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. 2018 Apr 23;12(4):e0006433.
doi: 10.1371/journal.pntd.0006433. eCollection 2018 Apr.

Entomopathogenic fungal infection leads to temporospatial modulation of the mosquito immune system

José L Ramirez  1 , Christopher A Dunlap  1 , Ephantus J Muturi  1 , Ana B F Barletta  2 , Alejandro P Rooney  1
Affiliations

Entomopathogenic fungal infection leads to temporospatial modulation of the mosquito immune system

José L Ramirez et al. PLoS Negl Trop Dis. .

Abstract

Alternative methods of mosquito control are needed to tackle the rising burden of mosquito-borne diseases while minimizing the use of synthetic insecticides, which are threatened by the rapid increase in insecticide resistance in mosquito populations. Fungal biopesticides show great promise as potential alternatives because of their ecofriendly nature and ability to infect mosquitoes on contact. Here we describe the temporospatial interactions between the mosquito Aedes aegypti and several entomopathogenic fungi. Fungal infection assays followed by the molecular assessment of infection-responsive genes revealed an intricate interaction between the mosquito immune system and entomopathogenic fungi. We observed contrasting tissue and time-specific differences in the activation of immune signaling pathways and antimicrobial peptide expression. In addition, these antifungal responses appear to vary according to the fungal entomopathogen used in the infection. Enzyme activity-based assays coupled with gene expression analysis of prophenoloxidase genes revealed a reduction in phenoloxidase (PO) activity in mosquitoes infected with the most virulent fungal strains at 3 and 6d post-fungal infection. Moreover, fungal infection led to an increase in midgut microbiota that appear to be attributed in part to reduced midgut reactive oxygen species (ROS) activity. This indicates that the fungal infection has far reaching effects on other microbes naturally associated with mosquitoes. This study also revealed that despite fungal recognition and immune elicitation by the mosquito, it is unable to successfully eliminate the entomopathogenic fungal infection. Our study provides new insights into this intricate multipartite interaction and contributes to a better understanding of mosquito antifungal immunity.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Mosquito infection by diverse fungal strains.
(A) Survival curves of fungal-challenged mosquitoes. Graph represents 3 independent experiments and data was analyzed with Log-rank Test (GraphPad Prism 7) (B) Entomopathogenic fungi infection stages: conidia (top), hemocytes and blastospores (middle), and hyphal growth on mosquito cadavers (bottom). Black arrows indicate hemocytes while red arrows indicate blastospores. No blastospores were found in T. roseum-challenged mosquitoes.
Fig 2
Fig 2. Recognition of diverse fungal strains by the mosquito immune system.
Relative expression of fungal recognition genes CLSP2 and TEP22 in the midgut and fat body at 3d and 6d PI. Data represents the fold change in expression from three independent experiments. Data was analyzed by one-way ANOVA with Dunnett’s post-test; * P<0.05, ** P<0.01, *** P<0.001, **** P<0.0001.
Fig 3
Fig 3. Elicitation of innate immune signaling pathways following fungal infection is time, tissue and fungal strain-specific.
(A) Gene expression analysis of REL1 (Toll pathway), REL2 (IMD pathway), STAT (JAK-STAT pathway) and JNK (JNK pathway) in the midgut and fat body at 3d and 6d PI. Data represents the fold change in expression from at least three independent experiments. (B) Heatmap generated from the antimicrobial peptide gene expression following fungal infection in the midgut and fat body at 3d and 6d PI. Heat-map from qPCR data represents the median of log2 fold change values from three independent experiments with red representing higher expression levels and green lower expression levels compared to the control. CECG, cecropin G; DEFC, defensin C; ATTA, attacin; LYSC, lysozyme C. Data was analyzed by one-way ANOVA with Dunnett’s post-test; * P<0.05, ** P<0.01, *** P<0.001, **** P<0.0001.
Fig 4
Fig 4. Entomopathogenic fungal infection results in the downregulation of PPO gene expression and reduction of phenoloxidase activity.
Gene expression profiles of (A) PPO3 and (B) PPO5 in the midgut and fat body at 3d and 6d PI. Data represents the fold change in expression from three independent experiments. Data was analyzed by one-way ANOVA with Dunnett’s post-test. * P<0.05, ** P<0.01, *** P<0.001, **** P<0.0001. Phenoloxidase activity (Vmax) was evaluated from whole-body macerates of mosquitoes at (C) 3d and (D) 6d PI. Data represents samples from 3 independent experiments. Data was analyzed via ANOVA-Kruskal-Wallis followed by Dunn’s multiple comparison test. * P<0.05, ** P<0.01, *** P<0.001, **** P<0.0001.
Fig 5
Fig 5. Entomopathogenic fungal infection leads to dysregulation of the mosquito midgut homeostasis.
Relative quantification of (A) bacterial 16s rRNA in the mosquito midgut at 3d and 6d PI and (B) bacterial 16s rDNA at 6d PI. ** P<0.01. (C) Relative bacterial OTU abundance at the genus level from mosquito midguts collected at 6d PI. Labels on the x-axis represent the replicate groups under each treatment. (D) Principal coordinate analysis (PCoA) depicting patterns of beta diversity for the mosquito midgut bacterial communities under different fungal infections. PCoA was based on the Bray-Curtis dissimilarity matrix.
Fig 6
Fig 6. Dysbiosis of the mosquito gut occurs via modulation of gut-homoeostasis-related genes and reduction of ROS activity in the midgut.
Expression profiles of (A) MESH and (B) DUOX1 in the mosquito midgut at 3d and 6d PI. Data represents the fold change in expression from at least three independent experiments. (C) Hydrogen peroxide release (ROS) from the mosquito midgut at 6d PI. (D) Midgut ROS activity was imaged using DHE at 6d PI. Top panels show DHE fluorescence (red) while lower panels show DAPI staining of nuclei (blue). Heat map depicting the differential expression of oxidant and antioxidant genes in the (E) midgut and (F) fat body at 3d and 6d PI. Data represents the fold change in expression from three independent experiments. Data was analyzed by one-way ANOVA with Dunnett’s post-test. * P<0.05, ** P<0.01, *** P<0.001, **** P<0.0001.
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Grants and funding

The authors received no specific funding for this study.
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