SignificanceThis manuscript describes a precise diel cycle carried out by airborne microbiota in the tropics. 795 metagenomes from air samples taken from a single site show that fungi, bacteria, and plants all adhere to a specific timing for their presence in the near-surface atmosphere. The airborne community composition thereby shows an unexpected robustness, with the majority of the dynamics in taxa composition occurring within 24 h, but not across days, weeks, or months. Environmental parameters are the main drivers for the observed phenomenon, with temperature being the most important one.

Blowflies and houseflies are mechanical vectors inhabiting synanthropic environments around the world. They feed and breed in fecal and decaying organic matter, but the microbiome they harbour and transport is largely uncharacterized. We sampled 116 individual houseflies and blowflies from varying habitats on three continents and subjected them to high-coverage, whole-genome shotgun sequencing. This allowed for genomic and metagenomic analyses of the host-associated microbiome at the species level. Both fly host species segregate based on principal coordinate analysis of their microbial communities, but they also show an overlapping core microbiome. Legs and wings displayed the largest microbial diversity and were shown to be an important route for microbial dispersion. The environmental sequencing approach presented here detected a stochastic distribution of human pathogens, such as Helicobacter pylori, thereby demonstrating the potential of flies as proxies for environmental and public health surveillance.

BackgroundDynamic aspects of gene regulatory networks are typically investigated by measuring system variables at multiple time points. Current state-of-the-art computational approaches for reconstructing gene networks directly build on such data, making a strong assumption that the system evolves in a synchronous fashion at fixed points in time. However, nowadays omics data are being generated with increasing time course granularity. Thus, modellers now have the possibility to represent the system as evolving in continuous time and to improve the models’ expressiveness.ResultsContinuous time Bayesian networks are proposed as a new approach for gene network reconstruction from time course expression data. Their performance was compared to two state-of-the-art methods: dynamic Bayesian networks and Granger causality analysis. On simulated data, the methods comparison was carried out for networks of increasing size, for measurements taken at different time granularity densities and for measurements unevenly spaced over time. Continuous time Bayesian networks outperformed the other methods in terms of the accuracy of regulatory interactions learnt from data for all network sizes. Furthermore, their performance degraded smoothly as the size of the network increased. Continuous time Bayesian networks were significantly better than dynamic Bayesian networks for all time granularities tested and better than Granger causality for dense time series. Both continuous time Bayesian networks and Granger causality performed robustly for unevenly spaced time series, with no significant loss of performance compared to the evenly spaced case, while the same did not hold true for dynamic Bayesian networks. The comparison included the IRMA experimental datasets which confirmed the effectiveness of the proposed method. Continuous time Bayesian networks were then applied to elucidate the regulatory mechanisms controlling murine T helper 17 (Th17) cell differentiation and were found to be effective in discovering well-known regulatory mechanisms, as well as new plausible biological insights.ConclusionsContinuous time Bayesian networks were effective on networks of both small and large size and were particularly feasible when the measurements were not evenly distributed over time. Reconstruction of the murine Th17 cell differentiation network using continuous time Bayesian networks revealed several autocrine loops, suggesting that Th17 cells may be auto regulating their own differentiation process.