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Multilevel interactions between microbes, insects and plants:
ecological and evolutionary constraints underlying interactions
PhD student:
Matteo Brunetti
Tutor: Franco Faoro
Co-tutor: Matteo Montagna
Insect-plant coevolutionary arm race
Futuyma & Agrawal 2009, Proc. Natl. Acad. Sci., 106(43), 18054-18061
Host plantsPhytophagous
insects
Erlich & Raven, 1964, Evolution, 586-608; Bernays & Graham, 1988, Ecology, 69(4), 886-892; Mitter et al., 1988, Am Nat,
107-128; Farrell et al., 1992, BioScience, 34-42; Karban & Agrawal, 2002, Annu. Rev. Ecol. Syst., 641-664; Futuyma &
Agrawal, 2009, Proc. Natl. Acad. Sci., 106(43), 18054-18061; Janz, 2011, Annu. Rev. Ecol. Evol. Syst., 42(1), 71; Ali &
Agrawal, 2012, Trends Plant Sci., 17(5), 293-302; Forister et al., 2012, Ecology, 93(5), 981-991
Matteo
BrunettiPhD students "Annual report day" - 25 September 2019
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mod. from Hansen & Moran (2014). Molecular ecology, 23(6), 1473-1496.
Moran et al., 2008, Annu. Rev. Genet., 42, 165-190
Clark et al., 2010, Protoplasma, 244(1-4), 25-51
Feldhaar, 2011, Ecol. Entomol., 36(5), 533-543
Frago et al., 2012, Trends ecol. evol., 27(12), 705-711
Guerrero et al., 2013, Int. Microbiol., 16(3), 133-143
Chung et al., 2013, Proc Natl A Sci, 110(39), 15728-15733
Zhu et al., 2014, New Phytologist, 204(2), 315-321
Hansen & Moran, 2014, Mol ecol, 23(6), 1473-1496
Douglas, 2015, Annu rev entomol, 60, 17-34
Mason et al., 2018, Plant Cell Environ, 0
Jones et al., 2019, eLife, 9(1), 2792
Traits mediated by microbial symbionts
in phytophagous insects
• Provide essential nutrients lacking in
insect diet (e.g. Buchnera
aphidicola)
• Digestion of plant macromolecules
(e.g. Termites fermentation
chambers)
• Detoxification (e.g. Dendroctonus
ponderosae)
• Suppress plant defences (e.g.
Liberibacter psyllaurous)
Matteo
BrunettiPhD students "Annual report day" - 25 September 2019
Microbiota of phytophagous insects
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Matteo
BrunettiPhD students "Annual report day" - 25 September 2019
Multilevel interactions
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~ 37,000 species (worldwide)
Matteo
BrunettiPhD students "Annual report day" - 25 September 2019
Leaf beetles (Coleoptera: Chrysomelidae)
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Bacterial communities associated to insects feeding on the same host
plant share common features, both in terms of taxonomic composition
and dominant metabolic pathways
Bacterial communities associated to insects feeding on plants with
common defense strategies (e.g. same toxic compounds) share common
features, both in terms of taxonomic composition and dominant
metabolic pathways
Bacterial communities hosted by polyphagous insect species are more
complex than those hosted by monophagous species.
[H1]
[H2]
[H3]
Matteo
BrunettiPhD students "Annual report day" - 25 September 2019
Hypothesis
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Matteo
BrunettiPhD students "Annual report day" - 25 September 2019
Characterize the bacterial communities associated to ~200 species of leaf
beetles (Coleoptera: Chrysomelidae) of the Euro-Mediterranean region
and investigate possible correlation with the phylogeny and the ecology of
the insect hosts
Characterize the metabolic potential of the identified microbiotas and
correlate it with characteristics of the host plants
Extend the framework of this project using already published microbiotas
data (online databases) and correlate features of the microbial
communities with characteristics of the insect host (e.g., taxonomy, diet,
habitat, life stage)
[A1]
[A2]
[A3]
Aims
• DNA extraction, libraries preparation
and 16S (V1-V2 & V4 regions) high-
throughput sequencing (Ion Torrent)
• Cluster analysis and taxonomic
identification (QIIME2 platform) [A1]
• Functional characterization of bacterial
communities (PICRUSt2) [A2]
• Published microbiota data mining (NCBI
SRA tool kit) [A3]
• Data analysis (QIIME2, R software)
[A1-A3]
Matteo
BrunettiPhD students "Annual report day" - 25 September 2019
Materials and methods
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Matteo
BrunettiPhD students "Annual report day" - 25 September 2019
Bibliographic research
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Best methodology for 16S metabarcoding
10
Matteo
BrunettiPhD students "Annual report day" - 25 September 2019
Preliminary results: whole bacterial communities
Monophagous Oligophagous Poliphagous
NO clear pattern
NO statistically
significant result
Primary symbionts could
play a confounding role
Exclude known primary
symbionts (e.g., Wolbachia)
from subsequent analyses
11
Matteo
BrunettiPhD students "Annual report day" - 25 September 2019
Preliminary results: alpha diversity
Kruskal-Wallis test H p-value
Monophagous-Oligophagous 1.31 0.25
Monophagous-Poliphagous 6.22 0.013
Oligophagous-Poliphagous 2.14 0.14
All groups 6.77 0.049
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Matteo
BrunettiPhD students "Annual report day" - 25 September 2019
Preliminary results: alpha diversity
Kruskal-Wallis test H p-value
Monophagous-Oligophagous 1.31 0.25
Monophagous-Poliphagous 6.22 0.013
Oligophagous-Poliphagous 2.14 0.14
All groups 6.77 0.049
13
Matteo
BrunettiPhD students "Annual report day" - 25 September 2019
Preliminary results: alpha diversity
Kruskal-Wallis test H p-value
All-groups 11.54 0.32
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Matteo
BrunettiPhD students "Annual report day" - 25 September 2019
Preliminary results: alpha diversity
Kruskal-Wallis test H p-value
All-groups 11.54 0.32
p-value < 0.05
15
Matteo
BrunettiPhD students "Annual report day" - 25 September 2019
Preliminary results: Beta diversity
Pairwise-permanova pseudo-F p-value
Monophagous-Oligophagous 1.32 0.19
Monophagous-Poliphagous 1.52 0.092
Oligophagous-Poliphagous 2.2 0.009
All groups 1.72 0.01
Unweighted Unifrac Distance (D):
➢ D = 0 →
➢ D ~ 0.5 →
➢ D = 1 →Monophagous Oligophagous Poliphagous
identical
communities
related
communities
unrelated
communities
16
Matteo
BrunettiPhD students "Annual report day" - 25 September 2019
Preliminary results: Beta diversity
Pairwise-permanova pseudo-F p-value
Monophagous-Oligophagous 1.32 0.19
Monophagous-Poliphagous 1.52 0.092
Oligophagous-Poliphagous 2.2 0.009
All groups 1.72 0.01
Unweighted Unifrac Distance (D):
➢ D = 0 →
➢ D ~ 0.5 →
➢ D = 1 →Monophagous Oligophagous Poliphagous
identical
communities
related
communities
unrelated
communities
17
Matteo
BrunettiPhD students "Annual report day" - 25 September 2019
Perspectives
• Extract DNA from about 200 species of leaf beetles from Euro-
Mediterranean region and sequence the V1-V2 and V4 regions
of the bacterial 16S rRNA gene.
• Development of the dataset made of already published data on
microbiota associated with insects.
• Characterize the metabolic potential of the obtained microbiotas
(PICRUSt2) and correlate it with the secondary metabolites
present in the host plant tissues.
• Analyze (QIIME2, R software) the obtained datasets in the light
of information on characteristics of the insect hosts (e.g.,
taxonomy, host plant, habitat, life stage)
• Prof. Franco Faoro
• Dr. Matteo Montagna
• Dr. Giulia Magoga
THANK YOU FOR YOUR ATTENTION!
WORKING GROUP:
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