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How bacterial strains within a complex human microbiota collectively shape intestinal T cell homeostasis is not well-understood. Methods that quickly identify effector strains or species that drive specific mucosal T cell phenotypes are needed to define general principles for how the microbiota modulates host immunity. We colonize germ-free mice with defined communities of cultured strains and profile antigen-specific responses directed towards individual strains ex vivo. We find that lamina propria T cells are specific to bacterial strains at the species level and can discriminate between strains of the same species. Ex vivo restimulations consistently identify the strains within complex communities that induce Th17 responses in vivo providing the potential to shape baseline immune tone via community composition. Using an adoptive transfer model of colitis, we find that lamina propria T cells respond to different bacterial strains in conditions of inflammation versus homeostasis. Collectively, our approach represents a novel method for efficiently predicting the relative impact of individual bacterial strains within a complex community and for parsing microbiota-dependent phenotypes into component fractions.SIGNIFICANCEDetermining the mechanisms by which the gut microbiome modulates the host immune system has translational potential for treating or preventing immune mediated disease. A key challenge is identifying the immunogenic bacterial strains in the setting of a complex microbiota. We use a combination of anaerobic culturing, in vitro T cell assays, gnotobiotic mouse models, and ex vivo T cell restimulations to explore the influence of species and strain diversity on the specificity of mucosal T cells. Our approach efficiently predicts the relative impact of individual bacterial strains within a complex community and can be used to parse microbiota-dependent phenotypes into component fractions.

Despite the fundamental role of strain variation in gut microbiota function, the number of unique strains of a species that can stably colonize the human gut is still unknown. In this work, we determine the strain richness of common gut species using thousands of sequenced bacterial isolates and metagenomes. We find that strain richness varies across species, is transferable by fecal microbiota transplantation, and is low in the gut compared to other environments. Therapeutic administration of supraphysiologic numbers of strains per species only temporarily increases recipient strain richness, which subsequently converges back to the population average. These results suggest that properties of the gut ecosystem govern the number of strains of each species colonizing the gut and provide a theoretical framework for strain engraftment and replacement in fecal microbiota transplantation and defined live biotherapeutic products.

Fecal microbiota transplantation (FMT) has been successfully applied to treat recurrent Clostridium difficile infection in humans, but a precise method to measure which bacterial strains stably engraft in recipients and evaluate their association with clinical outcomes is lacking. We assembled a collection of >1,000 different bacterial strains that were cultured from the fecal samples of 22 FMT donors and recipients. Using our strain collection combined with metagenomic sequencing data from the same samples, we developed a statistical approach named Strainer for the detection and tracking of bacterial strains from metagenomic sequencing data. We applied Strainer to evaluate a cohort of 13 FMT longitudinal clinical interventions and detected stable engraftment of 71% of donor microbiota strains in recipients up to 5 years post-FMT. We found that 80% of recipient gut bacterial strains pre-FMT were eliminated by FMT and that post-FMT the strains present persisted up to 5 years later, together with environmentally acquired strains. Quantification of donor bacterial strain engraftment in recipients independently explained (precision 100%, recall 95%) the clinical outcomes (relapse or success) after initial and repeat FMT. We report a compendium of bacterial species and strains that consistently engraft in recipients over time that could be used in defined live biotherapeutic products as an alternative to FMT. Our analytical framework and Strainer can be applied to systematically evaluate either FMT or defined live bacterial therapeutic studies by quantification of strain engraftment in recipients. Quantification of gut bacterial strains after fecal microbiome transplantation using the Strainer algorithm delineates long-term stable engraftment that explains patient outcomes.

Background and aims: Immune dysregulation caused by SARS-CoV-2 infection is thought to play a pathogenic role in COVID-19. SARS-CoV-2 can infect a variety of host cells, including intestinal epithelial cells. We sought to characterize the role of the gastrointestinal immune system in the pathogenesis of the inflammatory response associated with COVID-19. Methods: We measured cytokines, inflammatory markers, viral RNA, microbiome composition and antibody responses in stool and serum samples from a prospectively enrolled cohort of 44 hospitalized COVID-19 patients. Results: SARS-CoV-2 RNA was detected in stool of 41% of patients and was found more frequently in patients with diarrhea than those without (16[44%] vs 5[19%], p=0.06). Patients who survived had lower median viral genome copies than those who did not (p=0.021). Compared to uninfected controls, COVID-19 patients had higher median fecal levels of IL-8 (166.5 vs 286.5 pg/mg; p=0.05) and lower levels of fecal IL-10 (678 vs 194 pg/mg; p

The intestinal microbiota actively converts dietary flavanols into phenolic acids, some of which are bioavailable in vivo and may promote resilience to select neurological disorders by interfering with key pathologic mechanisms. Since every person harbors a unique set of gut bacteria, we investigated the influence of the gut microbiota's interpersonal heterogeneity on the production and bioavailability of flavonoid metabolites that may interfere with the misfolding of alpha (α)-synuclein, a process that plays a central role in Parkinson's disease and other α-synucleinopathies. We generated two experimental groups of humanized gnotobiotic mice with compositionally diverse gut bacteria and orally treated the mice with a flavanol-rich preparation (FRP). The two gnotobiotic mouse groups exhibited distinct differences in the generation and bioavailability of FRP-derived microbial phenolic acid metabolites that have bioactivity towards interfering with α-synuclein misfolding or inflammation. We also demonstrated that these bioactive phenolic acids are effective in modulating the development and progression of motor dysfunction in a Drosophila model of α-synucleinopathy. Lastly, through in vitro bacterial fermentation studies, we identified select bacteria that are capable of supporting the generation of these bioavailable and bioactive phenolic acids. Outcomes from our studies provide a better understanding of how interpersonal heterogeneity in the gut microbiota differentially modulates the efficacy of dietary flavanols to protect against select pathologic mechanisms. Collectively, our findings provide the basis for future developments of probiotic, prebiotic, or synbiotic approaches for modulating the onset and/or progression of α-synucleinopathies and other neurological disorders involving protein misfolding and/or inflammation.

Accumulating evidence indicates that the health impact of dietary phenolic compounds, including the principal grape-derived polyphenols, ( + )-catechin and ( − )-epicatechin, is exerted by not only the parent compounds but also their phenolic metabolites generated by the gut microbiota. In this work, a new high-throughput, sensitive and reproducible analytical method was developed employing ultra-high performance liquid chromatography coupled with triple quadrupole tandem mass spectrometry (UHPLC-QqQ-MS/MS) for the simultaneous analysis of 16 microbial-generated phenolic acid metabolites (PAMs) along with their precursors, catechin and epicatechin. Following optimizing the solvent system, LC conditions and MS parameters, method validation was carried out to evaluate the sensitivity, selectivity, accuracy and precision of the proposed method, and to ensure promising recovery of all analytes extracted from the matrix prior to bioanalysis. Results showed that the optimized analytical method allowed successful confirmation and quantitation of all analytes under dynamic multiple reaction monitoring mode using trans-cinnamic acid-d(7) as an internal standard (I.S.). Excellent sensitivity and linearity were obtained for all analytes, with lower limits of detection (LLODs) and lower limits of quantification (LLOQs) in the ranges of 0.225–2.053 ng/mL and 0.698–8.116 ng/mL, respectively. By examining blank matrix spiked with standard mixture at different concentration levels, promising recoveries at two spiking levels (low level, 91.2–115%; high level 90.2–121%), and excellent precision (RSD < 10%) were obtained. This method was then successfully applied to an in vitro study where catechin/epicatechin-enriched broth samples were anaerobically fermented with gut microbes procured from healthy human donors. All sources of bacteria employed showed remarkable activity in metabolizing grape polyphenols and distinct variations in the production of PAMs. The successful application of this method in the in vitro fermentation assays demonstrates its suitability for high-throughput analysis of polyphenol metabolites, particularly catechin/epicatechin-derived PAMs, in biological studies.

SummaryThe population structure of strains within a bacterial species is poorly defined, despite the functional importance of strain variation in the human gut microbiota on health. Here we analyzed >1000 sequenced bacterial strains from the fecal microbiota of 47 individuals from two countries and combined them with >150,000 bacterial genomes from NCBI to quantify the strain population size of different bacterial species, as well as the frequency of finding the same strain colonized in unrelated individuals who had no opportunities for direct microbial strain transmission. Strain population sizes ranged from tens to over one-hundred thousand per species. Prevalent strains in common gut microbiota species with small population sizes were the most likely to be harbored in two or more unrelated individuals. The finite strain population size of certain species creates the opportunity to comprehensively sequence the entirety of these species’ prevalent strains and associate their presence in different individuals with health outcomes.

Fecal Microbiota Transplantation (FMT), while successful for the treatment of recurrent Clostridioides difficile (rCDI) infection, lacks a quantitative identification of the discrete bacterial strains that transmit and stably engraft in recipients, and their association with clinical outcomes. Using >1,000 unique bacterial strains isolated and sequenced from a combination of 22 FMT donors and recipients, we develop a statistical approach Strainer to detect and track sequenced bacterial strains from low depth metagenomic sequencing data. On application to 14 FMT interventions, we detect stable and high engraftment of ∼71% of gut microbiota strains in recipients at even 5-years post-transplant, a remarkably durable therapeutic from a single administration. We found differential transmission and engraftment efficacy across bacterial taxonomic groups over short and long-time scales. Although ∼80% of the original pre-FMT recipient strains were eliminated by the FMT, those strains that remain persist even 5 years later, along with newer strains acquired from the environment. The precise quantification of donor bacterial strains in recipients independently explained the clinical outcomes of early and late relapse. Our framework identifies the consistently engrafting discrete bacterial strains for use in Live Biotherapeutic Products (LBP) as a safer, scalable alternative to FMT and enables systematic evaluation of different FMT and LBP study designs.

Recent studies demonstrate that gut microbiota regulate tumor response to immune checkpoint blockade. Still, the mechanisms by which microbiota control tumor response to immunotherapy remain unclear. We colonized germ-free mice with cultured human-derived microbiota prior to tumor inoculation. While no human donor microbiota altered tumor growth, two distinct gut microbiota inhibited tumor response to anti-PD-L1. Colonization with non-responder microbiota led to reduced tumor immune cell infiltration and modified antigen presenting cell phenotype. RNA sequencing of tumor-infiltrating CD8+ T cells revealed enrichment for stem cell-like genes in non-responders and reduced effector-like expression conferring cytotoxic potential. Antibiotic depletion and microbiota transplant restored anti-PD-L1 response in non-responders, with expansion of effector cells and cytotoxicity. Concomitant blockade of TNFα similarly improved response to anti-PD-L1 and increased cytotoxicity. These results demonstrate inhibitory roles for the microbiota in checkpoint blockade and reveal the potential for microbiota transplant and TNF blockade to overcome microbiota-mediated resistance.

Background Recurrent and severe Clostridium difficile infections (CDI) are treated with fecal microbiota transplant (FMT). Uncertainty exists regarding FMT effectiveness for CDI with underlying inflammatory bowel disease (IBD) and regarding its effects on disease activity and effectiveness in transferring the donor microbiota to patients with and without IBD. Methods Subjects with and without IBD who underwent FMT for recurrent or severe CDI between 2013 and 2016 at The Mount Sinai Hospital were followed for up to 6 months. The primary outcome was CDI recurrence 6 months after FMT. Secondary outcomes were (1) CDI recurrence 2 months after FMT; (2) frequency of IBD flare after FMT; (3) microbiota engraftment after FMT; (and 4) predictors of CDI recurrence. Results One hundred thirty-four patients, 46 with IBD, were treated with FMT. Follow-up was available in 83 and 118 patients at 6 and 2 months, respectively. There was no difference in recurrence in patients with and without IBD at 6 months (38.7% vs 36.5%; P > 0.99) and 2 months (22.5% vs 17.9%; P = 0.63). Proton pump inhibitor use, severe CDI, and comorbid conditions were predictors of recurrence. Pre-FMT microbiota was not predictive of CDI recurrence. Subjects with active disease requiring medication escalation had reduced engraftment, with no difference in engraftment based on CDI recurrence or IBD endoscopic severity at FMT. Conclusions Inflammatory bowel disease did not affect CDI recurrence rates 6 months after FMT. Pre-FMT microbiota was not predictive of recurrence, and microbial engraftment was impacted in those requiring IBD treatment escalation, though not by CDI recurrence or IBD disease severity.

Fecal IgA production depends on colonization by a gut microbiota. However, the bacterial strains that drive gut IgA production remain largely unknown. By accessing the IgA-inducing capacity of a diverse set of human gut microbial strains, we identified Bacteroides ovatus as the species that best induced gut IgA production. However, this induction varied biomodally across different B. ovatus strains. The high IgA-inducing B. ovatus strains preferentially elicited more IgA production in the large intestine through both T-cell-dependent and T-cell-independent B cell-activation pathways. Remarkably, a low-IgA phenotype in mice could be robustly and consistently converted into a high-IgA phenotype by transplanting a multiplex cocktail of high IgA-inducing B. ovatus strains but not individual ones. Thus, microbial strain specificity is essential for the optimal induction of high-IgA responses in the gut. Our results highlight the critical importance of microbial strains in driving phenotype variation in the mucosal immune system and provide a strategy to robustly modify a gut immune phenotype, including IgA production.

Background and aimsPrenatal and early life bacterial colonisation is thought to play a major role in shaping the immune system. Furthermore, accumulating evidence links early life exposures to the risk of developing IBD later in life. We aimed to assess the effect of maternal IBD on the composition of the microbiome during pregnancy and on the offspring’s microbiome.MethodsWe prospectively examined the diversity and taxonomy of the microbiome of pregnant women with and without IBD and their babies at multiple time points. We evaluated the role of maternal IBD diagnosis, the mode of delivery, antibiotic use and feeding behaviour on the microbiome composition during early life. To assess the effects of IBD-associated maternal and infant microbiota on the enteric immune system, we inoculated germ-free mice (GFM) with the respective stool and profiled adaptive and innate immune cell populations in the murine intestines.ResultsPregnant women with IBD and their offspring presented with lower bacterial diversity and altered bacterial composition compared with control women and their babies. Maternal IBD was the main predictor of the microbiota diversity in the infant gut at 7, 14, 30, 60 and 90 days of life. Babies born to mothers with IBD demonstrated enrichment in Gammaproteobacteria and depletion in Bifidobacteria. Finally, GFM inoculated with third trimester IBD mother and 90-day infant stools showed significantly reduced microbial diversity and fewer class-switched memory B cells and regulatory T cells in the colon.ConclusionAberrant gut microbiota composition persists during pregnancy with IBD and alters the bacterial diversity and abundance in the infant stool. The dysbiotic microbiota triggered abnormal imprinting of the intestinal immune system in GFM.

Summary Fecal IgA production depends on colonization by a gut microbiota. However, the bacterial strains that drive gut IgA production remain largely unknown. Here, we assessed the IgA-inducing capacity of a diverse set of human gut microbial strains by monocolonizing mice with each strain. We identified Bacteroides ovatus as the species that best induced gut IgA production. However, this induction varied bimodally across different B. ovatus strains. The high IgA-inducing B. ovatus strains preferentially elicited more IgA production in the large intestine through the T cell-dependent B cell-activation pathway. Remarkably, a low-IgA phenotype in mice could be robustly and consistently converted into a high-IgA phenotype by transplanting a multiplex cocktail of high IgA-inducing B. ovatus strains but not individual ones. Our results highlight the critical importance of microbial strains in driving phenotype variation in the mucosal immune system and provide a strategy to robustly modify a gut immune phenotype, including IgA production.

Microbiota are thought to influence the development and progression of inflammatory bowel disease (IBD), but determining generalizable effects of microbiota on IBD etiology requires larger-scale functional analyses. We colonized germ-free mice with intestinal microbiotas from 30 healthy and IBD donors and determined the homeostatic intestinal T cell response to each microbiota. Compared to microbiotas from healthy donors, transfer of IBD microbiotas into germ-free mice increased numbers of intestinal Th17 cells and Th2 cells and decreased numbers of RORγt

To identify factors that regulate gut microbiota density and the impact of varied microbiota density on health, we assayed this fundamental ecosystem property in fecal samples across mammals, human disease, and therapeutic interventions. Physiologic features of the host (carrying capacity) and the fitness of the gut microbiota shape microbiota density. Therapeutic manipulation of microbiota density in mice altered host metabolic and immune homeostasis. In humans, gut microbiota density was reduced in Crohn’s disease, ulcerative colitis, and ileal pouch-anal anastomosis. The gut microbiota in recurrent Clostridium difficile infection had lower density and reduced fitness that were restored by fecal microbiota transplantation. Understanding the interplay between microbiota and disease in terms of microbiota density, host carrying capacity, and microbiota fitness provide new insights into microbiome structure and microbiome targeted therapeutics.Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

To examine the functional contribution of Inflammatory Bowel Disease (IBD) microbes to immune homeostasis and colitis, we colonized unchallenged and colitis-susceptible germ-free mice with over twenty human intestinal microbiotas from healthy and IBD donors. Compared to healthy microbiotas, IBD microbiotas led to expanded RORγt+Th17 cells and reduced RORγt+Treg in the gut of unchallenged gnotobiotic mice and increased disease severity in colitis-susceptible mice. The proportions of RORγt+Th17 and RORγt+Treg induced by each microbiota were highly predictive of the human disease status and strongly correlated with disease severity in colitis-susceptible mice colonized with the same human microbiotas. The transmittable functional potential of IBD microbes suggests a mechanism for a microbial contribution to IBD pathogenesis and a potential route for its treatment and prevention.

Background & AimsRecurrent and refractory Clostridium difficile infections (CDI) are effectively treated with fecal microbiota transplant (FMT). Uncertainty exists regarding the effectiveness of FMT for CDI with underlying inflammatory bowel disease (IBD), its effects on disease activity and its effectiveness transferring the donor microbiome to patients with and without IBD. This study aims to determine FMTs effectiveness in subjects with and without IBD, its impact on IBD activity, the level of microbiome engraftment, and predictors of CDI recurrence.MethodsSubjects with and without IBD who underwent FMT for recurrent or refractory CDI between 2013 and 2016 at The Mount Sinai Hospital were followed for up to 6 months. The primary outcome was CDI recurrence 6 months after FMT. Secondary outcomes were (1) CDI recurrence 2 months after FMT; (2) Frequency of IBD flare after FMT; (3) Microbiome engraftment after FMT; (4) Predictors of CDI recurrence.ResultsOverall, 134 patients, 46 with IBD, were treated with FMT. There was no difference in recurrence in patients with and without IBD at 2 months (22.5% vs 17.9%; p=0.63) and 6 months (38.7% vs 36.5%; p>0.99). Proton pump inhibitor use, severe CDI, and comorbid conditions were predictors of recurrence. The pre-FMT microbiome was not predictive of CDI recurrence. Subjects with active disease requiring medication escalation had reduced engraftment. There was no difference in engraftment based on IBD endoscopic severity at FMT.ConclusionsIBD did not affect CDI recurrence rates 6 months after FMT. Pre-FMT microbiome was not predictive of recurrence, and microbial engraftment was dependent on IBD treatment escalation but not on underlying disease severity.

Grape-derived products contain a wide array of bioactive phenolic compounds which are of significant interest to consumers and researchers for their multiple health benefits. The majority of bioavailable grape polyphenols, including the most abundant flavan-3-ols, i.e. (+)-catechin and (−)-epicatechin, undergo extensive microbial metabolism in the gut, forming metabolites that can be highly bioavailable and bioactive. To gain a better understanding in microbial metabolism of grape polyphenols and to identify bioactive metabolites, advanced analytical methods are needed to accurately quantitate microbial-derived metabolites, particularly at trace levels, in addition to their precursors. This work describes the development and validation of a high-throughput, sensitive and reproducible GC-QqQ/MS method operated under MRM mode that allowed the identification and quantification of 16 phenolic acid metabolites, along with (+)-catechin and (−)-epicatechin, in flavanol-enriched broth samples anaerobically fermented with human intestinal bacteria. Excellent sensitivity was achieved with low limits of detection and low limits of quantification in the range of 0.24–6.18 ng/mL and 0.480–12.37 ng/mL, respectively. With the exception of hippuric acid, recoveries of most analytes were greater than 85%. The percent accuracies for almost all analytes were within ±23% and precision results were all below 18%. Application of the developed method to in vitro samples fermented with different human gut microbiota revealed distinct variations in the extent of flavanol catabolism, as well as production of bioactive phenolic acid metabolites. These results support that intestinal microbiota have a significant impact on the production of flavanol metabolites. The successful application of the established method demonstrates its applicability and robustness for analysis of grape flavanols and their microbial metabolites in biological samples.

Summary Microbiota are thought to influence the development and progression of inflammatory bowel disease (IBD), but determining generalizable effects of microbiota on IBD etiology requires larger-scale functional analyses. We colonized germ-free mice with intestinal microbiotas from 30 healthy and IBD donors and determined the homeostatic intestinal T cell response to each microbiota. Compared to microbiotas from healthy donors, transfer of IBD microbiotas into germ-free mice increased numbers of intestinal Th17 cells and Th2 cells and decreased numbers of RORγt+ Treg cells. Colonization with IBD microbiotas exacerbated disease in a model where colitis is induced upon transfer of naive T cells into Rag1−/− mice. The proportions of Th17 and RORγt+ Treg cells induced by each microbiota were predictive of human disease status and accounted for disease severity in the Rag1−/− colitis model. Thus, an impact on intestinal Th17 and RORγt+ Treg cell compartments emerges as a unifying feature of IBD microbiotas, suggesting a general mechanism for microbial contribution to IBD pathogenesis.

Gene promoters typically contain multiple transcription factor binding sites (TFBSs), which may vary in affinity for their cognate transcription factors (TFs). One major challenge in studying cis-regulation is to understand how TFBS variants affect gene expression. We studied the in vivo effects of TFBS variants on cis-regulation using synthetic promoters coupled with a thermodynamic model of TF binding. We measured expression driven by each promoter with RNA-seq of transcribed sequence barcodes. This allowed reporter genes to be highly multiplexed and increased our statistical power to detect the effects of TFBS variants. We analyzed the effects of TFBS variants using a thermodynamic framework that models both TF-DNA interactions and TF-TF interactions. We found that this system accurately estimates the in vivo relative affinities of TFBSs and predicts unexpected interactions between several TFBSs. Our results reveal that binding site variants can have complex effects on gene expression due to differences in TFBS affinity for cognate TFs and differences in TFBS specificity for noncognate TFs.

Shotgun metagenomics methods enable characterization of microbial communities in human microbiome and environmental samples. Assembly of metagenome sequences does not output whole genomes, so computational binning methods have been developed to cluster sequences into genome ‘bins’. These methods exploit sequence composition, species abundance, or chromosome organization but cannot fully distinguish closely related species and strains. We present a binning method that incorporates bacterial DNA methylation signatures, which are detected using single-molecule real-time sequencing. Our method takes advantage of these endogenous epigenetic barcodes to resolve individual reads and assembled contigs into species- and strain-level bins. We validated our method using synthetic and real microbiome sequences. In addition to genome binning, we show that our method links plasmids and other mobile genetic elements to their host species in a real microbiome sample. Incorporation of DNA methylation information into shotgun metagenomics analyses will complement existing methods to enable more accurate sequence binning.

Summary While conventional pathogenic protists have been extensively studied, there is an underappreciated constitutive protist microbiota that is an integral part of the vertebrate microbiome. The impact of these species on the host and their potential contributions to mucosal immune homeostasis remain poorly studied. Here, we show that the protozoan Tritrichomonas musculis activates the host epithelial inflammasome to induce IL-18 release. Epithelial-derived IL-18 promotes dendritic cell-driven Th1 and Th17 immunity and confers dramatic protection from mucosal bacterial infections. Along with its role as a "protistic" antibiotic, colonization with T. musculis exacerbates the development of T-cell-driven colitis and sporadic colorectal tumors. Our findings demonstrate a novel mutualistic host-protozoan interaction that increases mucosal host defenses at the cost of an increased risk of inflammatory disease.

The expression of most genes is regulated by multiple transcription factors. The interactions between transcription factors produce complex patterns of gene expression that are not always obvious from the arrangement of cis-regulatory elements in a promoter. One critical element of promoters is the TATA box, the docking site for the RNA polymerase holoenzyme. Using a synthetic promoter system coupled to a thermodynamic model of combinatorial regulation, we analyze the effects of different strength TATA boxes on various aspects of combinatorial cis-regulation. The thermodynamic model explains 75% of the variance in gene expression in synthetic promoter libraries with different strength TATA boxes, suggesting that many of the salient aspects of cis-regulation are captured by the model. Our results demonstrate that the effect of changing the TATA box on gene expression is the same for all synthetic promoters regardless of the arrangement of cis-regulatory sites we studied. Our analysis also showed that in our synthetic system the strength of the RNA polymerase–TATA interaction does not alter the combinatorial interactions between transcription factors, or between transcription factors and RNA polymerase. Finally, we show that although stronger TATA boxes increase expression in a predictable fashion, stronger TATA boxes have very little effect on noise in our synthetic promoters, regardless of the arrangement of cis-regulatory sites. Our results support a modular model of promoter function, where cis-regulatory elements can be mixed and matched (programmed) with outcomes on expression that are predictable based on the rules of simple protein–protein and protein–DNA interactions.

Cis -regulatory elements (CREs) control gene expression by recruiting transcription factors (TFs) and other DNA binding proteins. We aim to understand how individual nucleotides contribute to the function of CREs. Here we introduce CRE analysis by sequencing (CRE-seq), a high-throughput method for producing and testing large numbers of reporter genes in mammalian cells. We used CRE-seq to assay >1,000 single and double nucleotide mutations in a 52-bp CRE in the Rhodopsin promoter that drives strong and specific expression in mammalian photoreceptors. We find that this particular CRE is remarkably complex. The majority (86%) of single nucleotide substitutions in this sequence exert significant effects on regulatory activity. Although changes in the affinity of known TF binding sites explain some of these expression changes, we present evidence for complex phenomena, including binding site turnover and TF competition. Analysis of double mutants revealed complex, nucleotide-specific interactions between residues in different TF binding sites. We conclude that some mammalian CREs are finely tuned by evolution and function through complex, nonadditive interactions between bound TFs. CRE-seq will be an important tool to uncover the rules that govern these interactions.

We report here the results of an analysis of the regulatory range of the GacS/GacA two-component system in Pseudomonas aeruginosa. Using microarrays, we identified a large number of genes that are regulated by the system, and detected a near complete overlap of these genes with those regulated by two small RNAs (sRNAs), RsmY and RsmZ, suggesting that the expression of all GacA-regulated genes is RsmY/Z-dependent. Using genome-wide DNA-protein interaction analyses, we identified only two genomic regions that associated specifically with GacA, located upstream of the rsmY and rsmZ genes. These results demonstrate that in P. aeruginosa, the GacS/GacA system transduces the regulatory signals to downstream genes exclusively by directly controlling the expression of only two genes rsmY and rsmZ. These two sRNAs serve as intermediates between the input signals and the output at the level of mRNA stability, although additional regulatory inputs can influence the levels of these two riboregulators. We show that the A+T-rich DNA segment upstream of rsmZ is bound and silenced by MvaT and MvaU, the global gene regulators of the H-NS family. This work highlights the importance of post-transcriptional mechanisms involving sRNAs in controlling gene expression during bacterial adaptation to different environments.

Cell-to-cell variance in protein levels (noise) is a ubiquitous phenomenon that can increase fitness by generating phenotypic differences within clonal populations of cells. An important challenge is to identify the specific molecular events that control noise. This task is complicated by the strong dependence of a protein's cell-to-cell variance on its mean expression level through a power-law like relationship (σ2∝μ1.69). Here, we dissect the nature of this relationship using a stochastic model parameterized with experimentally measured values. This framework naturally recapitulates the power-law like relationship (σ2∝μ1.6) and accurately predicts protein variance across the yeast proteome (r2 = 0.935). Using this model we identified two distinct mechanisms by which protein variance can be increased. Variables that affect promoter activation, such as nucleosome positioning, increase protein variance by changing the exponent of the power-law relationship. In contrast, variables that affect processes downstream of promoter activation, such as mRNA and protein synthesis, increase protein variance in a mean-dependent manner following the power-law. We verified our findings experimentally using an inducible gene expression system in yeast. We conclude that the power-law-like relationship between noise and protein mean is due to the kinetics of promoter activation. Our results provide a framework for understanding how molecular processes shape stochastic variation across the genome.

Machine learning approaches offer the potential to systematically identify transcriptional regulatory interactions from a compendium of microarray expression profiles. However, experimental validation of the performance of these methods at the genome scale has remained elusive. Here we assess the global performance of four existing classes of inference algorithms using 445 Escherichia coli Affymetrix arrays and 3,216 known E. coli regulatory interactions from RegulonDB. We also developed and applied the context likelihood of relatedness (CLR) algorithm, a novel extension of the relevance networks class of algorithms. CLR demonstrates an average precision gain of 36% relative to the next-best performing algorithm. At a 60% true positive rate, CLR identifies 1,079 regulatory interactions, of which 338 were in the previously known network and 741 were novel predictions. We tested the predicted interactions for three transcription factors with chromatin immunoprecipitation, confirming 21 novel interactions and verifying our RegulonDB-based performance estimates. CLR also identified a regulatory link providing central metabolic control of iron transport, which we confirmed with real-time quantitative PCR. The compendium of expression data compiled in this study, coupled with RegulonDB, provides a valuable model system for further improvement of network inference algorithms using experimental data., Author Summary Organisms can adapt to changing environments—becoming more virulent, for example, or activating stress responses—thanks to a flexible gene expression program controlled by the dynamic interactions of hundreds of transcriptional regulators. To unravel this regulatory complexity, multiple computational algorithms have been developed to analyze gene expression profiles and detect dependencies among genes over different conditions. It has been difficult to judge whether these algorithms can generate accurate global maps of regulatory interactions, however, because of the absence of a model organism with both a compendium of gene expression data and a corresponding network of experimentally determined regulatory interactions. To address this issue, we assembled 445 Escherichia coli microarrays, applied four classes of inference algorithms to the dataset, and validated the predictions against 3,216 experimentally determined E. coli interactions. The top-performing algorithm identifies 1,079 regulatory interactions at a confidence level of 60% or higher. Of these predicted interactions, 741 are novel and illuminate the regulation of amino acid biosynthesis, flagella biosynthesis, osmotic stress response, antibiotic resistance, and iron regulation. By defining the capabilities and limitations of network inference algorithms for large-scale mapping of prokaryotic regulatory networks, our work should facilitate their application to the mapping of novel microbes., A novel, machine-learning method is developed to predict transcriptional regulatory interactions, making use of microarray data. One interaction identified appears to be important for the control of iron transport.