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Although mosquito microbiota are known to influence reproduction, nutrition, disease transmission, and pesticide resistance, the relationship between host-associated microbial community composition and geographical location is poorly understood. To begin addressing this knowledge gap, we characterized microbiota associated with adult females of Culex nigripalpus mosquito vectors of Saint Louis Encephalitis and West Nile viruses sampled from three locations in Florida (Vero Beach, Palmetto Inland, and Palmetto Coast). High-throughput sequencing of PCR-amplified 16S rRNA genes demonstrated significant differences among microbial communities of mosquitoes sampled from the three locations. Mosquitoes from Vero Beach (east coast Florida) were dominated by uncultivated Asaia sp. (Alphaproteobacteria), whereas microbiota associated with mosquitoes collected from two mosquito populations at Palmetto (west coast Florida) sites were dominated by uncultured Spironema culicis (Spirochaetes), Salinisphaera hydrothermalis (Gammaproteobacteria), Spiroplasma (Mollicutes), uncultured Enterobacteriaceae, Candidatus Megaira (Alphaproteobacteria; Rickettsiae), and Zymobacter (Gammaproteobacteria). The variation in taxonomic profiles of Cx. nigripalpus gut microbial communities, especially with respect to dominating taxa, is a potentially critical factor in understanding disease transmission and mosquito susceptibility to insecticides among different mosquito populations.

Taxonomic markers such as the 16S ribosomal RNA gene are widely used in microbial community analysis. A common first step in marker-gene analysis is grouping genes into clusters to reduce data sets to a more manageable size and potentially mitigate the effects of sequencing error. Instead of clustering based on sequence identity, marker-gene data sets collected over time can be clustered based on temporal correlation to reveal ecologically meaningful associations. We present Ananke, a free and open-source algorithm and software package that complements existing sequence-identity-based clustering approaches by clustering marker-gene data based on time-series profiles and provides interactive visualization of clusters, including highlighting of internal OTU inconsistencies. Ananke is able to cluster distinct temporal patterns from simulations of multiple ecological patterns, such as periodic seasonal dynamics and organism appearances/disappearances. We apply our algorithm to two longitudinal marker gene data sets: faecal communities from the human gut of an individual sampled over one year, and communities from a freshwater lake sampled over eleven years. Within the gut, the segregation of the bacterial community around a food-poisoning event was immediately clear. In the freshwater lake, we found that high sequence identity between marker genes does not guarantee similar temporal dynamics, and Ananke time-series clusters revealed patterns obscured by clustering based on sequence identity or taxonomy. Ananke is free and open-source software available at https://github.com/beiko-lab/ananke.