Despite much research, our understanding of the rules by which cis-regulatory sequences are translated into expression levels is still lacking. We devised a method for obtaining parallel and highly accurate expression measurements of thousands of fully designed promoters, and applied it to measure the effect of systematic changes to location, number, orientation, affinity and organization of transcription factor (TF) binding sites and of nucleosome disfavoring sequences. Our analyses reveal a clear relationship between expression and binding site number, and TF-specific dependencies of expression on the distance between sites and gene starts including a striking ~10bp periodic relationship. We also demonstrate the utility of our approach for measuring TF sequence specificities and sensitivity of TF sites to surrounding sequence context, and for profiling the activity of most yeast transcription factors. Our method is readily applicable for studying both the cis and trans effects of genotype on transcriptional, post-transcriptional, and translational control.

The establishment of complex expression patterns at precise times and locations is key to metazoan development, yet a mechanistic understanding of the underlying transcription control networks is still missing. Here we describe a novel thermodynamic model that computes expression patterns as a function of cis-regulatory sequence and of the binding-site preferences and expression of participating transcription factors. We apply this model to the segmentation gene network of Drosophila melanogaster and find that it predicts expression patterns of cis-regulatory modules with remarkable accuracy, demonstrating that positional information is encoded in the regulatory sequence and input factor distribution. Our analysis reveals that both strong and weaker binding sites contribute, leading to high occupancy of the module DNA, and conferring robustness against mutation; short-range homotypic clustering of weaker sites facilitates cooperative binding, which is necessary to sharpen the patterns. Our computational framework is generally applicable to most protein-DNA interaction systems.

Understanding how precise control of gene expression is specified within regulatory DNA sequences is a key challenge with far-reaching implications. Many studies have focused on the regulatory role of transcription factor-binding sites. Here, we explore the transcriptional effects of different elements, nucleosome-disfavoring sequences and, specifically, poly(dA:dT) tracts that are highly prevalent in eukaryotic promoters. By measuring promoter activity for a large-scale promoter library, designed with systematic manipulations to the properties and spatial arrangement of poly(dA:dT) tracts, we show that these tracts significantly and causally affect transcription. We show that manipulating these elements offers a general genetic mechanism, applicable to promoters regulated by different transcription factors, for tuning expression in a predictable manner, with resolution that can be even finer than that attained by altering transcription factor sites. Overall, our results advance the understanding of the regulatory code and suggest a potential mechanism by which promoters yielding prespecified expression patterns can be designed.

Premature infants are highly vulnerable to aberrant gastrointestinal tract colonization, a process that may lead to diseases like necrotizing enterocolitis. Thus, spread of potential pathogens among hospitalized infants is of great concern. Here, we reconstructed hundreds of high-quality genomes of microorganisms that colonized co-hospitalized premature infants, assessed their metabolic potential, and tracked them over time to evaluate bacterial strain dispersal among infants. We compared microbial communities in infants who did and did not develop necrotizing enterocolitis. Surprisingly, while potentially pathogenic bacteria of the same species colonized many infants, our genome-resolved analysis revealed that strains colonizing each baby were typically distinct. In particular, no strain was common to all infants who developed necrotizing enterocolitis. The paucity of shared gut colonizers suggests the existence of significant barriers to the spread of bacteria among infants. Importantly, we demonstrate that strain-resolved comprehensive community analysis can be accomplished on potentially medically relevant time scales.DOI: http://dx.doi.org/10.7554/eLife.05477.001

IMPORTANCE Emerging evidence suggests that postprandial glycemic responses (PPGRs) to food may be influenced by and predicted according to characteristics unique to each individual, including anthropometric and microbiome variables. Interindividual diversity in PPGRs to food requires a personalized approach for the maintenance of healthy glycemic levels. OBJECTIVES To describe and predict the glycemic responses of individuals to a diverse array of foods using a model that considers the physiology and microbiome of the individual in addition to the characteristics of the foods consumed. DESIGN, SETTING, AND PARTICIPANTS This cohort study using a personalized predictive model enrolled 327 individuals without diabetes from October 11, 2016, to December 13, 2017, in Minnesota and Florida to be part of a study lasting 6 days. The study measured anthropometric variables, described the gut microbial composition, and assessed blood glucose levels every 5 minutes using a continuous glucose monitor. Participants logged their food and activity information for the duration of the study. A predictive model of individualized PPGRs to a diverse array of foods was trained and applied. MAIN OUTCOMES AND MEASURES Glycemic responses to food consumed over 6 days for each participant. The predictive model of personalized PPGRs considered individual features, including the microbiome, in addition to the features of the foods consumed. RESULTS Postprandial response to the same foods varied across 327 individuals (mean [SD] age, 45 [12] years; 78.0% female). A model predicting each individual's responses to food that considers several individual factors in addition to food features had better overall performance (R = 0.62) than current standard-of-care approaches using nutritional content alone (R = 0.34 for calories and R = 0.40 for carbohydrates) to control postprandial glycemic levels. CONCLUSIONS AND RELEVANCE Across the cohort of adults without diabetes who were examined, a personalized predictive model that considers unique features of the individual, such as clinical characteristics, physiological variables, and the microbiome, in addition to nutrient content was more predictive than current dietary approaches that focus only on the calorie or carbohydrate content of foods. Providing individuals with tools to manage their glycemic responses to food based on personalized predictions of their PPGRs may allow them to maintain their blood glucose levels within limits associated with good health.

Coordinate regulation of ribosomal protein (RP) genes is key for controlling cell growth. In yeast, it is unclear how this regulation achieves the required equimolar amounts of the different RP components, given that some RP genes exist in duplicate copies, while others have only one copy. Here, we tested whether the solution to this challenge is partly encoded within the DNA sequence of the RP promoters, by fusing 110 different RP promoters to a fluorescent gene reporter, allowing us to robustly detect differences in their promoter activities that are as small as~10%. We found that single-copy RP promoters have significantly higher activities, suggesting that proper RP stoichiometry is indeed partly encoded within the RP promoters. Notably, we also partially uncovered how this regulation is encoded by finding that RP promoters with higher activity have more nucleosome-disfavoring sequences and characteristic spatial organizations of these sequences and of binding sites for key RP regulators. Mutations in these elements result in a significant decrease of RP promoter activity. Thus, our results suggest that intrinsic (DNA-dependent) nucleosome organization may be a key mechanism by which genomes encode biologically meaningful promoter activities. Our approach can readily be applied to uncover how transcriptional programs of other promoters are encoded.

Vα24-invariant natural killer T (NKT) cells have shown potent anti-tumor properties in murine tumor models and have been linked to favorable outcomes in patients with cancer. However, low numbers of these cells in humans have hindered their clinical applications. Here we report interim results from all three patients enrolled on dose level 1 in a phase 1 dose-escalation trial of autologous NKT cells engineered to co-express a GD2-specific chimeric antigen receptor (CAR) with interleukin-15 in children with relapsed or resistant neuroblastoma (NCT03294954). Primary and secondary objectives were to assess safety and anti-tumor responses, respectively, with immune response evaluation as an additional objective. We ex vivo expanded highly pure NKT cells (mean ± s.d., 94.7 ± 3.8%) and treated patients with 3 × 10 6 CAR-NKT cells per square meter of body surface area after lymphodepleting conditioning with cyclophosphamide/fludarabine (Cy/Flu). Cy/Flu conditioning was the probable cause for grade 3-4 hematologic adverse events, as they occurred before CAR-NKT cell infusion, and no dose-limiting toxicities were observed. CAR-NKT cells expanded in vivo, localized to tumors and, in one patient, induced an objective response with regression of bone metastatic lesions. These initial results suggest that CAR-NKT cells can be expanded to clinical scale and safely applied to treat patients with cancer.

The potentially critical stage of initial gut colonization in premature infants occurs in the hospital environment, where infants are exposed to a variety of hospital-associated bacteria. Because few studies of microbial communities are strain-resolved, we know little about the extent to which specific strains persist in the hospital environment and disperse among infants. To study this, we compared 304 near-complete genomes reconstructed from fecal samples of 21 infants hospitalized in the same intensive care unit in two cohorts, over 3 years apart. The genomes represent 159 distinct bacterial strains, only 14 of which occurred in multiple infants. Enterococcus faecalis and Staphylococcus epidermidis, common infant gut colonists, exhibit diversity comparable to that of reference strains, inline with introduction of strains from infant-specific sources rather than a hospital strain pool. Unlike other infants, a pair of sibling infants shared multiple strains, even after extensive antibiotic administration, suggesting overlapping strain-sources and/or genetic selection drive microbiota similarities. Interestingly, however, five strains were detected in infants hospitalized three years apart. Three of these were also detected in multiple infants in the same year. This finding of a few widely dispersed and persistent bacterial colonizers despite overall low potential for strain dispersal among infants has implications for understanding and directing healthy colonization.