Habitat type and host grazing regimen influence the soil microbial diversity and communities within potential biting midge larval habitats

Tiny silver bullets: silver nanoparticles are insecticidal to Culicoides sonorensis (Diptera: Ceratopogonidae) biting midge larvae

Insecticide formulations with safer environmental profiles and limited off-target effects are desirable to manage medical and veterinary pests. Silver nanoparticles are insecticidal against mosquitos, nonbiting midges, and other insects. The biting midge, Culicoides sonorensis Wirth and Jones, is a vector of agriculturally important pathogens in much of the United States, and this study aimed to examine the insecticidal properties of silver nanoparticles in larvae of this species. Mortality of third-instar larvae was assessed daily for 7 days after exposure to concentrations of silver nanoparticles, sorghum polymer particles, and hybrid silver-sorghum polymer particles. Both silver nanoparticles and silver-sorghum polymer particles were insecticidal, but sorghum polymer particles alone did not significantly contribute to larval mortality. Concentrations of 100 mg/liter of silver nanoparticles achieved >50% mortality at day 7, and 200 mg/liter treatments achieved >75% larval mortality within 24 h. The antimicrobial properties of silver nanoparticles were also examined, and culturable bacteria were recovered from larval-rearing media at 200 mg/liter but not at 400 mg/liter of silver nanoparticles. These data suggest that C. sonorensis larval mortality is primarily caused by silver nanoparticle toxicity and not by the reduction of bacteria (i.e., a larval food source). This work describes the first use of silver nanoparticles in C. sonorensis and shows the potential insecticide applications of these nanoparticles against this agricultural pest. The grain-polymer particles also successfully carried insecticidal silver nanoparticles, and their utility in loading diverse compounds could be a novel toxin delivery system for biting midges and similar pests.

DL-TODA: A Deep Learning Tool for Omics Data Analysis

Metagenomics is a technique for genome-wide profiling of microbiomes; this technique generates billions of DNA sequences called reads. Given the multiplication of metagenomic projects, computational tools are necessary to enable the efficient and accurate classification of metagenomic reads without needing to construct a reference database. The program DL-TODA presented here aims to classify metagenomic reads using a deep learning model trained on over 3000 bacterial species. A convolutional neural network architecture originally designed for computer vision was applied for the modeling of species-specific features. Using synthetic testing data simulated with 2454 genomes from 639 species, DL-TODA was shown to classify nearly 75% of the reads with high confidence. The classification accuracy of DL-TODA was over 0.98 at taxonomic ranks above the genus level, making it comparable with Kraken2 and Centrifuge, two state-of-the-art taxonomic classification tools. DL-TODA also achieved an accuracy of 0.97 at the species level, which is higher than 0.93 by Kraken2 and 0.85 by Centrifuge on the same test set. Application of DL-TODA to the human oral and cropland soil metagenomes further demonstrated its use in analyzing microbiomes from diverse environments. Compared to Centrifuge and Kraken2, DL-TODA predicted distinct relative abundance rankings and is less biased toward a single taxon.