Wolbachia Infection Alters the Microbiota of the Invasive Leaf-Miner Liriomyza huidobrensis (Diptera: Agromyzidae)

Abstract

Microbe-microbe interactions within a host drive shifts in the host's microbiota composition, profoundly influencing host physiology, ecology, and evolution. Among these microbes, the maternally inherited endosymbiont Wolbachia is widespread in the invasive pest Liriomyza huidorbrensis (Diptera: Agromyzidae). However, its influence on the host microbiota remains largely unexplored. In the study presented herein, we investigated the bacterial communities of Wolbachia wLhui-infected (wLhui+) and -uninfected lines (wLhui-) of L. huidorbrensis using 16S rRNA gene high-throughput sequencing. For both leaf-miner lines, Bacteroidota was the dominant phylum (relative abundance: 59.18%), followed by Pseudomonadota (36.63%), Actinomycetota (2.42%), and Bacillota (0.93%). We found no significant differences in alpha-diversity indices between the wLhui+ and wLhui- lines (p > 0.05). However, principal coordinates analysis revealed significant differences in microbiota composition between the wLhui+ and wLhui- lines (PERMANOVA: p < 0.001), explaining 76.70% of the variance in microbiota composition. Correlation network analysis identified robust negative and positive associations between Wolbachia and several genera, suggesting that Wolbachia shapes microbial composition through competitive or cooperative interactions with specific taxa. Overall, our study suggests that Wolbachia plays a key role in shaping the leaf-miner microbiome, potentially affecting host fitness.

Keywords: Wolbachia; endosymbiont; invasive pest; microbiome.

Figure 1

Bacterial alpha-diversity in Wolbachia wLhui-infected (wLhui+) and Wolbachia-uninfected (wLhui−) leaf-miners, as indicated by ASV observed richness, Shannon, Chao 1, ACE, Simpson, and Fisher indices. Boxplots represent the 10th and 90th percentiles (lower and upper boundaries), with the median indicated by the horizontal line inside each box. Statistical significance was assessed using the Mann–Whitney U test (ns, not significant).

Figure 2

Bacterial beta-diversity of leaf-miners infected with or without Wolbachia. PCoA based on Bray–Curtis dissimilarities of Wolbachia wLhui-infected (wLhui+) and wLhui-uninfected (wLhui−) leaf-miners. The percentage of variance explained by the first two principal components is shown in parentheses. The significance of the differences in bacterial community composition was assessed using PERMANOVA (p < 0.05).

Figure 3

Relative abundance of bacterial genera in Wolbachia wLhui-infected (wLhui+) and -uninfected (wLhui−) leaf-miners. The relative abundance of the top bacterial genera is shown for each sample from both lines.

Figure 4

Variation in bacterial taxa between Wolbachia wLhui-infected (wLhui+) and -uninfected (wLhui−) leaf-miners. Significant differences in the relative abundance of bacterial genera between the two lines were calculated using DESeq2 analysis. Genera with log2 fold change (wLhui+ vs. wLhui−) > 2 and p < 0.05 were considered significantly different. The size of the points is determined by the logarithmic counts of genera between two lines. Upward-facing triangles indicate a positive fold change, while downward-facing triangles represent a negative fold change.

Figure 5

Correlation network of bacterial taxa (at the genus level) in both Wolbachia wLhui-infected (wLhui+) and wLhui-uninfected (wLhui−) leaf-miners. Pairwise microbe–microbe correlations were assessed using Spearman rank correlation, with a threshold of p < 0.05 and |ρ| > 0.6. The size of nodes represents the number of connections associated with each taxon. Green and orange shading within the nodes reflects the relative abundance of each genus in the wLhui+ or wLhui− lines, respectively. Positive correlations are represented by red edges, while negative correlations are indicated by blue edges. The thickness of the edges corresponds to the strength of the correlation. Statistical details are provided in Table S3.