Higher-Order Interactions Dampen Pairwise Competition in the Zebrafish Gut Microbiome.

The microbial communities resident in animal intestines are composed of multiple species that together play important roles in host development, health, and disease. Due to the complexity of these communities and the difficulty of characterizing them in situ, the determinants of microbial composition remain largely unknown. Further, it is unclear for many multispecies consortia whether their species-level makeup can be predicted based on an understanding of pairwise species interactions or whether higher-order interactions are needed to explain emergent compositions. To address this, we examine commensal intestinal microbes in larval zebrafish, initially raised germfree, to allow the introduction of controlled combinations of bacterial species. Using a dissection and plating assay, we demonstrate the construction of communities of one to five bacterial species and show that the outcomes from the two-species competitions fail to predict species abundances in more complex communities. With multiple species present, interbacterial interactions become weaker, suggesting that higher-order interactions in the vertebrate gut stabilize complex communities.IMPORTANCE Understanding the rules governing the composition of the diverse microbial communities that reside in the vertebrate gut environment will enhance our ability to manipulate such communities for therapeutic ends. Synthetic microbial communities, assembled from specific combinations of microbial species in germfree animals, allow investigation of the fundamental question of whether multispecies community composition can be predicted solely based on the combined effects of interactions between pairs of species. If so, such predictability would enable the construction of communities with desired species from the bottom up. If not, the apparent higher-order interactions imply that emergent community-level characteristics are crucial. Our findings using up to five coexisting native bacterial species in larval zebrafish, a model vertebrate, provide experimental evidence for higher-order interactions and, moreover, show that these interactions promote the coexistence of microbial species in the gut.

Performance of convolutional neural networks for identification of bacteria in 3D microscopy datasets.

Three-dimensional microscopy is increasingly prevalent in biology due to the development of techniques such as multiphoton, spinning disk confocal, and light sheet fluorescence microscopies. These methods enable unprecedented studies of life at the microscale, but bring with them larger and more complex datasets. New image processing techniques are therefore called for to analyze the resulting images in an accurate and efficient manner. Convolutional neural networks are becoming the standard for classification of objects within images due to their accuracy and generalizability compared to traditional techniques. Their application to data derived from 3D imaging, however, is relatively new and has mostly been in areas of magnetic resonance imaging and computer tomography. It remains unclear, for images of discrete cells in variable backgrounds as are commonly encountered in fluorescence microscopy, whether convolutional neural networks provide sufficient performance to warrant their adoption, especially given the challenges of human comprehension of their classification criteria and their requirements of large training datasets. We therefore applied a 3D convolutional neural network to distinguish bacteria and non-bacterial objects in 3D light sheet fluorescence microscopy images of larval zebrafish intestines. We find that the neural network is as accurate as human experts, outperforms random forest and support vector machine classifiers, and generalizes well to a different bacterial species through the use of transfer learning. We also discuss network design considerations, and describe the dependence of accuracy on dataset size and data augmentation. We provide source code, labeled data, and descriptions of our analysis pipeline to facilitate adoption of convolutional neural network analysis for three-dimensional microscopy data.

Automated high-throughput light-sheet fluorescence microscopy of larval zebrafish.

Light sheet fluorescence microscopy enables fast, minimally phototoxic, three-dimensional imaging of live specimens, but is currently limited by low throughput and tedious sample preparation. Here, we describe an automated high-throughput light sheet fluorescence microscope in which specimens are positioned by and imaged within a fluidic system integrated with the sheet excitation and detection optics. We demonstrate the ability of the instrument to rapidly examine live specimens with minimal manual intervention by imaging fluorescent neutrophils over a nearly 0.3 mm3 volume in dozens of larval zebrafish. In addition to revealing considerable inter-individual variability in neutrophil number, known previously from labor-intensive methods, three-dimensional imaging allows assessment of the correlation between the bulk measure of total cellular fluorescence and the spatially resolved measure of actual neutrophil number per animal. We suggest that our simple experimental design should considerably expand the scope and impact of light sheet imaging in the life sciences.

Modernized Tools for Streamlined Genetic Manipulation and Comparative Study of Wild and Diverse Proteobacterial Lineages.

Correlating the presence of bacteria and the genes they carry with aspects of plant and animal biology is rapidly outpacing the functional characterization of naturally occurring symbioses. A major barrier to mechanistic studies is the lack of tools for the efficient genetic manipulation of wild and diverse bacterial isolates. To address the need for improved molecular tools, we used a collection of proteobacterial isolates native to the zebrafish intestinal microbiota as a testbed to construct a series of modernized vectors that expedite genetic knock-in and knockout procedures across lineages. The innovations that we introduce enhance the flexibility of conventional genetic techniques, making it easier to manipulate many different bacterial isolates with a single set of tools. We developed alternative strategies for domestication-free conjugation, designed plasmids with customizable features, and streamlined allelic exchange using visual markers of homologous recombination. We demonstrate the potential of these tools through a comparative study of bacterial behavior within the zebrafish intestine. Live imaging of fluorescently tagged isolates revealed a spectrum of distinct population structures that differ in their biogeography and dominant growth mode (i.e., planktonic versus aggregated). Most striking, we observed divergent genotype-phenotype relationships: several isolates that are predicted by genomic analysis and in vitro assays to be capable of flagellar motility do not display this trait within living hosts. Together, the tools generated in this work provide a new resource for the functional characterization of wild and diverse bacterial lineages that will help speed the research pipeline from sequencing-based correlations to mechanistic underpinnings.IMPORTANCE A great challenge in microbiota research is the immense diversity of symbiotic bacteria with the capacity to impact the lives of plants and animals. Moving beyond correlative DNA sequencing-based studies to define the cellular and molecular mechanisms by which symbiotic bacteria influence the biology of their hosts is stalling because genetic manipulation of new and uncharacterized bacterial isolates remains slow and difficult with current genetic tools. Moreover, developing tools de novo is an arduous and time-consuming task and thus represents a significant barrier to progress. To address this problem, we developed a suite of engineering vectors that streamline conventional genetic techniques by improving postconjugation counterselection, modularity, and allelic exchange. Our modernized tools and step-by-step protocols will empower researchers to investigate the inner workings of both established and newly emerging models of bacterial symbiosis.

Passive and Active Microrheology of the Intestinal Fluid of the Larval Zebrafish.

The fluids of the intestine serve as a physical barrier to pathogens, a medium for the diffusion of nutrients and metabolites, and an environment for commensal microbes. The rheological properties of intestinal mucus have therefore been the subject of many investigations, thus far limited to in vitro studies due to the difficulty of measurement in the natural context of the gut. This limitation especially hinders our understanding of how the gut microbiota interact with the intestinal space, since examination of this calls not only for in vivo measurement techniques, but for techniques that can be applied to model organisms in which the microbial state of the gut can be controlled. We have addressed this challenge with two complementary approaches. We performed passive microrheological measurements using thermally driven nanoparticles and active microrheology using micron-scale ellipsoidal magnetic microparticles, in both cases using light-sheet fluorescence microscopy to optically access the intestinal bulb of the larval zebrafish, a model vertebrate. We present viscosity measurements in germ-free animals (devoid of gut microbes), animals colonized by a single bacterial species, and conventionally reared animals, and find that in all cases, the mucin-rich intestinal liquid is well described as a Newtonian fluid. Surprisingly, despite known differences in the number of secretory cells in germ-free zebrafish and their conventional counterparts, the fluid viscosity for these two groups is very similar, as measured with either technique. Our study provides, to our knowledge, the first in vivo microrheological measurements of the intestinal space in living animals, and we comment on its implications for timescales of host-microbe interactions in the gut.