Bacterial phylogeny predicts volatile organic compound composition and olfactory response of an aphid parasitoid

There is increasing evidence that microorganisms emit a wide range of volatile compounds (mVOCs, microbial volatile organic compounds) that act as insect semiochemicals, and therefore play an important role in insect behaviour. Although it is generally believed that phylogenetically closely related microbes tend to have similar phenotypic characteristics and therefore may elicit similar responses in insects, currently little is known about whether the evolutionary history and phylogenetic relationships among microorganisms have an impact on insect‐microbe interactions. In this study, we tested the hypothesis that phylogenetic relationships among 40 Bacillus strains isolated from diverse environmental sources predicted mVOC composition and the olfactory response of the generalist aphid parasitoid Aphidius colemani . Results revealed that phylogenetically closely related Bacillus strains emitted similar blends of mVOCs and elicited a comparable olfactory response of A. colemani in Y‐tube olfactometer bioassays, varying between attraction and repellence. Analysis of the chemical composition of the mVOC blends showed that all Bacillus strains produced a highly similar set of volatiles, but often in different concentrations and ratios. Benzaldehyde was produced in relatively high concentrations by strains that repel A. colemani , while attractive mVOC blends contained relatively higher amounts of acetoin, 2,3‐butanediol, 2,3‐butanedione, eucalyptol and isoamylamine. Overall, these results indicate that bacterial phylogeny had a strong impact on mVOC compositions and as a result on the olfactory responses of insects

Curated single cell multimodal landmark datasets for R/Bioconductor.

BackgroundThe majority of high-throughput single-cell molecular profiling methods quantify RNA expression; however, recent multimodal profiling methods add simultaneous measurement of genomic, proteomic, epigenetic, and/or spatial information on the same cells. The development of new statistical and computational methods in Bioconductor for such data will be facilitated by easy availability of landmark datasets using standard data classes.ResultsWe collected, processed, and packaged publicly available landmark datasets from important single-cell multimodal protocols, including CITE-Seq, ECCITE-Seq, SCoPE2, scNMT, 10X Multiome, seqFISH, and G&T. We integrate data modalities via the MultiAssayExperiment Bioconductor class, document and re-distribute datasets as the SingleCellMultiModal package in Bioconductor's Cloud-based ExperimentHub. The result is single-command actualization of landmark datasets from seven single-cell multimodal data generation technologies, without need for further data processing or wrangling in order to analyze and develop methods within Bioconductor's ecosystem of hundreds of packages for single-cell and multimodal data.ConclusionsWe provide two examples of integrative analyses that are greatly simplified by SingleCellMultiModal. The package will facilitate development of bioinformatic and statistical methods in Bioconductor to meet the challenges of integrating molecular layers and analyzing phenotypic outputs including cell differentiation, activity, and disease