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. 2022 Dec 22;11(1):40.
doi: 10.3390/microorganisms11010040.

Studying Plant-Insect Interactions through the Analyses of the Diversity, Composition, and Functional Inference of Their Bacteriomes

Affiliations

Studying Plant-Insect Interactions through the Analyses of the Diversity, Composition, and Functional Inference of Their Bacteriomes

Zyanya Mayoral-Peña et al. Microorganisms. .

Abstract

As with many other trophic interactions, the interchange of microorganisms between plants and their herbivorous insects is unavoidable. To test the hypothesis that the composition and diversity of the insect bacteriome are driven by the bacteriome of the plant, the bacteriomes of both the plant Datura inoxia and its specialist insect Lema daturaphila were characterised using 16S sRNA gene amplicon sequencing. Specifically, the bacteriomes associated with seeds, leaves, eggs, guts, and frass were described and compared. Then, the functions of the most abundant bacterial lineages found in the samples were inferred. Finally, the patterns of co-abundance among both bacteriomes were determined following a multilayer network approach. In accordance with our hypothesis, most genera were shared between plants and insects, but their abundances differed significantly within the samples collected. In the insect tissues, the most abundant genera were Pseudomonas (24.64%) in the eggs, Serratia (88.46%) in the gut, and Pseudomonas (36.27%) in the frass. In contrast, the most abundant ones in the plant were Serratia (40%) in seeds, Serratia (67%) in foliar endophytes, and Hymenobacter (12.85%) in foliar epiphytes. Indeed, PERMANOVA analysis showed that the composition of the bacteriomes was clustered by sample type (F = 9.36, p < 0.001). Functional inferences relevant to the interaction showed that in the plant samples, the category of Biosynthesis of secondary metabolites was significantly abundant (1.4%). In turn, the category of Xenobiotics degradation and metabolism was significantly present (2.5%) in the insect samples. Finally, the phyla Proteobacteria and Actinobacteriota showed a pattern of co-abundance in the insect but not in the plant, suggesting that the co-abundance and not the presence−absence patterns might be more important when studying ecological interactions.

Keywords: bacteriomes; co-abundance networks; diversity; foliar microbiota; functional inference; gut microbiota; plant–insect interaction.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Distribution of alpha diversity in samples collected from the plant D. inoxia and its specialist insect L. daturaphila. (A) Comparison of the Rènyi profiles of bacterial communities considering the total number of ASVs. Alpha values of 0, 1, 2, and infinity correspond to the species richness, Shannon diversity index, the logarithm of the reciprocal Simpson diversity index and Berger–Parker diversity index, respectively. Each colour represents a different sample. Solid lines correspond to insect samples, while dotted lines correspond to plant samples. (B) Richness. (C) Index of Shannon. (D) Phylogenetic diversity. Endophyte and seed samples were not included in this analysis, given the relatively small number of ASVs identified. Asterisks denote significant differences following an ANOVA test (p < 0.05).
Figure 2
Figure 2
Taxonomic composition of the 12 most abundant bacterial genera of the plant D. inoxia and its specialist insect L. daturaphila. In the insect, it represented 70.2%, while in the plant, it corresponded to 56.3% of the total readings.
Figure 3
Figure 3
Beta diversity analysis for the samples of L. daturaphila (eggs, gut, and frass) and D. inoxia (foliar epiphytes). Principal component analysis (PCA) plot based on the Bray–Curtis distance of the ASVs. Each colour represents the bacteriome community in a single type of sample. The symbols represent the origin of the samples: plants or insects. The enclosing ellipses are estimated using the Khachiyan algorithm. Endophyte and seed samples were not included in this analysis, given the relatively small number of ASVs identified.
Figure 4
Figure 4
Heatmap of the functional categories inferred from samples collected from the plant D. inoxia and its specialist insect L. daturaphila. Each column corresponds to a sample, and rows correspond to a specific functional category. The lightest colours represent the highest values, while darker colours correspond to the lowest values.
Figure 5
Figure 5
Relative abundances of KEGG orthologs (KOs) at level 1 involved drug metabolism of other enzymes, cytochrome p450, an indole alkaloid, sesquiterpenoid, and terpenoid, monoterpenoid biosynthesis, aminobenzoate degradation, geraniol degradation, and other glycan degradation inferred from samples of the plant D. inoxia and its specialist insect L. daturaphila. Different letters denote significant differences following a t-test (p < 0.05).
Figure 6
Figure 6
Multilayer network showing the co-abundances among bacterial phyla in the plant D. inoxia and its specialist insect L. daturaphila. The colours of the nodes correspond to the phylum level, and their sizes are proportional to their abundance in the different samples. Layers depicted the co-abundance networks inferred by the algorithm SPIEC-EASI. The third layer is the resulting aggregate network, where the links are the sum of the weighted individual connections, and the size of the nodes is the sum of the abundances.

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