High-throughput transcriptomics of 409 bacteria-drug pairs reveals drivers of gut microbiota perturbation.

Many drugs can perturb the gut microbiome, potentially leading to negative health consequences. However, mechanisms of most microorganism-drug responses have not been elucidated at the genetic level. Using high-throughput bacterial transcriptomics, we systematically characterized the gene expression profiles of prevalent human gut bacteria exposed to the most frequently prescribed orally administered pharmaceuticals. Across >400 drug-microorganism pairs, significant and reproducible transcriptional responses were observed, including pathways involved in multidrug resistance, metabolite transport, tartrate metabolism and riboflavin biosynthesis. Importantly, we discovered that statin-mediated upregulation of the AcrAB-TolC efflux pump in Bacteroidales species enhances microbial sensitivity to vitamin A and secondary bile acids. Moreover, gut bacteria carrying acrAB-tolC genes are depleted in patients taking simvastatin, suggesting that drug-efflux interactions generate collateral toxicity that depletes pump-containing microorganisms from patient microbiomes. This study provides a resource to further understand the drivers of drug-mediated microbiota shifts for better informed clinical interventions.

High-throughput microbial culturomics using automation and machine learning.

Pure bacterial cultures remain essential for detailed experimental and mechanistic studies in microbiome research, and traditional methods to isolate individual bacteria from complex microbial ecosystems are labor-intensive, difficult-to-scale and lack phenotype-genotype integration. Here we describe an open-source high-throughput robotic strain isolation platform for the rapid generation of isolates on demand. We develop a machine learning approach that leverages colony morphology and genomic data to maximize the diversity of microbes isolated and enable targeted picking of specific genera. Application of this platform on fecal samples from 20 humans yields personalized gut microbiome biobanks totaling 26,997 isolates that represented >80% of all abundant taxa. Spatial analysis on >100,000 visually captured colonies reveals cogrowth patterns between Ruminococcaceae, Bacteroidaceae, Coriobacteriaceae and Bifidobacteriaceae families that suggest important microbial interactions. Comparative analysis of 1,197 high-quality genomes from these biobanks shows interesting intra- and interpersonal strain evolution, selection and horizontal gene transfer. This culturomics framework should empower new research efforts to systematize the collection and quantitative analysis of imaging-based phenotypes with high-resolution genomics data for many emerging microbiome studies.

Robust direct digital-to-biological data storage in living cells.

DNA has been the predominant information storage medium for biology and holds great promise as a next-generation high-density data medium in the digital era. Currently, the vast majority of DNA-based data storage approaches rely on in vitro DNA synthesis. As such, there are limited methods to encode digital data into the chromosomes of living cells in a single step. Here, we describe a new electrogenetic framework for direct storage of digital data in living cells. Using an engineered redox-responsive CRISPR adaptation system, we encoded binary data in 3-bit units into CRISPR arrays of bacterial cells by electrical stimulation. We demonstrate multiplex data encoding into barcoded cell populations to yield meaningful information storage and capacity up to 72 bits, which can be maintained over many generations in natural open environments. This work establishes a direct digital-to-biological data storage framework and advances our capacity for information exchange between silicon- and carbon-based entities.

The effect of short-course antibiotics on the resistance profile of colonizing gut bacteria in the ICU: a prospective cohort study.

BACKGROUND: The need for early antibiotics in the intensive care unit (ICU) is often balanced against the goal of antibiotic stewardship. Long-course antibiotics increase the burden of antimicrobial resistance within colonizing gut bacteria, but the dynamics of this process are not fully understood. We sought to determine how short-course antibiotics affect the antimicrobial resistance phenotype and genotype of colonizing gut bacteria in the ICU by performing a prospective cohort study with assessments of resistance at ICU admission and exactly 72 h later. METHODS: Deep rectal swabs were performed on 48 adults at the time of ICU admission and exactly 72 h later, including patients who did and did not receive antibiotics. To determine resistance phenotype, rectal swabs were cultured for methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus (VRE). In addition, Gram-negative bacterial isolates were cultured against relevant antibiotics. To determine resistance genotype, quantitative PCR (qPCR) was performed from rectal swabs for 87 established resistance genes. Within-individual changes in antimicrobial resistance were calculated based on culture and qPCR results and correlated with exposure to relevant antibiotics (e.g., did ß-lactam antibiotic exposure associate with a detectable change in ß-lactam resistance over this 72-h period?). RESULTS: Of 48 ICU patients, 41 (85%) received antibiotics. Overall, there was no increase in the antimicrobial resistance profile of colonizing gut bacteria during the 72-h study period. There was also no increase in antimicrobial resistance after stratification by receipt of antibiotics (i.e., no detectable increase in ß-lactam, vancomycin, or macrolide resistance regardless of whether patients received those same antibiotics). This was true for both culture and PCR. Antimicrobial resistance pattern at ICU admission strongly predicted resistance pattern after 72 h. CONCLUSIONS: Short-course ICU antibiotics made little detectable difference in the antimicrobial resistance pattern of colonizing gut bacteria over 72 h in the ICU. This provides an improved understanding of the dynamics of antimicrobial resistance in the ICU and some reassurance that short-course antibiotics may not adversely impact the stewardship goal of reducing antimicrobial resistance.

Macroecological dynamics of gut microbiota.

The gut microbiota is now widely recognized as a dynamic ecosystem that plays an important role in health and disease. Although current sequencing technologies make it possible to explore how relative abundances of host-associated bacteria change over time, the biological processes governing microbial dynamics remain poorly understood. Therefore, as in other ecological systems, it is important to identify quantitative relationships describing various aspects of gut microbiota dynamics. In the present study, we use multiple high-resolution time series data obtained from humans and mice to demonstrate that, despite their inherent complexity, gut microbiota dynamics can be characterized by several robust scaling relationships. Interestingly, the observed patterns are highly similar to those previously identified across diverse ecological communities and economic systems, including the temporal fluctuations of animal and plant populations and the performance of publicly traded companies. Specifically, we find power-law relationships describing short- and long-term changes in gut microbiota abundances, species residence and return times, and the correlation between the mean and the temporal variance of species abundances. The observed scaling laws are altered in mice receiving different diets and are affected by context-specific perturbations in humans. We use the macroecological relationships to reveal specific bacterial taxa, the dynamics of which are substantially perturbed by dietary and environmental changes. Overall, our results suggest that a quantitative macroecological framework will be important for characterizing and understanding the complex dynamics of diverse microbial communities.

Recording mobile DNA in the gut microbiota using an Escherichia coli CRISPR-Cas spacer acquisition platform.

The flow of genetic material between bacteria is central to the adaptation and evolution of bacterial genomes. However, our knowledge about DNA transfer within complex microbiomes is lacking, with most studies of horizontal gene transfer (HGT) relying on bioinformatic analyses of genetic elements maintained on evolutionary timescales or experimental measurements of phenotypically trackable markers. Here, we utilize the CRISPR-Cas spacer acquisition process to detect DNA acquisition events from complex microbiota in real-time and at nucleotide resolution. In this system, an E. coli recording strain is exposed to a microbial sample and spacers are acquired from transferred plasmids and permanently stored in genomic CRISPR arrays. Sequencing and analysis of acquired spacers enables identification of the transferred plasmids. This approach allowed us to identify individual mobile elements without relying on phenotypic markers or post-transfer replication. We found that HGT into the recording strain in human clinical fecal samples can be extensive and is driven by different plasmid types, with the IncX type being the most actively transferred.

Scalable and cost-effective ribonuclease-based rRNA depletion for transcriptomics.

Bacterial RNA sequencing (RNA-seq) is a powerful approach for quantitatively delineating the global transcriptional profiles of microbes in order to gain deeper understanding of their physiology and function. Cost-effective bacterial RNA-seq requires efficient physical removal of ribosomal RNA (rRNA), which otherwise dominates transcriptomic reads. However, current methods to effectively deplete rRNA of diverse non-model bacterial species are lacking. Here, we describe a probe and ribonuclease based strategy for bacterial rRNA removal. We implemented the method using either chemically synthesized oligonucleotides or amplicon-based single-stranded DNA probes and validated the technique on three novel gut microbiota isolates from three distinct phyla. We further showed that different probe sets can be used on closely related species. We provide a detailed methods protocol, probe sets for >5000 common microbes from RefSeq, and an online tool to generate custom probe libraries. This approach lays the groundwork for large-scale and cost-effective bacterial transcriptomics studies.

Spatial metagenomic characterization of microbial biogeography in the gut.

Spatial structuring is important for the maintenance of natural ecological systems1,2. Many microbial communities, including the gut microbiome, display intricate spatial organization3-9. Mapping the biogeography of bacteria can shed light on interactions that underlie community functions10-12, but existing methods cannot accommodate the hundreds of species that are found in natural microbiomes13-17. Here we describe metagenomic plot sampling by sequencing (MaPS-seq), a culture-independent method to characterize the spatial organization of a microbiome at micrometer-scale resolution. Intact microbiome samples are immobilized in a gel matrix and cryofractured into particles. Neighboring microbial taxa in the particles are then identified by droplet-based encapsulation, barcoded 16S rRNA amplification and deep sequencing. Analysis of three regions of the mouse intestine revealed heterogeneous microbial distributions with positive and negative co-associations between specific taxa. We identified robust associations between Bacteroidales taxa in all gut compartments and showed that phylogenetically clustered local regions of bacteria were associated with a dietary perturbation. Spatial metagenomics could be used to study microbial biogeography in complex habitats.

Quantifying spatiotemporal variability and noise in absolute microbiota abundances using replicate sampling.

Metagenomic sequencing has enabled detailed investigation of diverse microbial communities, but understanding their spatiotemporal variability remains an important challenge. Here, we present decomposition of variance using replicate sampling (DIVERS), a method based on replicate sampling and spike-in sequencing. The method quantifies the contributions of temporal dynamics, spatial sampling variability, and technical noise to the variances and covariances of absolute bacterial abundances. We applied DIVERS to investigate a high-resolution time series of the human gut microbiome and a spatial survey of a soil bacterial community in Manhattan's Central Park. Our analysis showed that in the gut, technical noise dominated the abundance variability for nearly half of the detected taxa. DIVERS also revealed substantial spatial heterogeneity of gut microbiota, and high temporal covariances of taxa within the Bacteroidetes phylum. In the soil community, spatial variability primarily contributed to abundance fluctuations at short time scales (weeks), while temporal variability dominated at longer time scales (several months).

Rewiring bacterial two-component systems by modular DNA-binding domain swapping.

Two-component systems (TCSs) are the largest family of multi-step signal transduction pathways and valuable sensors for synthetic biology. However, most TCSs remain uncharacterized or difficult to harness for applications. Major challenges are that many TCS output promoters are unknown, subject to cross-regulation, or silent in heterologous hosts. Here, we demonstrate that the two largest families of response regulator DNA-binding domains can be interchanged with remarkable flexibility, enabling the corresponding TCSs to be rewired to synthetic output promoters. We exploit this plasticity to eliminate cross-regulation, un-silence a gram-negative TCS in a gram-positive host, and engineer a system with over 1,300-fold activation. Finally, we apply DNA-binding domain swapping to screen uncharacterized Shewanella oneidensis TCSs in Escherichia coli, leading to the discovery of a previously uncharacterized pH sensor. This work should accelerate fundamental TCS studies and enable the engineering of a large family of genetically encoded sensors with diverse applications.

DNA-based memory devices for recording cellular events.

Measuring biological data across time and space is critical for understanding complex biological processes and for various biosurveillance applications. However, such data are often inaccessible or difficult to directly obtain. Less invasive, more robust and higher-throughput biological recording tools are needed to profile cells and their environments. DNA-based cellular recording is an emerging and powerful framework for tracking intracellular and extracellular biological events over time across living cells and populations. Here, we review and assess DNA recorders that utilize CRISPR nucleases, integrases and base-editing strategies, as well as recombinase and polymerase-based methods. Quantitative characterization, modelling and evaluation of these DNA-recording modalities can guide their design and implementation for specific application areas.

Multiplex recording of cellular events over time on CRISPR biological tape.

Although dynamics underlie many biological processes, our ability to robustly and accurately profile time-varying biological signals and regulatory programs remains limited. Here we describe a framework for storing temporal biological information directly in the genomes of a cell population. We developed a "biological tape recorder" in which biological signals trigger intracellular DNA production that is then recorded by the CRISPR-Cas adaptation system. This approach enables stable recording over multiple days and accurate reconstruction of temporal and lineage information by sequencing CRISPR arrays. We further demonstrate a multiplexing strategy to simultaneously record the temporal availability of three metabolites (copper, trehalose, and fucose) in the environment of a cell population over time. This work enables the temporal measurement of dynamic cellular states and environmental changes and suggests new applications for chronicling biological events on a large scale.

Engineering bacterial thiosulfate and tetrathionate sensors for detecting gut inflammation.

There is a groundswell of interest in using genetically engineered sensor bacteria to study gut microbiota pathways, and diagnose or treat associated diseases. Here, we computationally identify the first biological thiosulfate sensor and an improved tetrathionate sensor, both two-component systems from marine Shewanella species, and validate them in laboratory Escherichia coli Then, we port these sensors into a gut-adapted probiotic E. coli strain, and develop a method based upon oral gavage and flow cytometry of colon and fecal samples to demonstrate that colon inflammation (colitis) activates the thiosulfate sensor in mice harboring native gut microbiota. Our thiosulfate sensor may have applications in bacterial diagnostics or therapeutics. Finally, our approach can be replicated for a wide range of bacterial sensors and should thus enable a new class of minimally invasive studies of gut microbiota pathways.

Manipulating Bacterial Communities by in situ Microbiome Engineering.

Microbial communities inhabit our entire planet and have a crucial role in biogeochemical processes, agriculture, biotechnology, and human health. Here, we argue that 'in situ microbiome engineering' represents a new paradigm of community-scale genetic and microbial engineering. We discuss contemporary applications of this approach to directly add, remove, or modify specific sets of functions and alter community-level properties in terrestrial, aquatic, and host-associated microbial communities. Specifically, we highlight emerging in situ genome engineering approaches as tractable techniques to manipulate microbial communities with high specificity and efficacy. Finally, we describe opportunities for technological innovation and ways to bridge existing knowledge gaps to accelerate the development of in situ approaches for microbiome manipulations.

Refactoring and optimization of light-switchable Escherichia coli two-component systems.

Light-switchable proteins enable unparalleled control of molecular biological processes in live organisms. Previously, we have engineered red/far-red and green/red photoreversible two-component signal transduction systems (TCSs) with transcriptional outputs in E. coli and used them to characterize and control synthetic gene circuits with exceptional quantitative, temporal, and spatial precision. However, the broad utility of these light sensors is limited by bulky DNA encoding, incompatibility with commonly used ligand-responsive transcription factors, leaky output in deactivating light, and less than 10-fold dynamic range. Here, we compress the four genes required for each TCS onto two streamlined plasmids and replace all chemically inducible and evolved promoters with constitutive, engineered versions. Additionally, we systematically optimize the expression of each sensor histidine kinase and response regulator, and redesign both pathway output promoters, resulting in low leakiness and 72- and 117-fold dynamic range, respectively. These second-generation light sensors can be used to program the expression of more genes over a wider range and can be more easily combined with additional plasmids or moved to different host strains. This work demonstrates that bacterial TCSs can be optimized to function as high-performance sensors for scientific and engineering applications.