Environment-specific combinatorial cis-regulation in synthetic promoters

Generating information-dense promoter sequences with optimal string packing

Dense arrangements of binding sites within nucleotide sequences can collectively influence downstream transcription rates or initiate biomolecular interactions. For example, natural promoter regions can harbor many overlapping transcription factor binding sites that influence the rate of transcription initiation. Despite the prevalence of overlapping binding sites in nature, rapid design of nucleotide sequences with many overlapping sites remains a challenge. Here, we show that this is an NP-hard problem, coined here as the nucleotide String Packing Problem (SPP). We then introduce a computational technique that efficiently assembles sets of DNA-protein binding sites into dense, contiguous stretches of double-stranded DNA. For the efficient design of nucleotide sequences spanning hundreds of base pairs, we reduce the SPP to an Orienteering Problem with integer distances, and then leverage modern integer linear programming solvers. Our method optimally packs sets of 20-100 binding sites into dense nucleotide arrays of 50-300 base pairs in 0.05-10 seconds. Unlike approximation algorithms or meta-heuristics, our approach finds provably optimal solutions. We demonstrate how our method can generate large sets of diverse sequences suitable for library generation, where the frequency of binding site usage across the returned sequences can be controlled by modulating the objective function. As an example, we then show how adding additional constraints, like the inclusion of sequence elements with fixed positions, allows for the design of bacterial promoters. The nucleotide string packing approach we present can accelerate the design of sequences with complex DNA-protein interactions. When used in combination with synthesis and high-throughput screening, this design strategy could help interrogate how complex binding site arrangements impact either gene expression or biomolecular mechanisms in varied cellular contexts.

Generating information-dense promoter sequences with optimal string packing

Dense arrangements of binding sites within nucleotide sequences can collectively influence downstream transcription rates or initiate biomolecular interactions. For example, natural promoter regions can harbor many overlapping transcription factor binding sites that influence the rate of transcription initiation. Despite the prevalence of overlapping binding sites in nature, rapid design of nucleotide sequences with many overlapping sites remains a challenge. Here, we show that this is an NP-hard problem, coined here as the nucleotide String Packing Problem (SPP). We then introduce a computational technique that efficiently assembles sets of DNA-protein binding sites into dense, contiguous stretches of double-stranded DNA. For the efficient design of nucleotide sequences spanning hundreds of base pairs, we reduce the SPP to an Orienteering Problem with integer distances, and then leverage modern integer linear programming solvers. Our method optimally packs libraries of 20-100 binding sites into dense nucleotide arrays of 50-300 base pairs in 0.05-10 seconds. Unlike approximation algorithms or meta-heuristics, our approach finds provably optimal solutions. We demonstrate how our method can generate large sets of diverse sequences suitable for library generation, where the frequency of binding site usage across the returned sequences can be controlled by modulating the objective function. As an example, we then show how adding additional constraints, like the inclusion of sequence elements with fixed positions, allows for the design of bacterial promoters. The nucleotide string packing approach we present can accelerate the design of sequences with complex DNA-protein interactions. When used in combination with synthesis and high-throughput screening, this design strategy could help interrogate how complex binding site arrangements impact either gene expression or biomolecular mechanisms in varied cellular contexts.

Author Summary

The way protein binding sites are arranged on DNA can control the regulation and transcription of downstream genes. Areas with a high concentration of binding sites can enable complex interplay between transcription factors, a feature that is exploited by natural promoters. However, designing synthetic promoters that contain dense arrangements of binding sites is a challenge. The task involves overlapping many binding sites, each typically about 10 nucleotides long, within a constrained sequence area, which becomes increasingly difficult as sequence length decreases, and binding site variety increases. We introduce an approach to design nucleotide sequences with optimally packed protein binding sites, which we call the nucleotide String Packing Problem (SPP). We show that the SPP can be solved efficiently using integer linear programming to identify the densest arrangements of binding sites for a specified sequence length. We show how adding additional constraints, like the inclusion of sequence elements with fixed positions, allows for the design of bacterial promoters. The presented approach enables the rapid design and study of nucleotide sequences with complex, dense binding site architectures.

Thermodynamic Modelling of Transcriptional Control: A Sensitivity Analysis

Modelling is a tool used to decipher the biochemical mechanisms involved in transcriptional control. Experimental evidence in genetics is usually supported by theoretical models in order to evaluate the effects of all the possible interactions that can occur in these complicated processes. Models derived from the thermodynamic method are critical in this labour because they are able to take into account multiple mechanisms operating simultaneously at the molecular micro-scale and relate them to transcriptional initiation at the tissular macro-scale. This work is devoted to adapting computational techniques to this context in order to theoretically evaluate the role played by several biochemical mechanisms. The interest of this theoretical analysis relies on the fact that it can be contrasted against those biological experiments where the response to perturbations in the transcriptional machinery environment is evaluated in terms of genetically activated/repressed regions. The theoretical reproduction of these experiments leads to a sensitivity analysis whose results are expressed in terms of the elasticity of a threshold function determining those activated/repressed regions. The study of this elasticity function in thermodynamic models already proposed in the literature reveals that certain modelling approaches can alter the balance between the biochemical mechanisms considered, and this can cause false/misleading outcomes. The reevaluation of classical thermodynamic models gives us a more accurate and complete picture of the interactions involved in gene regulation and transcriptional control, which enables more specific predictions. This sensitivity approach provides a definite advantage in the interpretation of a wide range of genetic experimental results.

High-Throughput Affinity Measurements of Transcription Factor and DNA Mutations Reveal Affinity and Specificity Determinants

Transcription factors (TFs) bind regulatory DNA to control gene expression, and mutations to either TFs or DNA can alter binding affinities to rewire regulatory networks and drive phenotypic variation. While studies have profiled energetic effects of DNA mutations extensively, we lack similar information for TF variants. Here, we present STAMMP (simultaneous transcription factor affinity measurements via microfluidic protein arrays), a high-throughput microfluidic platform enabling quantitative characterization of hundreds of TF variants simultaneously. Measured affinities for ∼210 mutants of a model yeast TF (Pho4) interacting with 9 oligonucleotides (>1,800 K_ds) reveal that many combinations of mutations to poorly conserved TF residues and nucleotides flanking the core binding site alter but preserve physiological binding, providing a mechanism by which combinations of mutations in cis and trans could modulate TF binding to tune occupancies during evolution. Moreover, biochemical double-mutant cycles across the TF-DNA interface reveal molecular mechanisms driving recognition, linking sequence to function. A record of this paper's Transparent Peer Review process is included in the Supplemental Information.

A Simple Grammar Defines Activating and Repressing cis-Regulatory Elements in Photoreceptors

Transcription factors often activate and repress different target genes in the same cell. How activation and repression are encoded by different arrangements of transcription factor binding sites in cis-regulatory elements is poorly understood. We investigated how sites for the transcription factor CRX encode both activation and repression in photoreceptors by assaying thousands of genomic and synthetic cis-regulatory elements in wild-type and Crx^-/- retinas. We found that sequences with high affinity for CRX repress transcription, whereas sequences with lower affinity activate. This rule is modified by a cooperative interaction between CRX sites and sites for the transcription factor NRL, which overrides the repressive effect of high affinity for CRX. Our results show how simple rearrangements of transcription factor binding sites encode qualitatively different responses to a single transcription factor and explain how CRX plays multiple cis-regulatory roles in the same cell.

In silico design of context-responsive mammalian promoters with user-defined functionality

Comprehensive de novo-design of complex mammalian promoters is restricted by unpredictable combinatorial interactions between constituent transcription factor regulatory elements (TFREs). In this study, we show that modular binding sites that do not function cooperatively can be identified by analyzing host cell transcription factor expression profiles, and subsequently testing cognate TFRE activities in varying homotypic and heterotypic promoter architectures. TFREs that displayed position-insensitive, additive function within a specific expression context could be rationally combined together in silico to create promoters with highly predictable activities. As TFRE order and spacing did not affect the performance of these TFRE-combinations, compositions could be specifically arranged to preclude the formation of undesirable sequence features. This facilitated simple in silico-design of promoters with context-required, user-defined functionalities. To demonstrate this, we de novo-created promoters for biopharmaceutical production in CHO cells that exhibited precisely designed activity dynamics and long-term expression-stability, without causing observable retroactive effects on cellular performance. The design process described can be utilized for applications requiring context-responsive, customizable promoter function, particularly where co-expression of synthetic TFs is not suitable. Although the synthetic promoter structure utilized does not closely resemble native mammalian architectures, our findings also provide additional support for a flexible billboard model of promoter regulation.

Programming mRNA decay to modulate synthetic circuit resource allocation

Synthetic circuits embedded in host cells compete with cellular processes for limited intracellular resources. Here we show how funnelling of cellular resources, after global transcriptome degradation by the sequence-dependent endoribonuclease MazF, to a synthetic circuit can increase production. Target genes are protected from MazF activity by recoding the gene sequence to eliminate recognition sites, while preserving the amino acid sequence. The expression of a protected fluorescent reporter and flux of a high-value metabolite are significantly enhanced using this genome-scale control strategy. Proteomics measurements discover a host factor in need of protection to improve resource redistribution activity. A computational model demonstrates that the MazF mRNA-decay feedback loop enables proportional control of MazF in an optimal operating regime. Transcriptional profiling of MazF-induced cells elucidates the dynamic shifts in transcript abundance and discovers regulatory design elements. Altogether, our results suggest that manipulation of cellular resource allocation is a key control parameter for synthetic circuit design.

Synthetic circuits in host cells compete with endogenous processes for limited resources. Here the authors use MazF to funnel cellular resources to a synthetic circuit to increase product production and demonstrate how resource allocation can be manipulated.

Can We Predict Gene Expression by Understanding Proximal Promoter Architecture?

We review computational predictions of expression from the promoter architecture - the set of transcription factors that can bind the proximal promoter. We focus on spatial expression patterns in animals with complex body plans and many distinct tissue types. This field is ripe for change as functional genomics datasets accumulate for both expression and protein-DNA interactions. While there has been some success in predicting the breadth of expression (i.e., the fraction of tissue types a gene is expressed in), predicting tissue specificity remains challenging. We discuss how progress can be achieved through either machine learning or complementary combinatorial data mining. The likely impact of single-cell expression data is considered. Finally, we discuss the design of artificial promoters as a practical application.

Interactions between pluripotency factors specify cis-regulation in embryonic stem cells

We investigated how interactions between pluripotency transcription factors (TFs) affect cis-regulation. We created hundreds of synthetic cis-regulatory elements (CREs) comprised of combinations of binding sites for pluripotency TFs and measured their expression in mouse embryonic stem (ES) cells. A thermodynamic model that incorporates interactions between TFs explains a large portion (72%) of the variance in expression of these CREs. These interactions include three favorable heterotypic interactions between TFs. The model also predicts an unfavorable homotypic interaction between TFs, helping to explain the observation that homotypic chains of binding sites express at low levels. We further investigated the expression driven by CREs comprised of homotypic chains of KLF4 binding sites. Our results suggest that KLF homologs make unique contributions to regulation by these CREs. We conclude that a specific set of interactions between pluripotency TFs plays a large role in setting the levels of expression driven by CREs in ES cells.

Prediction of fine-tuned promoter activity from DNA sequence

The quantitative prediction of transcriptional activity of genes using promoter sequence is fundamental to the engineering of biological systems for industrial purposes and understanding the natural variation in gene expression. To catalyze the development of new algorithms for this purpose, the Dialogue on Reverse Engineering Assessment and Methods (DREAM) organized a community challenge seeking predictive models of promoter activity given normalized promoter activity data for 90 ribosomal protein promoters driving expression of a fluorescent reporter gene. By developing an unbiased modeling approach that performs an iterative search for predictive DNA sequence features using the frequencies of various k-mers, inferred DNA mechanical properties and spatial positions of promoter sequences, we achieved the best performer status in this challenge. The specific predictive features used in the model included the frequency of the nucleotide G, the length of polymeric tracts of T and TA, the frequencies of 6 distinct trinucleotides and 12 tetranucleotides, and the predicted protein deformability of the DNA sequence. Our method accurately predicted the activity of 20 natural variants of ribosomal protein promoters (Spearman correlation r = 0.73) as compared to 33 laboratory-mutated variants of the promoters (r = 0.57) in a test set that was hidden from participants. Notably, our model differed substantially from the rest in 2 main ways: i) it did not explicitly utilize transcription factor binding information implying that subtle DNA sequence features are highly associated with gene expression, and ii) it was entirely based on features extracted exclusively from the 100 bp region upstream from the translational start site demonstrating that this region encodes much of the overall promoter activity. The findings from this study have important implications for the engineering of predictable gene expression systems and the evolution of gene expression in naturally occurring biological systems.

Combinatorial Cis-regulation in Saccharomyces Species

Transcriptional control of gene expression requires interactions between the cis-regulatory elements (CREs) controlling gene promoters. We developed a sensitive computational method to identify CRE combinations with conserved spacing that does not require genome alignments. When applied to seven sensu stricto and sensu lato Saccharomyces species, 80% of the predicted interactions displayed some evidence of combinatorial transcriptional behavior in several existing datasets including: (1) chromatin immunoprecipitation data for colocalization of transcription factors, (2) gene expression data for coexpression of predicted regulatory targets, and (3) gene ontology databases for common pathway membership of predicted regulatory targets. We tested several predicted CRE interactions with chromatin immunoprecipitation experiments in a wild-type strain and strains in which a predicted cofactor was deleted. Our experiments confirmed that transcription factor (TF) occupancy at the promoters of the CRE combination target genes depends on the predicted cofactor while occupancy of other promoters is independent of the predicted cofactor. Our method has the additional advantage of identifying regulatory differences between species. By analyzing the S. cerevisiae and S. bayanus genomes, we identified differences in combinatorial cis-regulation between the species and showed that the predicted changes in gene regulation explain several of the species-specific differences seen in gene expression datasets. In some instances, the same CRE combinations appear to regulate genes involved in distinct biological processes in the two different species. The results of this research demonstrate that (1) combinatorial cis-regulation can be inferred by multi-genome analysis and (2) combinatorial cis-regulation can explain differences in gene expression between species.

Engineering Synthetic cis-Regulatory Elements for Simultaneous Recognition of Three Transcriptional Factors in Bacteria

Recognition of cis-regulatory elements by transcription factors (TF) at target promoters is crucial to gene regulation in bacteria. In this process, binding of TFs to their cognate sequences depends on a set of physical interactions between these proteins and specific nucleotides in the operator region. Previously, we showed that in silico optimization algorithms are able to generate short sequences that are recognized by two different TFs of Escherichia coli, namely, CRP and IHF, thus generating an AND logic gate. Here, we expanded this approach in order to engineer DNA sequences that can be simultaneously recognized by three unrelated TFs (CRP, IHF, and Fis). Using in silico optimization and experimental validation strategies, we were able to obtain a candidate promoter (Plac-CFI1) regulated by only two TFs with an AND logic, thus demonstrating a limitation in the design. Subsequently, we modified the algorithm to allow the optimization of extended sequences, and were able to design two synthetic promoters (PCFI20-1 and PCFI22-5) that were functional in vivo. Expression assays in E. coli mutant strains for each TF revealed that while CRP positively regulates the promoter activities, IHF and Fis are strong repressors of both the promoter variants. Taken together, our results demonstrate the potential of in silico strategies in bacterial synthetic promoter engineering. Furthermore, the study also shows how small modifications in cis-regulatory elements can drastically affect the final logic of the resulting promoter.

RNA-Binding Efficacy of N-Phenylbenzohydroxamic Acid: An Invitro and Insilico Approach

RNA has attracted recent attention for its key role in gene expression and hence targeting by small molecules for therapeutic intervention. This study is aimed to elucidate the specificity of RNA binding affinity of parent compound of N-arylhydroxamic acids series, N-phenylbenzohydroxamic acid trivially named as PBHA,C6H5NOH.C6H5C˭O. The binding behavior was examined by various biophysical methods such as absorption, fluorescence, and viscosity measurements. Molecular docking was also done. The value of affinity constant and overall binding constant was calculated 5.79±0.03×10(4) M(-1) and K'=1.09±0.03×10(5) M(-1), respectively. The Stern-Volmer constant Ksv obtained was 2.28±0.04×10(4) M(-1). The compound (PBHA) shows a concentration-based enhancement of fluorescence intensity with increasing RNA concentration. Fluorescence quenching of PBHA-RNA complex in presence of K4 [Fe(CN)6] was also observed. Viscometric studies complimented the UV results where a continuous increase in relative viscosity of the RNA solution was observed with added optimal PBHA concentration. All the experimental evidences indicate that PBHA can strongly bind to RNA through an intercalative mode.

Understanding how cis-regulatory function is encoded in DNA sequence using massively parallel reporter assays and designed sequences

Genome-scale methods have identified thousands of candidate cis-regulatory elements (CREs), but methods to directly assay the regulatory function of these elements on a comparably large scale have not been available. The inability to directly test and perturb the regulatory activity of large numbers of DNA sequences has hindered efforts to discover how cis-regulatory function is encoded in genomic sequence. Recently developed massively parallel reporter gene assays combine next generation sequencing with high-throughput oligonucleotide synthesis to offer the capacity to test and mutationally perturb thousands of specifically chosen or designed cis-regulatory sequences in a single experiment. These assays are the basis of recent studies that include large-scale functional validation of genomic CREs, exhaustive mutational analyses of individual regulatory sequences, and tests of large libraries of synthetic CREs. The results demonstrate how massively parallel reporter assays with libraries of designed sequences provide the statistical power required to address previously intractable questions about cis-regulatory function.

Parameter estimation methods for gene circuit modeling from time-series mRNA data: a comparative study

Parameter estimation is a challenging computational problem in the reverse engineering of biological systems. Because advances in biotechnology have facilitated wide availability of time-series gene expression data, systematic parameter estimation of gene circuit models from such time-series mRNA data has become an important method for quantitatively dissecting the regulation of gene expression. By focusing on the modeling of gene circuits, we examine here the performance of three types of state-of-the-art parameter estimation methods: population-based methods, online methods and model-decomposition-based methods. Our results show that certain population-based methods are able to generate high-quality parameter solutions. The performance of these methods, however, is heavily dependent on the size of the parameter search space, and their computational requirements substantially increase as the size of the search space increases. In comparison, online methods and model decomposition-based methods are computationally faster alternatives and are less dependent on the size of the search space. Among other things, our results show that a hybrid approach that augments computationally fast methods with local search as a subsequent refinement procedure can substantially increase the quality of their parameter estimates to the level on par with the best solution obtained from the population-based methods while maintaining high computational speed. These suggest that such hybrid methods can be a promising alternative to the more commonly used population-based methods for parameter estimation of gene circuit models when limited prior knowledge about the underlying regulatory mechanisms makes the size of the parameter search space vastly large.

A multi-scale model of hepcidin promoter regulation reveals factors controlling systemic iron homeostasis

Systemic iron homeostasis involves a negative feedback circuit in which the expression level of the peptide hormone hepcidin depends on and controls the iron blood levels. Hepcidin expression is regulated by the BMP6/SMAD and IL6/STAT signaling cascades. Deregulation of either pathway causes iron-related diseases such as hemochromatosis or anemia of inflammation. We quantitatively analyzed how BMP6 and IL6 control hepcidin expression. Transcription factor (TF) phosphorylation and reporter gene expression were measured under co-stimulation conditions, and the promoter was perturbed by mutagenesis. Using mathematical modeling, we systematically analyzed potential mechanisms of cooperative and competitive promoter regulation by the transcription factors, and experimentally validated the model predictions. Our results reveal that hepcidin cross-regulation primarily occurs by combinatorial transcription factor binding to the promoter, whereas signaling crosstalk is insignificant. We find that the presence of two BMP-responsive elements enhances the steepness of the promoter response towards the iron-sensing BMP signaling axis, which promotes iron homeostasis in vivo. IL6 co-stimulation reduces the promoter sensitivity towards the BMP signal, because the SMAD and STAT transcription factors compete for recruiting RNA polymerase to the transcription start site. This may explain why inflammatory signals disturb iron homeostasis in anemia of inflammation. Taken together, our results reveal why the iron homeostasis circuit is sensitive to perturbations implicated in disease.

The nutritional iron uptake is tightly regulated because the body has limited capacity of iron excretion. Mammals maintain iron homeostasis by a negative feedback loop, in which the peptide hepcidin senses the iron blood level and controls iron resorption. Molecular perturbations in the homeostasis loop lead to iron-related diseases such as hemochromatosis or anemia of inflammation. Quantitative studies are required to understand the dynamics of the iron homeostasis circuitry in health and disease. We investigated how the biological activity of hepcidin is regulated by combining experiments with mathematical modeling. We present a multi-scale model that describes the signaling network and the gene promoter controlling hepcidin expression. Possible scenarios of hepcidin regulation were systematically tested against experimental data, and interpreted using a network model of iron metabolism in vivo. The analysis showed that the presence of multiple redundant regulatory elements in the hepcidin gene promoter facilitates homeostasis, because changes in iron blood levels are sensed with high sensitivity. We further suggest that inflammatory signals establish molecular competition at the hepcidin promoter, thereby reducing its iron sensitivity and leading to a loss of homeostasis in anemia of inflammation. We conclude that quantitative insights into hepcidin expression regulation explain features of systemic iron homeostasis.

Discrimination between thermodynamic models of cis-regulation using transcription factor occupancy data

Many studies have identified binding preferences for transcription factors (TFs), but few have yielded predictive models of how combinations of transcription factor binding sites generate specific levels of gene expression. Synthetic promoters have emerged as powerful tools for generating quantitative data to parameterize models of combinatorial cis-regulation. We sought to improve the accuracy of such models by quantifying the occupancy of TFs on synthetic promoters in vivo and incorporating these data into statistical thermodynamic models of cis-regulation. Using chromatin immunoprecipitation-seq, we measured the occupancy of Gcn4 and Cbf1 in synthetic promoter libraries composed of binding sites for Gcn4, Cbf1, Met31/Met32 and Nrg1. We measured the occupancy of these two TFs and the expression levels of all promoters in two growth conditions. Models parameterized using only expression data predicted expression but failed to identify several interactions between TFs. In contrast, models parameterized with occupancy and expression data predicted expression data, and also revealed Gcn4 self-cooperativity and a negative interaction between Gcn4 and Nrg1. Occupancy data also allowed us to distinguish between competing regulatory mechanisms for the factor Gcn4. Our framework for combining occupancy and expression data produces predictive models that better reflect the mechanisms underlying combinatorial cis-regulation of gene expression.

Distinct properties of cell-type-specific and shared transcription factor binding sites

Most human transcription factors bind a small subset of potential genomic sites and often use different subsets in different cell types. To identify mechanisms that govern cell-type-specific transcription factor binding, we used an integrative approach to study estrogen receptor α (ER). We found that ER exhibits two distinct modes of binding. Shared sites, bound in multiple cell types, are characterized by high-affinity estrogen response elements (EREs), inaccessible chromatin, and a lack of DNA methylation, while cell-specific sites are characterized by a lack of EREs, co-occurrence with other transcription factors, and cell-type-specific chromatin accessibility and DNA methylation. These observations enabled accurate quantitative models of ER binding that suggest tethering of ER to one-third of cell-specific sites. The distinct properties of cell-specific binding were also observed with glucocorticoid receptor and for ER in primary mouse tissues, representing an elegant genomic encoding scheme for generating cell-type-specific gene regulation.

Combinatorial control of gene expression

The complexity and diversity of eukaryotic organisms are a feat of nature's engineering. Pulling the strings of such an intricate machinery requires an even more masterful and crafty approach. Only the number and type of responses that they generate exceed the staggering proportions of environmental signals perceived and processed by eukaryotes. Hence, at first glance, the cell's sparse stockpile of controlling factors does not seem remotely adequate to carry out this response. The question as to how eukaryotes sense and respond to environmental cues has no single answer. It is an amalgamation, an interplay between several processes, pathways, and factors—a combinatorial control. A short description of some of the most important elements that operate this entire conglomerate is given in this paper.

Mapping the fine structure of a eukaryotic promoter input-output function

The precise tuning of gene expression levels is essential for the optimal performance of transcriptional regulatory networks. We created 209 variants of the Saccharomyces cerevisiae PHO5 promoter to quantify how different binding sites for the transcription factor Pho4 affect its output. We found that transcription-factor binding affinities determined in vitro could quantitatively predict the output of a complex yeast promoter. Promoter output was precisely tunable by subtle changes in binding-site affinity of less than 3 kcal mol(-1), which are accessible by modifying 1-2 bases. Our results provide insights into how transcription-factor binding sites regulate gene expression, their possible evolution and how they can be used to precisely tune gene expression. More generally, we show that in vitro binding-energy landscapes of transcription factors can precisely predict the output of a native yeast promoter, indicating that quantitative models of transcriptional regulatory networks are feasible.

Inferring gene expression from ribosomal promoter sequences, a crowdsourcing approach

The Gene Promoter Expression Prediction challenge consisted of predicting gene expression from promoter sequences in a previously unknown experimentally generated data set. The challenge was presented to the community in the framework of the sixth Dialogue for Reverse Engineering Assessments and Methods (DREAM6), a community effort to evaluate the status of systems biology modeling methodologies. Nucleotide-specific promoter activity was obtained by measuring fluorescence from promoter sequences fused upstream of a gene for yellow fluorescence protein and inserted in the same genomic site of yeast Saccharomyces cerevisiae. Twenty-one teams submitted results predicting the expression levels of 53 different promoters from yeast ribosomal protein genes. Analysis of participant predictions shows that accurate values for low-expressed and mutated promoters were difficult to obtain, although in the latter case, only when the mutation induced a large change in promoter activity compared to the wild-type sequence. As in previous DREAM challenges, we found that aggregation of participant predictions provided robust results, but did not fare better than the three best algorithms. Finally, this study not only provides a benchmark for the assessment of methods predicting activity of a specific set of promoters from their sequence, but it also shows that the top performing algorithm, which used machine-learning approaches, can be improved by the addition of biological features such as transcription factor binding sites.

Massively parallel synthetic promoter assays reveal the in vivo effects of binding site variants

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.

Predictive models for the accumulation of a fluorescent marker protein in tobacco leaves according to the promoter/5'UTR combination

The promoter and 5'-untranslated region (5'UTR) play a key role in determining the efficiency of recombinant protein expression in plants. Comparative experiments are used to identify suitable elements but these are usually tested in transgenic plants or in transformed protoplasts/suspension cells, so their relevance in whole-plant transient expression systems is unclear given the greater heterogeneity in expression levels among different leaves. Furthermore, little is known about the impact of promoter/5'UTR interactions on protein accumulation. We therefore established a predictive model using a design of experiments (DoE) approach to compare the strong double-enhanced Cauliflower mosaic virus 35S promoter (CaMV 35SS) and the weaker Agrobacterium tumefaciens Ti-plasmid nos promoter in whole tobacco plants transiently expressing the fluorescent marker protein DsRed. The promoters were combined with one of three 5'UTRs (one of which was tested with and without an additional protein targeting motif) and the accumulation of DsRed was measured following different post-agroinfiltration incubation periods in all leaves and at different leaf positions. The model predictions were quantitative, allowing the rapid identification of promoter/5'UTR combinations stimulating the highest and quickest accumulation of the marker protein in all leaves. The model also suggested that increasing the incubation time from 5 to 8 days would reduce batch-to-batch variability in protein yields. We used the model to identify promoter/5'UTR pairs that resulted in the least spatiotemporal variation in expression levels. These ideal pairs are suitable for the simultaneous, balanced production of several proteins in whole plants by transient expression.

Rapid synthesis of defined eukaryotic promoter libraries

Current gene synthesis methods allow the generation of long segments of dsDNA. We show that these techniques can be used to create synthetic regulatory elements and describe a method for the creation of completely defined, synthetic variants of the PHO5 promoter from the budding yeast Saccharomyces cerevisae. Overall, 128 promoters were assembled by high-temperature ligation, cloned into plasmids by isothermal assembly, maintained in E. coli, and consequently transformed into yeast by homologous recombination. Synthesis errors occurred at frequencies comparable to or lower than those achieved with current gene synthesis methods. The promoter synthesis method reported here is robust, fast, and readily accessible. Synthetically engineered promoter libraries will be useful tools for dissecting the intricacies of promoter input-output functions and may serve as tunable components for synthetic genetic networks.

Versatility of cooperative transcriptional activation: a thermodynamical modeling analysis for greater-than-additive and less-than-additive effects

We derive a statistical model of transcriptional activation using equilibrium thermodynamics of chemical reactions. We examine to what extent this statistical model predicts synergy effects of cooperative activation of gene expression. We determine parameter domains in which greater-than-additive and less-than-additive effects are predicted for cooperative regulation by two activators. We show that the statistical approach can be used to identify different causes of synergistic greater-than-additive effects: nonlinearities of the thermostatistical transcriptional machinery and three-body interactions between RNA polymerase and two activators. In particular, our model-based analysis suggests that at low transcription factor concentrations cooperative activation cannot yield synergistic greater-than-additive effects, i.e., DNA transcription can only exhibit less-than-additive effects. Accordingly, transcriptional activity turns from synergistic greater-than-additive responses at relatively high transcription factor concentrations into less-than-additive responses at relatively low concentrations. In addition, two types of re-entrant phenomena are predicted. First, our analysis predicts that under particular circumstances transcriptional activity will feature a sequence of less-than-additive, greater-than-additive, and eventually less-than-additive effects when for fixed activator concentrations the regulatory impact of activators on the binding of RNA polymerase to the promoter increases from weak, to moderate, to strong. Second, for appropriate promoter conditions when activator concentrations are increased then the aforementioned re-entrant sequence of less-than-additive, greater-than-additive, and less-than-additive effects is predicted as well. Finally, our model-based analysis suggests that even for weak activators that individually induce only negligible increases in promoter activity, promoter activity can exhibit greater-than-additive responses when transcription factors and RNA polymerase interact by means of three-body interactions. Overall, we show that versatility of transcriptional activation is brought about by nonlinearities of transcriptional response functions and interactions between transcription factors, RNA polymerase and DNA.

A model of spatially restricted transcription in opposing gradients of activators and repressors

Morphogens control patterns of transcription in development, often by establishing concentration gradients of a single transcriptional activator. However, many morphogens, including Hedgehog, create opposing activator and repressor gradients (OARGs). In contrast to single activator gradients, it is not well understood how OARGs control transcriptional patterns. We present a general thermodynamic model that explains how spatial patterns of gene expression are established within OARGs. The model predicts that differences in enhancer binding site affinities for morphogen-responsive transcription factors (TFs) produce discrete transcriptional boundaries, but only when either activators or repressors bind cooperatively. This model quantitatively predicts the boundaries of gene expression within OARGs. When trained on experimental data, our model accounts for the counterintuitive observation that increasing the affinity of binding sites in enhancers of Hedgehog target genes produces more restricted transcription within Hedgehog gradients in Drosophila. Because our model is general, it may explain the role of low-affinity binding sites in many contexts, including mammalian Hedgehog gradients.

Mathematical modeling of gene expression: a guide for the perplexed biologist

The detailed analysis of transcriptional networks holds a key for understanding central biological processes, and interest in this field has exploded due to new large-scale data acquisition techniques. Mathematical modeling can provide essential insights, but the diversity of modeling approaches can be a daunting prospect to investigators new to this area. For those interested in beginning a transcriptional mathematical modeling project, we provide here an overview of major types of models and their applications to transcriptional networks. In this discussion of recent literature on thermodynamic, Boolean, and differential equation models, we focus on considerations critical for choosing and validating a modeling approach that will be useful for quantitative understanding of biological systems.

Predicting promoter activities of primary human DNA sequences

We developed a computer program that can predict the intrinsic promoter activities of primary human DNA sequences. We observed promoter activity using a quantitative luciferase assay and generated a prediction model using multiple linear regression. Our program achieved a prediction accuracy correlation coefficient of 0.87 between the predicted and observed promoter activities. We evaluated the prediction accuracy of the program using massive sequencing analysis of transcriptional start sites in vivo. We found that it is still difficult to predict transcript levels in a strictly quantitative manner in vivo; however, it was possible to select active promoters in a given cell from the other silent promoters. Using this program, we analyzed the transcriptional landscape of the entire human genome. We demonstrate that many human genomic regions have potential promoter activity, and the expression of some previously uncharacterized putatively non-protein-coding transcripts can be explained by our prediction model. Furthermore, we found that nucleosomes occasionally formed open chromatin structures with RNA polymerase II recruitment where the program predicted significant promoter activities, although no transcripts were observed.

Quantitative models of the mechanisms that control genome-wide patterns of transcription factor binding during early Drosophila development

Transcription factors that drive complex patterns of gene expression during animal development bind to thousands of genomic regions, with quantitative differences in binding across bound regions mediating their activity. While we now have tools to characterize the DNA affinities of these proteins and to precisely measure their genome-wide distribution in vivo, our understanding of the forces that determine where, when, and to what extent they bind remains primitive. Here we use a thermodynamic model of transcription factor binding to evaluate the contribution of different biophysical forces to the binding of five regulators of early embryonic anterior-posterior patterning in Drosophila melanogaster. Predictions based on DNA sequence and in vitro protein-DNA affinities alone achieve a correlation of ∼0.4 with experimental measurements of in vivo binding. Incorporating cooperativity and competition among the five factors, and accounting for spatial patterning by modeling binding in every nucleus independently, had little effect on prediction accuracy. A major source of error was the prediction of binding events that do not occur in vivo, which we hypothesized reflected reduced accessibility of chromatin. To test this, we incorporated experimental measurements of genome-wide DNA accessibility into our model, effectively restricting predicted binding to regions of open chromatin. This dramatically improved our predictions to a correlation of 0.6-0.9 for various factors across known target genes. Finally, we used our model to quantify the roles of DNA sequence, accessibility, and binding competition and cooperativity. Our results show that, in regions of open chromatin, binding can be predicted almost exclusively by the sequence specificity of individual factors, with a minimal role for protein interactions. We suggest that a combination of experimentally determined chromatin accessibility data and simple computational models of transcription factor binding may be used to predict the binding landscape of any animal transcription factor with significant precision.

Synthetic circuits, devices and modules

The aim of synthetic biology is to design artificial biological systems for novel applications. From an engineering perspective, construction of biological systems of defined functionality in a hierarchical way is fundamental to this emerging field. Here, we highlight some current advances on design of several basic building blocks in synthetic biology including the artificial gene control elements, synthetic circuits and their assemblies into devices and modules. Such engineered basic building blocks largely expand the synthetic toolbox and contribute to our understanding of the underlying design principles of living cells.

Gene-environment interactions at nucleotide resolution

Interactions among genes and the environment are a common source of phenotypic variation. To characterize the interplay between genetics and the environment at single nucleotide resolution, we quantified the genetic and environmental interactions of four quantitative trait nucleotides (QTN) that govern yeast sporulation efficiency. We first constructed a panel of strains that together carry all 32 possible combinations of the 4 QTN genotypes in 2 distinct genetic backgrounds. We then measured the sporulation efficiencies of these 32 strains across 8 controlled environments. This dataset shows that variation in sporulation efficiency is shaped largely by genetic and environmental interactions. We find clear examples of QTN:environment, QTN: background, and environment:background interactions. However, we find no QTN:QTN interactions that occur consistently across the entire dataset. Instead, interactions between QTN only occur under specific combinations of environment and genetic background. Thus, what might appear to be a QTN:QTN interaction in one background and environment becomes a more complex QTN:QTN:environment:background interaction when we consider the entire dataset as a whole. As a result, the phenotypic impact of a set of QTN alleles cannot be predicted from genotype alone. Our results instead demonstrate that the effects of QTN and their interactions are inextricably linked both to genetic background and to environmental variation.

Phenotypic variation among individuals is caused by naturally occurring genetic differences, or alleles. The relationship between an allele and the phenotype is extremely complex; for example, the effect of an allele often depends upon both the environment and the individual's genetic background. To better understand these complex relationships, we examined the effects of four quantitative trait nucleotides (QTN) in three genes that cause variation in sporulation efficiency between vineyard and oak tree strains of yeast. We measured the effects of the QTN while varying both the genetic makeup of the strains and their growth environments. We found that the effects of each of the four QTN alleles depended upon the genotypes at the other QTN, the growth environment, and whether the strain carried the oak or vineyard parent genome. There were no simple rules that describe the effects of the alleles across all environments; instead, detailed models were needed to account for environmental and genetic variation in order to predict the effects of alleles in specific individuals.

Transcription factor binding variation in the evolution of gene regulation

Transcription factor interactions with DNA are one of the primary mechanisms by which expression is modulated, yet their evolution remains poorly understood. Chromatin immunoprecipitation followed by microarray (ChIP-chip) or sequencing (ChIP-Seq) has revolutionized the study of protein-DNA interactions. However, only recently has attention focused on determining to what extent these regulatory interactions vary between species across entire genomes. A series of recent studies have compared in vivo binding data across a range of evolutionary distances. Binding events diverge rapidly, indicating gene regulation is an evolutionarily flexible process.

TATA is a modular component of synthetic promoters

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.

Perspectives on synthetic promoters for biocatalysis and biotransformation

Acting on the transcriptional level, synthetic promoters have been useful tools for controlling gene expression and have applications in many fields. Here, we discuss synthetic promoters and libraries in regard to current and future applications in the field of biocatalysis or biotransformation. We also focus on synthetic promoter design principles and distinguish between prokaryotic and eukaryotic destinations. The natural toolboxes available for tuneable gene expression and the regulation of enzyme function are limited and primarily host specific. Synthetic biology offers generally applicable concepts and quick implementation. Smart alternatives to transcriptional regulation enrich the engineer's tool box for optimizing industrial enzyme production and host-cell physiology for whole-cell processes. Industrially applicable, tuneable enzyme cascades and artificial circuits for iterative up- and down-regulation will soon be achieved.

A computational evaluation of over-representation of regulatory motifs in the promoter regions of differentially expressed genes

Background

Observed co-expression of a group of genes is frequently attributed to co-regulation by shared transcription factors. This assumption has led to the hypothesis that promoters of co-expressed genes should share common regulatory motifs, which forms the basis for numerous computational tools that search for these motifs. While frequently explored for yeast, the validity of the underlying hypothesis has not been assessed systematically in mammals. This demonstrates the need for a systematic and quantitative evaluation to what degree co-expressed genes share over-represented motifs for mammals.

Results

We identified 33 experiments for human and mouse in the ArrayExpress Database where transcription factors were manipulated and which exhibited a significant number of differentially expressed genes. We checked for over-representation of transcription factor binding sites in up- or down-regulated genes using the over-representation analysis tool oPOSSUM. In 25 out of 33 experiments, this procedure identified the binding matrices of the affected transcription factors. We also carried out de novo prediction of regulatory motifs shared by differentially expressed genes. Again, the detected motifs shared significant similarity with the matrices of the affected transcription factors.

Conclusions

Our results support the claim that functional regulatory motifs are over-represented in sets of differentially expressed genes and that they can be detected with computational methods.

Mechanisms of transcription factor selectivity

The initiation of transcription is regulated by transcription factors (TFs) binding to DNA response elements (REs). How do TFs recognize specific binding sites among the many similar ones available in the genome? Recent research has illustrated that even a single nucleotide substitution can alter the selective binding of TFs to coregulators, that prior binding events can lead to selective DNA binding, and that selectivity is influenced by the availability of binding sites in the genome. Here, we combine structural insights with recent genomics screens to address the problem of TF-DNA interaction specificity. The emerging picture of selective binding site sequence recognition and TF activation involves three major factors: the cellular network, protein and DNA as dynamic conformational ensembles and the tight packing of multiple TFs and coregulators on stretches of regulatory DNA. The classification of TF recognition mechanisms based on these factors impacts our understanding of how transcription initiation is regulated.

In silico analysis of transcription factor repertoire and prediction of stress responsive transcription factors in soybean

Sequence-specific DNA-binding transcription factors (TFs) are often termed as 'master regulators' which bind to DNA and either activate or repress gene transcription. We have computationally analysed the soybean genome sequence data and constructed a proper set of TFs based on the Hidden Markov Model profiles of DNA-binding domain families. Within the soybean genome, we identified 4342 loci encoding 5035 TF models which grouped into 61 families. We constructed a database named SoybeanTFDB (http://soybeantfdb.psc.riken.jp) containing the full compilation of soybean TFs and significant information such as: functional motifs, full-length cDNAs, domain alignments, promoter regions, genomic organization and putative regulatory functions based on annotations of gene ontology (GO) inferred by comparative analysis with Arabidopsis. With particular interest in abiotic stress signalling, we analysed the promoter regions for all of the TF encoding genes as a means to identify abiotic stress responsive cis-elements as well as all types of cis-motifs provided by the PLACE database. SoybeanTFDB enables scientists to easily access cis-element and GO annotations to aid in the prediction of TF function and selection of TFs with functions of interest. This study provides a basic framework and an important user-friendly public information resource which enables analyses of transcriptional regulation in soybean.

Epistasis in a quantitative trait captured by a molecular model of transcription factor interactions

With technological advances in genetic mapping studies more of the genes and polymorphisms that underlie Quantitative Trait Loci (QTL) are now being identified. As the identities of these genes become known there is a growing need for an analysis framework that incorporates the molecular interactions affected by natural polymorphisms. As a step towards such a framework we present a molecular model of genetic variation in sporulation efficiency between natural isolates of the yeast, Saccharomyces cerevisiae. The model is based on the structure of the regulatory pathway that controls sporulation. The model captures the phenotypic variation between strains carrying different combinations of alleles at known QTL. Compared to a standard linear model the molecular model requires fewer free parameters, and has the advantage of generating quantitative hypotheses about the affinity of specific molecular interactions in different genetic backgrounds. Our analyses provide a concrete example of how the thermodynamic properties of protein-protein and protein-DNA interactions naturally give rise to epistasis, the non-linear relationship between genotype and phenotype. As more causative genes and polymorphisms underlying QTL are identified, thermodynamic analyses of quantitative traits may provide a useful framework for unraveling the complex relationship between genotype and phenotype.

Mechanisms and evolution of control logic in prokaryotic transcriptional regulation

A major part of organismal complexity and versatility of prokaryotes resides in their ability to fine-tune gene expression to adequately respond to internal and external stimuli. Evolution has been very innovative in creating intricate mechanisms by which different regulatory signals operate and interact at promoters to drive gene expression. The regulation of target gene expression by transcription factors (TFs) is governed by control logic brought about by the interaction of regulators with TF binding sites (TFBSs) in cis-regulatory regions. A factor that in large part determines the strength of the response of a target to a given TF is motif stringency, the extent to which the TFBS fits the optimal TFBS sequence for a given TF. Advances in high-throughput technologies and computational genomics allow reconstruction of transcriptional regulatory networks in silico. To optimize the prediction of transcriptional regulatory networks, i.e., to separate direct regulation from indirect regulation, a thorough understanding of the control logic underlying the regulation of gene expression is required. This review summarizes the state of the art of the elements that determine the functionality of TFBSs by focusing on the molecular biological mechanisms and evolutionary origins of cis-regulatory regions.

Transcriptional regulatory circuits: predicting numbers from alphabets

Transcriptional regulatory circuits govern how cis and trans factors transform signals into messenger RNA (mRNA) expression levels. With advances in quantitative and high-throughput technologies that allow measurement of gene expression state in different conditions, data that can be used to build and test models of transcriptional regulation is being generated at a rapid pace. Here, we review experimental and computational methods used to derive detailed quantitative circuit models on a small scale and cruder, genome-wide models on a large scale. We discuss the potential of combining small- and large-scale approaches to understand the working and wiring of transcriptional regulatory circuits.