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Harris County, Texas, remains at continuous risk to mosquito-borne diseases due to its geographic landscape and abundance of medically important mosquito vectors. Targeted mitigation of these mosquitoes requires accurate identification of these mosquitoes taxa. Currently, there is a paucity of genetic information to inform molecular identification and phylogenetic relationships beyond well-studied mosquito species. Here we utilized a genome skimming approach using shallow shot gun sequencing to generate data and assemble the mitochondrial genomes of 37 mosquito species collected in Harris County, Texas. This report includes the complete mitochondrial genome for 25 newly sequenced species spanning 10 genera; the genomes were consistent with reference genomes in the GenBank database having 37 genes (13 protein-coding, 2 rRNA and 22 tRNA), and average AT content of 78.74%. Bayesian and maximum likelihood tree topologies using just the easily aligned 13 concatenated protein coding genes confirmed phylogenetic placement of species for Aedes, Anopheles and Culex genera clustering in single clades as expected. Furthermore, this approach provided more robust phylogenetic placement/identity of study taxa when compared to the use of the traditional cytochrome oxidase I partial gene barcode sequence for molecular identification. This study demonstrates the utility of genome skimming as a cost-effective alternative approach to generate reference sequences for the validation of mosquito identification and taxonomic rectification, knowledge necessary for guiding targeted vector interventions. 

Abstract The mosquito microbiome is critical for host development and plays a major role in many aspects of mosquito biology. While the microbiome is commonly dominated by a small number of genera, there is considerable variation in composition among mosquito species, life stages, and geography. How the host controls and is affected by this variation is unclear. Using microbiome transplant experiments, we asked whether there were differences in transcriptional responses when mosquitoes of different species were used as microbiome donors. We used microbiomes from four different donor species spanning the phylogenetic breadth of the Culicidae, collected either from the laboratory or the field. We found that when recipients received a microbiome from a donor reared in the laboratory, the response was remarkably similar regardless of donor species. However, when the donor had been collected from the field, many more genes were differentially expressed. We also found that while the transplant procedure did have some effect on the host transcriptome, this is likely to have had a limited effect on mosquito fitness. Overall, our results highlight the possibility that variation in mosquito microbiome communities is associated with variability in host–microbiome interactions and further demonstrate the utility of the microbiome transplantation technique for investigating host–microbe interactions in mosquitoes.