The Composition of Midgut Bacteria in Aedes aegypti (Diptera: Culicidae) That Are Naturally Susceptible or Refractory to Dengue Viruses

The composition, abundance, and diversity of midgut bacteria in mosquitoes can influence pathogen transmission. We used 16S rRNA microbiome profiling to survey midgut microbial diversity in pooled samples of laboratory colonized dengue-refractory, Cali-MIB, and dengue-susceptible, Cali-S Aedes aegypti (Linnaeus). The 16S rRNA sequences from the sugar-fed midguts of adult females clustered to 63 amplicon sequence variants (ASVs), primarily from Proteobacteria, Firmicutes, Flavobacteria, and Actinobacteria. An average of five ASVs dominated the midguts, and most ASVs were present in both Cali-MIB and Cali-S midguts. No differences in abundance were noted at any phylogenetic level (Phylum, Class, Order, Family, Genus) by analysis of composition of microbiome (w = 0). No community diversity metrics were significantly different between refractory and susceptible mosquitoes. These data suggest that phenotypic differences in the susceptibility to dengue virus between Cali-MIB and Cali-S are not likely due to major differences in midgut bacterial communities

Characterizing and engineering a dengue refractory phenotype in Aedes aegypti

Dengue viruses infect ~400 million people annually and are transmitted principally by Aedes aegypti. Severe dengue (dengue hemorrhagic fever and dengue shock syndrome) can be fatal, and there are no efficient drugs or vaccines to prevent the disease. Not all Ae. aegypti transmit dengue viruses; in Cali, Colombia, approximately 30% of feral populations are naturally refractory to all four viral serotypes through midgut mechanisms (Cali-MIB), while the remaining 70% are susceptible (Cali-S) and transmit the viruses. We used a combination of molecular biology and bioinformatic methods to identify differences between the refractory and susceptible strains. RNA sequencing, 16S rRNA bacterial profiling, and a genome wide association study (GWAS) were used to identify a subset of genes thought to contribute to the Cali-MIB and Cali-S phenotypes. Genes from this subset that were able to ‘flip’ the phenotype from susceptible to refractory through RNAi based knockdowns were further tested with gene-editing technology to knock-out these genes using clustered regularly interspaced palindromic repeats (CRISPR) – CRISPR-associated protein 9 (Cas9) guide RNA complexes. This research identified multiple genes we believe contribute to vector competence, created a DNA based assay for identifying Cali-MIB and Cali-S mosquitoes, and edited the germ-line of Ae. aegypti. This information could allow us to create lines of permanently refractory mosquitoes to dampen dengue transmission

Fig 3 -

(A) Neighbor-Joining tree of the pepC10 fragment of 21 ospC MGs sequences built with near-minimal sum of branch-length estimated using Hamming distance. The phylogenetic groups were identified as numbers (1 to 3) and circled. (B) Histogram of the mean values of reactivity of each phylogenetic group and (C) the polynomial model of the reactivity difference and genetic distance of pepC10 fragment.</p

S1 File -

The outer surface protein C (OspC) of the agent of Lyme disease, Borrelia burgdorferi sensu stricto, is a major lipoprotein surface-expressed during early-phase human infections. Antibodies to OspC are used in serological diagnoses. This study explored the hypothesis that serological test sensitivity decreases as genetic similarity of ospC major groups (MGs) of infecting strains, and ospC A (the MG in the strain B31 used to prepare antigen for serodiagnosis assays) decreases. We used a previously published microarray dataset to compare serological reactivity to ospC A (measured as pixel intensity) versus reactivity to 22 other ospC MGs, within a population of 55 patients diagnosed by two-tier serological testing using B. burgdorferi s.s. strain B31 as antigen, in which the ospC MG is OspC A. The difference in reactivity of sera to ospC A and reactivity to each of the other 22 ospC MGs (termed ‘reactivity difference’) was the outcome variable in regression analysis in which genetic distance of the ospC MGs from ospC A was the explanatory variable. Genetic distance was computed for the whole ospC sequence, and 9 subsections, from Neighbour Joining phylogenetic trees of the 23 ospC MGs. Regression analysis was conducted using genetic distance for the full ospC sequence, and the subsections individually. There was a significant association between the reactivity difference and genetic distance of ospC MGs from ospC A: increased genetic distance reduced reactivity to OspC A. No single ospC subsection sequence fully explained the relationship between genetic distance and reactivity difference. An analysis of single nucleotide polymorphisms supported a biological explanation via specific amino acid modifications likely to change protein binding affinity. This adds support to the hypothesis that genetic diversity of B. burgdorferi s.s. (here specifically OspC) may impact serological diagnostic test performance. Further prospective studies are necessary to explore the clinical implications of these findings.</div

Fig 9 -

(A) Histogram of the mean values of reactivity and genetic distance of the full sequence length of 22 ospC MGs relative to ospC A. (B) Fitted values of linear and polynomial regression models of the reactivity difference and the genetic distance of 22 ospC MGs and ospC A. (C) Leverage versus squared residual plot for regression diagnostic. (D) Polynomial regression models of the reactivity difference and the genetic distance of 21 ospC MGs and ospC A, without data for ospC D3.</p

Fig 4 -

(A) Neighbor-Joining tree of the α2L4 fragment of 23 ospC MGs sequences built with near-minimal sum of branch-length estimated using Hamming distance. The phylogenetic groups were identified as numbers (1 to 3) and circled. (B) Histogram of the mean values of reactivity of each phylogenetic group and (C) the polynomial model of the reactivity difference and genetic distance of α2L4 fragment.</p

The adjusted R squared and the P value for the thirteen linear models that explored the relationship between genetic distance of ospC MGs from ospC A, and the reactivity difference.

The adjusted R squared and the P value for the thirteen linear models that explored the relationship between genetic distance of ospC MGs from ospC A, and the reactivity difference.</p

Fig 7 -

(A) Neighbor-Joining tree of the α5 fragment of 23 ospC MGs sequences built with near-minimal sum of branch-length estimated using Hamming distance. The phylogenetic groups were identified as numbers (1 to 7) and circled. (B) Histogram of the mean values of reactivity of each phylogenetic group and (C) the linear model of the reactivity difference and genetic distance of α5 fragment.</p

Fig 2 -

(A) Neighbor-Joining tree of the N-terminal fragment of 23 ospC MGs sequences built with near-minimal sum of branch-length estimated using Hamming distance. The phylogenetic groups were identified as numbers (1 to 9) and circled. (B) Histogram of the mean values of reactivity of each phylogenetic group and (C) the local polynomial smoothing of the polynomial model of the reactivity difference and genetic distance of the N-terminal fragment.</p