificial diet are different from the microbiota associated with individuals from field [49].

5. Fungal Microbiota

Mutualistic insect-fungus associations occur among different taxa and with different strategies: bark beetles and ambrosia beetles, fungus farming ants, termites, wood wasps and gall midges are just few examples of the diversity of these associations [50,51,52]. Bark and ambrosia beetles are widely known for their strict association with fungal symbionts. Previous work has investigated the fungal community associated with bark and ambrosia beetles trapped at international harbors, revealing their association with plant pathogens and unknown fungi that they can potentially spread worldwide through the network of wood-products transportation [53]. On the other hand, there are cases of antagonistic relationships between insect and fungi, as it occurs for entomopathogenic fungi such as Beauveria spp. and Metarhizium spp. [54]. These, and other fungi, are widely employed as biocontrol agents against insect pests [55,56]. Further interactions include also peculiar multitrophic relationships, like the ability of Metarhizium robertsii to transfer nitrogen from infected larvae of Galleria mellonella (L.) to plants [57], phenomenon similarly reported for the ectomycorrhizal fungus Laccaria bicolor in white pine [58]. Gene horizontal transfer between the two kingdoms has been reported in aphids [59]. It has been also shown how plant-fungus interaction can help to contrast the negative effects of herbivores [60].
However, few studied focused on studying the entire fungal microbiota associated with insects, and to date collected information are restricted to Collembola [61], Lepidoptera [62], Coleoptera [63,64,65] and Diptera [66]. DNA metabarcoding allowed the analysis of the fungal microbiota of Bactrocera oleae (Rossi), a major pest of olive groves, revealing its association with fungal species of the genus Colletotrichum, agents of the devastating olive antrachnosis [67,68]. More work has been done to better understand the symbiotic relationship between ambrosia beetles and their fungal associates [63,64,69,70].

6. Applied Perspectives

Meta-omics pipelines are becoming increasingly accessible to a wider range of users, both in terms of cost and skills required for data analyses. These technologies have a wide range of applications, that fall beyond the study of the insects’ microbial ecology.
The study of microbial diversity associated with insects, besides to inform on biodiversity and to enhance biocontrol programs, can be source of information for the industry sector. For example, Warnecke et al. [71] used a low-throughput metagenomic analysis to study the function of hindgut microbiota in the termite Nasutitermes ephratae (Holmgren), revealing a wide presence of genes responsible of cellulose and xylan hydrolysis. It has been also shown that protistan communities hosted by Coptotermes formosanus Shiraki and Reticulitermes flaviceps (Oshima) are responsible to positively influence lignocellulolytic system, thus enhancing insects’ nutrition [72,73]. The use of the microbiota associated with wood-feeding beetles as source of novel enzymes, potentially useful in industrial bioprocesses, has been investigated by different studies [74,75,76,77,78,79]. Suen et al. [80] surveyed the microbial community of the leaf-cutter ants’ garden, suggesting a role of bacteria in concert with the fungus in degrading cellulose. Krishnan et al. [81] provided a review of the potential application of insect’s gut microbiome hosting bacteria with genes useful in cellulose hydrolysis, vitamin production, nitrogen fixation and many more. Indeed, the ability to isolate very specific information from the genes expressed by an entire community can be helpful in identifying candidate genes, and thus enzymes, that can improve the reliability and efficiency of industrial processes or introduce novel features [82].
Meta-omics techniques can be used to improve bio-surveillance programs, as tools to detect the arrival, origin, invasion pathways and adaptation traits of invasive species [83]. In particular, the monitoring of critical areas (e.g., ports of entry) through massive trapping is a common practice to identify the arrival of invasive insect species [84,85,86]. This process is time consuming and often requires extensive taxonomic knowledge of different systematic groups. However, the meta-omics toolkit can help to expedite this process by analyzing the entire genetic pool of single traps, and detecting not only the arrival of invasive insect species, but also likely plant pathogens [53,83,87].
Furthermore, insect-microorganism relationships could be manipulated to improve pest control, by decreasing pests’ fitness or by increasing the efficacy of pest management programs. One possible target are bacteria protecting insects from natural enemies (e.g., Hamiltonella defensa, Regiella insecticola), excluding parasitoids from the host and/or producing secondary metabolites that complete insect’s immune system [88,89]. Further research in this direction can provide striking results to enhance biocontrol programs.
DNA metabarcoding has been shown to be useful in the identification of botanical and entomological sources of honey, a valuable product subject to fraud [90]. Improvements in DNA extraction from honey, and the automatization of the procedure, together with the classification technique based on neural networks and machine learning, can lead the development of novel anti-fraud platforms.

7. Conclusions

We are now able to truly explore and finely investigate the insects’ microbial communities and their interaction with their hosts. Therefore, modern molecular technologies and bioinformatic tools could represent an alternative way to protect our crops, forests and ecosystems, while preserving health and environment. This is especially true, since our current climatic and social challenges are triggering radical changes in the way we approach plant protection. We are assisting to increased efforts in exploiting genomic technologies to retrieve pivotal information that can effectively be used in contrasting insect pests. Technologies and protocols are constantly improving, procedure automation is becoming increasingly common, and modern bioinformatic and machine learning workflows are helping the transition from conventional approaches. This is done by decreasing both the time-to-results and the range of skills required to correctly process samples and retrieve meaningful information. Therefore, it is now time to climb through the looking glass, search beyond the lab bench, and push our research efforts into real-world situations.


This research received no external funding.

Conflicts of Interest

I declare no conflict of interest.


  1. Prosser, J.I. Dispersing misconceptions and identifying opportunities for the use of “omics” in soil microbial ecology. Nat. Rev. Microbiol. 2015, 13, 439–446. [Google Scholar] [CrossRef] [PubMed]
  2. Douglas, A.E. The microbial dimension in insect nutritional ecology. Funct. Ecol. 2009, 23, 38–47. [Google Scholar] [CrossRef] [Green Version]
  3. Ercolini, D. High-throughput sequencing and metagenomics: Moving forward in the culture-independent analysis of food microbial ecology. Appl. Environ. Microbiol. 2013, 79, 3148–3155. [Google Scholar] [CrossRef] [PubMed]
  4. Huber, J.A.; Mark Welch, D.B.; Morrison, H.G.; Huse, S.M.; Neal, P.R.; Butterfield, D.A.; Sogin, M.L. Microbial population structures in the deep marine biosphere. Science 2007, 318, 97–100. [Google Scholar] [CrossRef] [PubMed]
  5. Ferrari, J.; Vavre, F. Bacterial symbionts in insects or the story of communities affecting communities. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 2011, 366, 1389–1400. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Peršoh, D. Plant-associated fungal communities in the light of meta’omics. Fungal Divers. 2015, 75, 1–25. [Google Scholar] [CrossRef]
  7. Handelsman, J.; Rondon, M.R.; Brady, S.F.; Clardy, J.; Goodman, R.M. Molecular biological access to the chemistry of unknown soil microbes: A new frontier for natural products. Chem. Biol. 1998, 5, R245–R249. [Google Scholar] [CrossRef]
  8. Abdelfattah, A.; Malacrinò, A.; Wisniewski, M.; Cacciola, S.O.; Schena, L. Metabarcoding: A powerful tool to investigate microbial communities and shape future plant protection strategies. Biol. Control 2018, 120, 1–10. [Google Scholar] [CrossRef]
  9. Langille, M.G.I.; Zaneveld, J.; Caporaso, J.G.; McDonald, D.; Knights, D.; Reyes, J.A.; Clemente, J.C.; Burkepile, D.E.; Vega Thurber, R.L.; Knight, R.; et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 2013, 31, 814–821. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  10. Nguyen, N.H.; Song, Z.; Bates, S.T.; Branco, S.; Tedersoo, L.; Menke, J.; Schilling, J.S.; Kennedy, P.G. FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 2016, 20, 241–248. [Google Scholar] [CrossRef]
  11. Shi, W.; Syrenne, R.; Sun, J.-Z.; Yuan, J.S. Molecular approaches to study the insect gut symbiotic microbiota at the ‘omics’ age. Insect Sci. 2010, 17, 199–219. [Google Scholar] [CrossRef]
  12. Turnbaugh, P.J.; Gordon, J.I. An Invitation to the Marriage of Metagenomics and Metabolomics. Cell 2008, 134, 708–713. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Gotelli, N.J.; Ellison, A.M.; Ballif, B.A. Environmental proteomics, biodiversity statistics and food-web structure. Trends Ecol. Evol. 2012, 27, 436–442. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. De Clerck, C.; Fujiwara, A.; Joncour, P.; Léonard, S.; Félix, M.-L.; Francis, F.; Jijakli, M.H.; Tsuchida, T.; Massart, S. A metagenomic approach from aphid’s hemolymph sheds light on the potential roles of co-existing endosymbionts. Microbiome 2015, 3, 63. [Google Scholar] [CrossRef] [PubMed]
  15. McCarthy, C.B.; Diambra, L.A.; Rivera Pomar, R.V. Metagenomic analysis of taxa associated with Lutzomyia longipalpis, vector of visceral leishmaniasis, using an unbiased high-throughput approach. PLoS Negl. Trop. Dis. 2011, 5, e1304. [Google Scholar] [CrossRef] [PubMed]
  16. Monteiro, C.C.; Villegas, L.E.M.; Campolina, T.B.; Pires, A.C.M.A.; Miranda, J.C.; Pimenta, P.F.P.; Secundino, N.F.C. Bacterial diversity of the American sand fly Lutzomyia intermedia using high-throughput metagenomic sequencing. Parasit. Vectors 2016, 9, 480. [Google Scholar] [CrossRef] [PubMed]
  17. Jia, S.; Zhang, X.; Zhang, G.; Yin, A.; Zhang, S.; Li, F.; Wang, L.; Zhao, D.; Yun, Q.; Tala; et al. Seasonally variable intestinal metagenomes of the red palm weevil (Rhynchophorus ferrugineus). Environ. Microbiol. 2013, 15. [Google Scholar] [CrossRef] [PubMed]
  18. Engel, P.; Martinson, V.G.; Moran, N.A. Functional diversity within the simple gut microbiota of the honey bee. Proc. Natl. Acad. Sci. USA 2012, 109, 11002–11007. [Google Scholar] [CrossRef] [PubMed]
  19. Tozkar, C.Ã.; Kence, M.; Kence, A.; Huang, Q.; Evans, J.D. Metatranscriptomic analyses of honey bee colonies. Front. Genet. 2015, 6, 100. [Google Scholar] [CrossRef] [PubMed]
  20. Johansson, H.; Dhaygude, K.; Lindström, S.; Helanterä, H.; Sundström, L.; Trontti, K. A metatranscriptomic approach to the identification of microbiota associated with the ant Formica exsecta. PLoS ONE 2013, 8, e79777. [Google Scholar] [CrossRef] [PubMed]
  21. Hernández-García, J.A.; Briones-Roblero, C.I.; Rivera-Orduña, F.N.; Zúñiga, G. Revealing the gut bacteriome of Dendroctonus bark beetles (Curculionidae: Scolytinae): Diversity, core members and co-evolutionary patterns. Sci. Rep. 2017, 7, 13864. [Google Scholar] [CrossRef] [PubMed]
  22. Chagnon, M.; Kreutzweiser, D.; Mitchell, E.A.D.; Morrissey, C.A.; Noome, D.A.; Van der Sluijs, J.P. Risks of large-scale use of systemic insecticides to ecosystem functioning and services. Environ. Sci. Pollut. Res. 2015, 22, 119–134. [Google Scholar] [CrossRef] [PubMed]
  23. Medina, R.F.; Nachappa, P.; Tamborindeguy, C. Differences in bacterial diversity of host-associated populations of Phylloxera notabilis Pergande (Hemiptera: Phylloxeridae) in pecan and water hickory. J. Evol. Biol. 2011, 24, 761–771. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Shin, S.C.; Kim, S.-H.; You, H.; Kim, B.; Kim, A.C.; Lee, K.-A.; Yoon, J.-H.; Ryu, J.-H.; Lee, W.-J. Drosophila microbiome modulates host developmental and metabolic homeostasis via insulin signaling. Science 2011, 334, 670–674. [Google Scholar] [CrossRef] [PubMed]
  25. Casteel, C.L.; Hansen, A.K.; Walling, L.L.; Paine, T.D. Manipulation of plant defense responses by the tomato psyllid (Bactericerca cockerelli) and its associated endosymbiont Candidatus Liberibacter psyllaurous. PLoS ONE 2012, 7, e35191. [Google Scholar] [CrossRef]
  26. Paine, T.D.; Raffa, K.F.; Harrington, T.C. Interactions among scolytid bark beetles, their associated fungi, and live host conifers. Annu. Rev. Entomol. 1997, 42, 179–206. [Google Scholar] [CrossRef] [PubMed]
  27. Kaiser, W.; Huguet, E.; Casas, J.; Commin, C.; Giron, D. Plant green-island phenotype induced by leaf-miners is mediated by bacterial symbionts. Proceedings. Biol. Sci. 2010, 277, 2311–2319. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Frago, E.; Dicke, M.; Godfray, H.C.J. Insect symbionts as hidden players in insect-plant interactions. Trends Ecol. Evol. 2012, 27, 705–711. [Google Scholar] [CrossRef] [PubMed]
  29. Xia, X.; Gurr, G.M.; Vasseur, L.; Zheng, D.; Zhong, H.; Qin, B.; Lin, J.; Wang, Y.; Song, F.; Li, Y.; et al. Metagenomic sequencing of diamondback moth gut microbiome unveils key holobiont adaptations for herbivory. Front. Microbiol. 2017, 8, 663. [Google Scholar] [CrossRef] [PubMed]
  30. Saha, S.; Hunter, W.B.; Reese, J.; Morgan, J.K.; Marutani-Hert, M.; Huang, H.; Lindeberg, M. Survey of endosymbionts in the Diaphorina citri metagenome and assembly of a Wolbachia wDi draft genome. PLoS ONE 2012, 7, e50067. [Google Scholar] [CrossRef] [PubMed]
  31. Cox-Foster, D.L.; Conlan, S.; Holmes, E.C.; Palacios, G.; Evans, J.D.; Moran, N.A.; Quan, P.-L.; Briese, T.; Hornig, M.; Geiser, D.M.; et al. A metagenomic survey of microbes in honey bee colony collapse disorder. Science 2007, 318, 283–287. [Google Scholar] [CrossRef] [PubMed]
  32. Cooling, M.; Gruber, M.A.M.; Hoffmann, B.D.; Sébastien, A.; Lester, P.J. A metatranscriptomic survey of the invasive yellow crazy ant, Anoplolepis gracilipes, identifies several potential viral and bacterial pathogens and mutualists. Insectes Soc. 2017, 64, 197–207. [Google Scholar] [CrossRef]
  33. Dayaram, A.; Galatowitsch, M.; Harding, J.S.; Argüello-Astorga, G.R.; Varsani, A. Novel circular DNA viruses identified in Procordulia grayi and Xanthocnemis zealandica larvae using metagenomic approaches. Infect. Genet. Evol. 2014, 22, 134–141. [Google Scholar] [CrossRef] [PubMed]
  34. Valles, S.M.; Oi, D.H.; Yu, F.; Tan, X.-X.; Buss, E.A. Metatranscriptomics and pyrosequencing facilitate discovery of potential viral natural enemies of the invasive caribbean crazy ant, Nylanderia pubens. PLoS ONE 2012, 7, e31828. [Google Scholar] [CrossRef] [PubMed]
  35. Shi, M.; Neville, P.; Nicholson, J.; Eden, J.-S.; Imrie, A.; Holmes, E.C. High-Resolution metatranscriptomics reveals the ecological dynamics of mosquito-associated RNA viruses in Western Australia. J. Virol. 2017, 91, e00680-17. [Google Scholar] [CrossRef] [PubMed]
  36. Ng, T.F.F.; Willner, D.L.; Lim, Y.W.; Schmieder, R.; Chau, B.; Nilsson, C.; Anthony, S.; Ruan, Y.; Rohwer, F.; Breitbart, M. Broad surveys of DNA viral diversity obtained through viral metagenomics of mosquitoes. PLoS ONE 2011, 6, e20579. [Google Scholar] [CrossRef] [PubMed]
  37. Douglas, A.E. The molecular basis of bacterial–insect symbiosis. J. Mol. Biol. 2014, 426, 3830–3837. [Google Scholar] [CrossRef] [PubMed]
  38. Engel, P.; Moran, N.A. The gut microbiota of insects–diversity in structure and function. FEMS Microbiol. Rev. 2013, 37, 699–735. [Google Scholar] [CrossRef] [PubMed]
  39. Hosokawa, T.; Kikuchi, Y.; Shimada, M.; Fukatsu, T. Obligate symbiont involved in pest status of host insect. Proceedings. Biol. Sci. 2007, 274, 1979–1984. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Kikuchi, Y.; Hosokawa, T.; Fukatsu, T. Insect-microbe mutualism without vertical transmission: A stinkbug acquires a beneficial gut symbiont from the environment every generation. Appl. Environ. Microbiol. 2007, 73, 4308–4316. [Google Scholar] [CrossRef] [PubMed]
  41. Berasategui, A.; Salem, H.; Paetz, C.; Santoro, M.; Gershenzon, J.; Kaltenpoth, M.; Schmidt, A. Gut microbiota of the pine weevil degrades conifer diterpenes and increases insect fitness. Mol. Ecol. 2017, 26, 4099–4110. [Google Scholar] [CrossRef] [PubMed]
  42. Vilanova, C.; Baixeras, J.; Latorre, A.; Porcar, M. The generalist inside the specialist: Gut bacterial communities of two insect species feeding on toxic plants are dominated by Enterococcus sp. Front. Microbiol. 2016, 7, 1005. [Google Scholar] [CrossRef] [PubMed]
  43. Gauthier, J.-P.; Outreman, Y.; Mieuzet, L.; Simon, J.-C. Bacterial Communities associated with host-adapted populations of pea aphids revealed by deep sequencing of 16S ribosomal DNA. PLoS ONE 2015, 10, e0120664. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Gayatri Priya, N.; Ojha, A.; Kajla, M.K.; Raj, A.; Rajagopal, R. Host plant induced variation in gut bacteria of Helicoverpa armigera. PLoS ONE 2012, 7, e30768. [Google Scholar] [CrossRef] [PubMed]
  45. Ruokolainen, L.; Ikonen, S.; Makkonen, H.; Hanski, I. Larval growth rate is associated with the composition of the gut microbiota in the Glanville fritillary butterfly. Oecologia 2016, 181, 895–903. [Google Scholar] [CrossRef] [PubMed]
  46. Strano, C.P.; Malacrinò, A.; Campolo, O.; Palmeri, V. Influence of Host Plant on Thaumetopoea pityocampa Gut Bacterial Community. Microb. Ecol. 2018, 75, 487–494. [Google Scholar] [CrossRef] [PubMed]
  47. Malacrinò, A.; Campolo, O.; Medina, R.F.; Palmeri, V. Instar-and host-associated differentiation of bacterial communities in the Mediterranean fruit fly Ceratitis capitata. PLoS ONE 2018, 13, e0194131. [Google Scholar] [CrossRef] [PubMed]
  48. Colman, D.R.; Toolson, E.C.; Takacs-Vesbach, C.D. Do diet and taxonomy influence insect gut bacterial communities? Mol. Ecol. 2012, 21, 5124–5137. [Google Scholar] [CrossRef] [PubMed]
  49. Belda, E.; Pedrola, L.; Peretó, J.; Martínez-Blanch, J.F.; Montagud, A.; Navarro, E.; Urchueguía, J.; Ramón, D.; Moya, A.; Porcar, M. Microbial diversity in the midguts of field and lab-reared populations of the European corn borer Ostrinia nubilalis. PLoS ONE 2011, 6, e21751. [Google Scholar] [CrossRef] [PubMed]
  50. Kellner, K.; Fernández-Marín, H.; Ishak, H.D.; Sen, R.; Linksvayer, T.A.; Mueller, U.G. Co-evolutionary patterns and diversification of ant-fungus associations in the asexual fungus-farming ant Mycocepurus smithii in Panama. J. Evol. Biol. 2013, 26, 1353–1362. [Google Scholar] [CrossRef] [PubMed]
  51. Six, D.L. Ecological and evolutionary determinants of bark beetle-fungus symbioses. Insects 2012, 3, 339–366. [Google Scholar] [CrossRef] [PubMed]
  52. Janson, E.M.; Peeden, E.R.; Stireman, J.O.; Abbot, P. Symbiont-mediated phenotypic variation without co-evolution in an insect-fungus association. J. Evol. Biol. 2010, 23, 2212–2228. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Malacrinò, A.; Rassati, D.; Schena, L.; Mehzabin, R.; Battisti, A.; Palmeri, V. Fungal communities associated with bark and ambrosia beetles trapped at international harbours. Fungal Ecol. 2017, 28, 44–52. [Google Scholar] [CrossRef]
  54. Shah, P.A.; Pell, J.K. Entomopathogenic fungi as biological control agents. Appl. Microbiol. Biotechnol. 2003, 61, 413–423. [Google Scholar] [CrossRef] [PubMed]
  55. Ruiu, L. Microbial biopesticides in agroecosystems. Agron. J. 2018, 8, 235. [Google Scholar] [CrossRef]
  56. Ruiu, L. Insect Pathogenic Bacteria in Integrated Pest Management. Insects 2015, 6, 352–367. [Google Scholar] [CrossRef] [PubMed]
  57. Behie, S.W.; Zelisko, P.M.; Bidochka, M.J. Endophytic insect-parasitic fungi translocate nitrogen directly from insects to plants. Science 2012, 336, 1576–1577. [Google Scholar] [CrossRef] [PubMed]
  58. Klironomos, J.N.; Hart, M.M. Food-web dynamics: Animal nitrogen swap for plant carbon. Nature 2001, 410, 651–652. [Google Scholar] [CrossRef] [PubMed]
  59. Moran, N.A.; Jarvik, T. Lateral transfer of genes from fungi underlies carotenoid production in aphids. Science 2010, 328, 624–627. [Google Scholar] [CrossRef] [PubMed]
  60. Bennett, A.E.; Orrell, P.; Malacrino, A.; Pozo, M.J.; Bennett, A.E.; Orrell, P.; Malacrino, A.; Pozo, M.J. Fungal-Mediated Above-Belowground Interactions: The Community Approach, Stability, Evolution, Mechanisms, and Applications. In Aboveground–Belowground Community Ecology; Ohgushi, T., Johnson, S.N., Wurst, S., Eds.; Springer: Berlin, Germany, 2018; pp. 85–116. [Google Scholar]
  61. Anslan, S.; Bahram, M.; Tedersoo, L. Temporal changes in fungal communities associated with guts and appendages of Collembola as based on culturing and high-throughput sequencing. Soil Biol. Biochem. 2016, 96, 152–159. [Google Scholar] [CrossRef]
  62. Harrison, J.G.; Urruty, D.M.; Forister, M.L. An exploration of the fungal assemblage in each life history stage of the butterfly, Lycaeides melissa (Lycaenidae), as well as its host plant Astragalus canadensis (Fabaceae). Fungal Ecol. 2016, 22, 10–16. [Google Scholar] [CrossRef]
  63. Miller, K.E.; Hopkins, K.; Inward, D.J.G.; Vogler, A.P. Metabarcoding of fungal communities associated with bark beetles. Ecol. Evol. 2016, 6, 1590–1600. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Kostovcik, M.; Bateman, C.C.; Kolarik, M.; Stelinski, L.L.; Jordal, B.H.; Hulcr, J. The ambrosia symbiosis is specific in some species and promiscuous in others: Evidence from community pyrosequencing. ISME J. 2015, 9, 126–138. [Google Scholar] [CrossRef] [PubMed]
  65. Kaltenpoth, M.; Steiger, S. Unearthing carrion beetles’ microbiome: Characterization of bacterial and fungal hindgut communities across the Silphidae. Mol. Ecol. 2014, 23, 1251–1267. [Google Scholar] [CrossRef] [PubMed]
  66. Chandler, J.A.; Eisen, J.A.; Kopp, A. Yeast communities of diverse Drosophila species: Comparison of two symbiont groups in the same hosts. Appl. Environ. Microbiol. 2012, 78, 7327–7336. [Google Scholar] [CrossRef] [PubMed]
  67. Malacrinò, A.; Schena, L.; Campolo, O.; Laudani, F.; Palmeri, V. Molecular analysis of the fungal microbiome associated with the olive fruit fly Bactrocera oleae. Fungal Ecol. 2015, 18, 67–74. [Google Scholar] [CrossRef]
  68. Malacrinò, A.; Schena, L.; Campolo, O.; Laudani, F.; Mosca, S.; Giunti, G.; Strano, C.P.; Palmeri, V. A Metabarcoding Survey on the Fungal Microbiota Associated to the Olive Fruit Fly. Microb. Ecol. 2017, 73, 677–684. [Google Scholar] [CrossRef] [PubMed]
  69. You, L.; Simmons, D.R.; Bateman, C.C.; Short, D.P.G.; Kasson, M.T.; Rabaglia, R.J.; Hulcr, J. New fungus-insect symbiosis: Culturing, molecular, and histological methods determine saprophytic polyporales mutualists of Ambrosiodmus ambrosia beetles. PLoS ONE 2015, 10, e0137689. [Google Scholar] [CrossRef] [PubMed]
  70. Hulcr, J.; Rountree, N.R.; Diamond, S.E.; Stelinski, L.L.; Fierer, N.; Dunn, R.R. Mycangia of ambrosia beetles host communities of bacteria. Microb. Ecol. 2012, 64, 784–793. [Google Scholar] [CrossRef] [PubMed]
  71. Warnecke, F.; Luginbühl, P.; Ivanova, N.; Ghassemian, M.; Richardson, T.H.; Stege, J.T.; Cayouette, M.; McHardy, A.C.; Djordjevic, G.; Aboushadi, N.; et al. Metagenomic and functional analysis of hindgut microbiota of a wood-feeding higher termite. Nature 2007, 450, 560–565. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  72. Liu, X.-J.; Che, M.; Xie, L.; Zhan, S.; Zhou, Z.-H.; Huang, Y.-P.; Wang, Q. Metatranscriptome of the protistan community in Reticulitermes flaviceps. Insect Sci. 2016, 23, 543–547. [Google Scholar] [CrossRef] [PubMed]
  73. Xie, L.; Zhang, L.; Zhong, Y.; Liu, N.; Long, Y.; Wang, S.; Zhou, X.; Zhou, Z.; Huang, Y.; Wang, Q. Profiling the metatranscriptome of the protistan community in Coptotermes formosanus with emphasis on the lignocellulolytic system. Genomics 2012, 99, 246–255. [Google Scholar] [CrossRef] [PubMed]
  74. Scully, E.D.; Geib, S.M.; Hoover, K.; Tien, M.; Tringe, S.G.; Barry, K.W.; Glavina del Rio, T.; Chovatia, M.; Herr, J.R.; Carlson, J.E. Metagenomic profiling reveals lignocellulose degrading system in a microbial community associated with a wood-feeding beetle. PLoS ONE 2013, 8, e73827. [Google Scholar] [CrossRef] [PubMed]
  75. Shi, W.; Xie, S.; Chen, X.; Sun, S.; Zhou, X.; Liu, L.; Gao, P.; Kyrpides, N.C.; No, E.-G.; Yuan, J.S. Comparative genomic analysis of the endosymbionts of herbivorous insects reveals eco-environmental adaptations: Biotechnology applications. PLoS Genet. 2013, 9, e1003131. [Google Scholar] [CrossRef]
  76. Li, L.-L.; McCorkle, S.R.; Monchy, S.; Taghavi, S.; van der Lelie, D. Bioprospecting metagenomes: Glycosyl hydrolases for converting biomass. Biotechnol. Biofuels 2009, 2, 10. [Google Scholar] [CrossRef] [PubMed]
  77. Liu, N.; Zhang, L.; Zhou, H.; Zhang, M.; Yan, X.; Wang, Q.; Long, Y.; Xie, L.; Wang, S.; Huang, Y.; et al. Metagenomic insights into metabolic capacities of the gut microbiota in a fungus-cultivating termite (Odontotermes yunnanensis). PLoS ONE 2013, 8, e69184. [Google Scholar] [CrossRef] [PubMed]
  78. He, S.; Ivanova, N.; Kirton, E.; Allgaier, M.; Bergin, C.; Scheffrahn, R.H.; Kyrpides, N.C.; Warnecke, F.; Tringe, S.G.; Hugenholtz, P. Comparative metagenomic and metatranscriptomic analysis of hindgut paunch microbiota in wood-and dung-feeding higher termites. PLoS ONE 2013, 8, e61126. [Google Scholar] [CrossRef] [PubMed]
  79. Marynowska, M.; Goux, X.; Sillam-Dussès, D.; Rouland-Lefèvre, C.; Roisin, Y.; Delfosse, P.; Calusinska, M. Optimization of a metatranscriptomic approach to study the lignocellulolytic potential of the higher termite gut microbiome. BMC Genomics 2017, 18, 681. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  80. Suen, G.; Scott, J.J.; Aylward, F.O.; Adams, S.M.; Tringe, S.G.; Pinto-Tomás, A.A.; Foster, C.E.; Pauly, M.; Weimer, P.J.; Barry, K.W.; et al. An insect herbivore microbiome with high plant biomass-degrading capacity. PLoS Genet. 2010, 6, e1001129. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  81. Krishnan, M.; Bharathiraja, C.; Pandiarajan, J.; Prasanna, V.A.; Rajendhran, J.; Gunasekaran, P. Insect gut microbiome—An unexploited reserve for biotechnological application. Asian Pac. J. Trop. Biomed. 2014, 4, S16–S21. [Google Scholar] [CrossRef] [PubMed]
  82. Schmeisser, C.; Steele, H.; Streit, W.R. Metagenomics, biotechnology with non-culturable microbes. Appl. Microbiol. Biotechnol. 2007, 75, 955–962. [Google Scholar] [CrossRef] [PubMed]
  83. Roe, A.D.; Torson, A.S.; Bilodeau, G.; Bilodeau, P.; Blackburn, G.S.; Cui, M.; Cusson, M.; Doucet, D.; Griess, V.C.; Lafond, V.; et al. Biosurveillance of forest insects: Part I—Integration and application of genomic tools to the surveillance of non-native forest insects. J. Pest Sci. 2018, 1–20. [Google Scholar] [CrossRef]
  84. Rassati, D.; Faccoli, M.; Petrucco Toffolo, E.; Battisti, A.; Marini, L. Improving the early detection of alien wood-boring beetles in ports and surrounding forests. J. Appl. Ecol. 2015, 52, 50–58. [Google Scholar] [CrossRef]
  85. Poland, T.M.; Rassati, D. Improved biosecurity surveillance of non-native forest insects: A review of current methods. J. Pest Sci. 2018, 1–13. [Google Scholar] [CrossRef]
  86. Rassati, D.; Faccoli, M.; Marini, L.; Haack, R.A.; Battisti, A.; Petrucco Toffolo, E. Exploring the role of wood waste landfills in early detection of non-native wood-boring beetles. J. Pest Sci. 2015, 88, 563–572. [Google Scholar] [CrossRef]
  87. Tremblay, É.D.; Kimoto, T.; Bérubé, J.A.; Bilodeau, G.J. Next-generation sequencing to investigate existing and new insect associations with phytopathogenic fungal propagules. 2018. [Google Scholar] [CrossRef]
  88. Degnan, P.H.; Yu, Y.; Sisneros, N.; Wing, R.A.; Moran, N.A. Hamiltonella defensa, genome evolution of protective bacterial endosymbiont from pathogenic ancestors. Proc. Natl. Acad. Sci. USA 2009, 106, 9063–9068. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  89. Scarborough, C.L.; Ferrari, J.; Godfray, H.C.J. Aphid protected from pathogen by endosymbiont. Science 2005, 310, 1781. [Google Scholar] [CrossRef] [PubMed]
  90. Prosser, S.W.J.; Hebert, P.D.N. Rapid identification of the botanical and entomological sources of honey using DNA metabarcoding. Food Chem. 2017, 214, 183–191. [Google Scholar] [CrossRef] [PubMed]

Share and Cite

MDPI and ACS Style

Malacrinò, A. Meta-Omics Tools in the World of Insect-Microorganism Interactions. Biology 2018, 7, 50.

AMA Style

Malacrinò A. Meta-Omics Tools in the World of Insect-Microorganism Interactions. Biology. 2018; 7(4):50.

Chicago/Turabian Style

Malacrinò, Antonino. 2018. "Meta-Omics Tools in the World of Insect-Microorganism Interactions" Biology 7, no. 4: 50.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop