Microbial communities of the house fly Musca domestica vary with geographical location and habitat

Environmental and Sex Effects on Bacterial Carriage by Adult House Flies (Musca domestica L.)

Adult house flies frequent microbe-rich sites such as urban dumpsters and animal facilities, and encounter and ingest bacteria during feeding and reproductive activities. Due to unique nutritional and reproductive needs, male and female flies demonstrate different interactions with microbe-rich substrates and therefore dissemination potential. We investigated culturable aerobic bacteria and coliform abundance in male and female flies (n = 107) collected from urban (restaurant dumpsters) and agricultural (dairy farm) sites. Whole-fly homogenate was aerobically cultured and enumerated on nonselective (tryptic soy agar; culturable bacteria) and selective (violet-red bile agar, VRBA; coliforms) media. Unique morphotypes from VRBA cultures of agricultural flies were identified and tested for susceptibility to 14 antimicrobials. Female flies harbored more bacteria than males and there was a sex by site interaction with sex effects on bacterial abundance at the urban site. Coliform abundance did not differ by sex, site or sex within site. Both male and female flies carried antimicrobial-resistant (AMR) bacteria: 36/38 isolates (95%) were resistant to ≥1 antimicrobial, 33/38 were multidrug-resistant (≥2), and 24/38 isolates were resistant to ≥4 antimicrobials. Our results emphasize the role of house flies in harboring bacteria including AMR strains that pose a risk to human and animal health.

Correlation and association analyses in microbiome study integrating multiomics in health and disease

Correlation and association analyses are one of the most widely used statistical methods in research fields, including microbiome and integrative multiomics studies. Correlation and association have two implications: dependence and co-occurrence. Microbiome data are structured as phylogenetic tree and have several unique characteristics, including high dimensionality, compositionality, sparsity with excess zeros, and heterogeneity. These unique characteristics cause several statistical issues when analyzing microbiome data and integrating multiomics data, such as large p and small n, dependency, overdispersion, and zero-inflation. In microbiome research, on the one hand, classic correlation and association methods are still applied in real studies and used for the development of new methods; on the other hand, new methods have been developed to target statistical issues arising from unique characteristics of microbiome data. Here, we first provide a comprehensive view of classic and newly developed univariate correlation and association-based methods. We discuss the appropriateness and limitations of using classic methods and demonstrate how the newly developed methods mitigate the issues of microbiome data. Second, we emphasize that concepts of correlation and association analyses have been shifted by introducing network analysis, microbe-metabolite interactions, functional analysis, etc. Third, we introduce multivariate correlation and association-based methods, which are organized by the categories of exploratory, interpretive, and discriminatory analyses and classification methods. Fourth, we focus on the hypothesis testing of univariate and multivariate regression-based association methods, including alpha and beta diversities-based, count-based, and relative abundance (or compositional)-based association analyses. We demonstrate the characteristics and limitations of each approaches. Fifth, we introduce two specific microbiome-based methods: phylogenetic tree-based association analysis and testing for survival outcomes. Sixth, we provide an overall view of longitudinal methods in analysis of microbiome and omics data, which cover standard, static, regression-based time series methods, principal trend analysis, and newly developed univariate overdispersed and zero-inflated as well as multivariate distance/kernel-based longitudinal models. Finally, we comment on current association analysis and future direction of association analysis in microbiome and multiomics studies.

The Microbial Diversity of Cabbage Pest Delia radicum Across Multiple Life Stages

The cabbage root fly Delia radicum is a worldwide pest that causes yield losses of many common cabbage crops. The bacteria associated with D. radicum are suggested to influence the pest status of their host. In this study, we characterized insect-associated bacteria of D. radicum across multiple life stages and of their diet plant (turnip, Brassica rapa subsp. rapa) by sequencing the V3–V4 region of 16S rRNA genes using the Illumina MiSeq platform. In total, over 1.2M paired-end reads were obtained, identifying 1006 bacterial amplicon sequence variants (ASVs) in samples obtained from the eggs, larvae, pupae and adults of D. radicum, as well as turnips that were either fresh or infested with D. radicum larvae. The microbial community in D. radicum was dominated by Wolbachia, a common endosymbiont of arthropods which we found in all of the investigated insect samples, with the pupal stage having the highest relative abundance. Moderate amounts of Firmicutes were found only in adult D. radicum flies, but not in previous life stages. Actinobacteria were mostly found on the eggs and on the skin of fresh plants on which the eggs were deposited. These plants also harbored a large amount of Pseudomonas. The bacterial diversity of the healthy turnip was low, whereas the microbial community of decaying turnips that were heavily infested by D. radicum larvae and showing symptoms of advanced soft rot was characterized by a high bacterial diversity. Taken together, this work provides insights into the bacterial communities associated with the cabbage pest D. radicum and its associated disease symptoms.

Integrated genome-wide investigations of the housefly, a global vector of diseases reveal unique dispersal patterns and bacterial communities across farms

Background

Houseflies (Musca domestica L.) live in intimate association with numerous microorganisms and is a vector of human pathogens. In temperate areas, houseflies will overwinter in environments constructed by humans and recolonize surrounding areas in early summer. However, the dispersal patterns and associated bacteria across season and location are unclear. We used genotyping-by-sequencing (GBS) for the simultaneous identification and genotyping of thousands of Single Nucleotide Polymorphisms (SNPs) to establish dispersal patterns of houseflies across farms. Secondly, we used 16S rRNA gene amplicon sequencing to establish the variation and association between bacterial communities and the housefly across farms.

Results

Using GBS we identified 18,000 SNPs across 400 individuals sampled within and between 11 dairy farms in Denmark. There was evidence for sub-structuring of Danish housefly populations and with genetic structure that differed across season and sex. Further, there was a strong isolation by distance (IBD) effect, but with large variation suggesting that other hidden geographic barriers are important. Large individual variations were observed in the community structure of the microbiome and it was found to be dependent on location, sex, and collection time. Furthermore, the relative prevalence of putative pathogens was highly dependent on location and collection time.

Conclusion

We were able to identify SNPs for the determination of the spatiotemporal housefly genetic structure, and to establish the variation and association between bacterial communities and the housefly across farms using novel next-generation sequencing (NGS) techniques. These results are important for disease prevention given the fine-scale population structure and IBD for the housefly, and that individual houseflies carry location specific bacteria including putative pathogens.