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Brown et al. apply integrated omics and phenotypic screening to assess the role of the gut microbiome in modulating host resilience in Drosophila melanogaster. They find that Acetobacter tropicalis in gnotobiotic animals, is sufficient to rescue increased atrazine toxicity, which could pave the way for biotic strategies to improve host resilience to environmental chemical exposure.

Summary: High-content image acquisition is generally limited to cells grown in culture, requiring complex hardware and preset imaging modalities. Here we report an open source software package, OpenHiCAMM (Open Hi Content Acquisition for μManager), that provides a flexible framework for integration of generic microscope-associated robotics and image processing with sequential workflows. As an example, we imaged Drosophila embryos, detecting the embryos at low resolution, followed by re-imaging the detected embryos at high resolution, suitable for computational analysis and screening. The OpenHiCAMM package is easy to use and adapt for automating complex microscope image tasks. It expands our abilities for high-throughput image-based screens to a new range of biological samples, such as organoids, and will provide a foundation for bioimaging systems biology. : Optical Imaging; Technical Aspects of Cell Biology; Bioinformatics Subject Areas: Optical Imaging, Technical Aspects of Cell Biology, Bioinformatics

Abstract Discovery of temporal and spatial patterns of gene expression is essential for understanding the regulatory networks and development in multicellular organisms. We analyzed the images from our large‐scale spatial expression data set of early Drosophila embryonic development and present a comprehensive computational image analysis of the expression landscape. For this study, we created an innovative virtual representation of embryonic expression patterns using an elliptically shaped mesh grid that allows us to make quantitative comparisons of gene expression using a common frame of reference. Demonstrating the power of our approach, we used gene co‐expression to identify distinct expression domains in the early embryo; the result is surprisingly similar to the fate map determined using laser ablation. We also used a clustering strategy to find genes with similar patterns and developed new analysis tools to detect variation within consensus patterns, adjacent non‐overlapping patterns, and anti‐correlated patterns. Of the 1800 genes investigated, only half had previously assigned functions. The known genes suggest developmental roles for the clusters, and identification of related patterns predicts requirements for co‐occurring biological functions.

Space travel presents unlimited opportunities for exploration and discovery, but requires better understanding of the biological consequences of long-term exposure to spaceflight. Immune function in particular is relevant for space travel. Human immune responses are weakened in space, with increased vulnerability to opportunistic infections and immune-related conditions. In addition, microorganisms can become more virulent in space, causing further challenges to health. To understand these issues better and to contribute to design of effective countermeasures, we used the Drosophila model of innate immunity to study immune responses in both hypergravity and spaceflight. Focusing on infections mediated through the conserved Toll and Imd signaling pathways, we found that hypergravity improves resistance to Toll-mediated fungal infections except in a known gravitaxis mutant of the yuri gagarin gene. These results led to the first spaceflight project on Drosophila immunity, in which flies that developed to adulthood in microgravity were assessed for immune responses by transcription profiling on return to Earth. Spaceflight alone altered transcription, producing activation of the heat shock stress system. Space flies subsequently infected by fungus failed to activate the Toll pathway. In contrast, bacterial infection produced normal activation of the Imd pathway. We speculate on possible linkage between functional Toll signaling and the heat shock chaperone system. Our major findings are that hypergravity and spaceflight have opposing effects, and that spaceflight produces stress-related transcriptional responses and results in a specific inability to mount a Toll-mediated infection response.

Cis-regulatory modules that drive precise spatial-temporal patterns of gene expression are central to the process of metazoan development. We describe a new computational strategy to annotate genomic sequences based on their "pattern generating potential" and to produce quantitative descriptions of transcriptional regulatory networks at the level of individual protein-module interactions. We use this approach to convert the qualitative understanding of interactions that regulate Drosophila segmentation into a network model in which a confidence value is associated with each transcription factor-module interaction. Sequence information from multiple Drosophila species is integrated with transcription factor binding specificities to determine conserved binding site frequencies across the genome. These binding site profiles are combined with transcription factor expression information to create a model to predict module activity patterns. This model is used to scan genomic sequences for the potential to generate all or part of the expression pattern of a nearby gene, obtained from available gene expression databases. Interactions between individual transcription factors and modules are inferred by a statistical method to quantify a factor's contribution to the module's pattern generating potential. We use these pattern generating potentials to systematically describe the location and function of known and novel cis-regulatory modules in the segmentation network, identifying many examples of modules predicted to have overlapping expression activities. Surprisingly, conserved transcription factor binding site frequencies were as effective as experimental measurements of occupancy in predicting module expression patterns or factor-module interactions. Thus, unlike previous module prediction methods, this method predicts not only the location of modules but also their spatial activity pattern and the factors that directly determine this pattern. As databases of transcription factor specificities and in vivo gene expression patterns grow, analysis of pattern generating potentials provides a general method to decode transcriptional regulatory sequences and networks.