Anti-GD2 CAR-NKT cells in patients with relapsed or refractory neuroblastoma: an interim analysis.

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 × 106 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.

Combined analysis of microbial metagenomic and metatranscriptomic sequencing data to assess in situ physiological conditions in the premature infant gut.

Microbes alter their transcriptomic profiles in response to the environment. The physiological conditions experienced by a microbial community can thus be inferred using meta-transcriptomic sequencing by comparing transcription levels of specifically chosen genes. However, this analysis requires accurate reference genomes to identify the specific genes from which RNA reads originate. In addition, such an analysis should avoid biases in transcript counts related to differences in organism abundance. In this study we describe an approach to address these difficulties. Sample-specific meta-genomic assembled genomes (MAGs) were used as reference genomes to accurately identify the origin of RNA reads, and transcript ratios of genes with opposite transcription responses were compared to eliminate biases related to differences in organismal abundance, an approach hereafter named the "diametric ratio" method. We used this approach to probe the environmental conditions experienced by Escherichia spp. in the gut of 4 premature infants, 2 of whom developed necrotizing enterocolitis (NEC), a severe inflammatory intestinal disease. We analyzed twenty fecal samples taken from four premature infants (4-6 time points from each infant), and found significantly higher diametric ratios of genes associated with low oxygen levels in samples of infants later diagnosed with NEC than in samples without NEC. We also show this method can be used for examining other physiological conditions, such as exposure to nitric oxide and osmotic pressure. These study results should be treated with caution, due to the presence of confounding factors that might also distinguish between NEC and control infants. Nevertheless, together with benchmarking analyses, we show here that the diametric ratio approach can be applied for evaluating the physiological conditions experienced by microbes in situ. Results from similar studies can be further applied for designing diagnostic methods to detect NEC in its early developmental stages.

Model of personalized postprandial glycemic response to food developed for an Israeli cohort predicts responses in Midwestern American individuals.

BACKGROUND: Controlled glycemic concentrations are associated with a lower risk of conditions such as cardiovascular disease and diabetes. Models commonly used to guide interventions to control the glycemic response to food have low efficacy, with recent clinical guidelines arguing for the use of personalized approaches. OBJECTIVE: We tested the efficacy of a predictive model of personalized postprandial glycemic response to foods that was developed with an Israeli cohort and that takes into consideration food components and specific features, including the microbiome, when applied to individuals from the Midwestern US. DESIGN: We recruited 327 individuals for this study. Participants provided information regarding lifestyle, dietary habits, and health, as well as a stool sample for characterization of their gut microbiome. Participants were connected to continuous glucose monitors for 6 d, and the glycemic response to meals logged during this time was computed. The ability of a model trained using meals logged by the Israeli cohort to correctly predict glycemic responses in the Midwestern cohort was assessed and compared with that of a model trained using meals logged by both cohorts. RESULTS: When trained on the Israeli cohort meals only, model performance for predicting responses of individuals in the Midwestern cohort was better (R = 0.596) than that observed for models taking into consideration the carbohydrate (R = 0.395) or calorie content of the meals alone (R = 0.336). Performance increased (R = 0.618) when the model was trained on meals from both cohorts, likely because of the observed differences in age distribution, diet, and microbiome. CONCLUSIONS: We show that the modeling framework described in Zeevi et al. for an Israeli cohort is applicable to a Midwestern population, and outperforms commonly used approaches for the control of blood glucose responses. The adaptation of the model to the Midwestern cohort further enhances performance and is a promising means for designing effective nutritional interventions to control glycemic responses to foods. This trial was registered at clinicaltrials.gov as NCT02945514.

Assessment of a Personalized Approach to Predicting Postprandial Glycemic Responses to Food Among Individuals Without Diabetes.

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.

Evidence for persistent and shared bacterial strains against a background of largely unique gut colonization in hospitalized premature infants.

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.

Gut bacteria are rarely shared by co-hospitalized premature infants, regardless of necrotizing enterocolitis development.

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.

Two DNA-encoded strategies for increasing expression with opposing effects on promoter dynamics and transcriptional noise.

Individual cells from a genetically identical population exhibit substantial variation in gene expression. A significant part of this variation is due to noise in the process of transcription that is intrinsic to each gene, and is determined by factors such as the rate with which the promoter transitions between transcriptionally active and inactive states, and the number of transcripts produced during the active state. However, we have a limited understanding of how the DNA sequence affects such promoter dynamics. Here, we used single-cell time-lapse microscopy to compare the effect on transcriptional dynamics of two distinct types of sequence changes in the promoter that can each increase the mean expression of a cell population by similar amounts but through different mechanisms. We show that increasing expression by strengthening a transcription factor binding site results in slower promoter dynamics and higher noise as compared with increasing expression by adding nucleosome-disfavoring sequences. Our results suggest that when achieving the same mean expression, the strategy of using stronger binding sites results in a larger number of transcripts produced from the active state, whereas the strategy of adding nucleosome-disfavoring sequences results in a higher frequency of promoter transitions between active and inactive states. In the latter strategy, this increased sampling of the active state likely reduces the expression variability of the cell population. Our study thus demonstrates the effect of cis-regulatory elements on expression variability and points to concrete types of sequence changes that may allow partial decoupling of expression level and noise.

Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters.

Despite extensive research, our understanding of the rules according to which cis-regulatory sequences are converted into gene expression is limited. We devised a method for obtaining parallel, highly accurate gene expression measurements from thousands of designed promoters and applied it to measure the effect of systematic changes in the location, number, orientation, affinity and organization of transcription-factor binding sites and nucleosome-disfavoring sequences. Our analyses reveal a clear relationship between expression and binding-site multiplicity, as well as dependencies of expression on the distance between transcription-factor binding sites and gene starts which are transcription-factor specific, including a striking ∼10-bp periodic relationship between gene expression and binding-site location. We show how this approach can measure transcription-factor sequence specificities and the sensitivity of transcription-factor sites to the surrounding sequence context, and compare the activity of 75 yeast transcription factors. Our method can be used to study both cis and trans effects of genotype on transcriptional, post-transcriptional and translational control.

Manipulating nucleosome disfavoring sequences allows fine-tune regulation of gene expression in yeast.

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.

Compensation for differences in gene copy number among yeast ribosomal proteins is encoded within their promoters.

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.

Incorporating nucleosomes into thermodynamic models of transcription regulation.

Transcriptional control is central to many cellular processes, and, consequently, much effort has been devoted to understanding its underlying mechanisms. The organization of nucleosomes along promoter regions is important for this process, since most transcription factors cannot bind nucleosomal sequences and thus compete with nucleosomes for DNA access. This competition is governed by the relative concentrations of nucleosomes and transcription factors and by their respective sequence binding preferences. However, despite its importance, a mechanistic understanding of the quantitative effects that the competition between nucleosomes and factors has on transcription is still missing. Here we use a thermodynamic framework based on fundamental principles of statistical mechanics to explore theoretically the effect that different nucleosome organizations along promoters have on the activation dynamics of promoters in response to varying concentrations of the regulating factors. We show that even simple landscapes of nucleosome organization reproduce experimental results regarding the effect of nucleosomes as general repressors and as generators of obligate binding cooperativity between factors. Our modeling framework also allows us to characterize the effects that various sequence elements of promoters have on the induction threshold and on the shape of the promoter activation curves. Finally, we show that using only sequence preferences for nucleosomes and transcription factors, our model can also predict expression behavior of real promoter sequences, thereby underscoring the importance of the interplay between nucleosomes and factors in determining expression kinetics.

Predicting expression patterns from regulatory sequence in Drosophila segmentation.

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.