Systematic Dissection of Sequence Elements Controlling σ70 Promoters Using a Genomically Encoded Multiplexed Reporter Assay in Escherichia coli

Determination of the chromosomal position effects for plug-and-play application in the Myxococcus xanthus chassis cells

The chromosomal position effect can significantly affect the transgene expression, which may provide an efficient strategy for the inauguration of alien genes in new hosts, but has been less explored rationally. The bacterium Myxococcus xanthus harbors a large circular high-GC genome, and the position effect in this chassis may result in a thousand-fold expression variation of alien natural products. In this study, we conducted transposon insertion at TA sites on the M. xanthus genome, and used enrichment and dilution indexes to respectively appraise high and low expression potentials of alien genes at insertion sites. The enrichment sites are characteristically distributed along the genome, and the dilution sites are overlapped well with the horizontal transfer genes. We experimentally demonstrated the enrichment sites as high expression integration sites (HEISs), and the dilution sites unsuitable for gene integration expression. This work highlights that HEISs are the plug-and-play sites for efficient expression of integrated genes.

Transcription Attenuation in Synthetic Promoters in Nonoverlapping Tandem Formation

Closely spaced promoters are ubiquitous in prokaryotic and eukaryotic genomes. How their structure and dynamics relate remains unclear, particularly for tandem formations. To study their transcriptional interference, we engineered two pairs and one trio of synthetic promoters in nonoverlapping, tandem formation, in single-copy plasmids transformed into Escherichia coli cells. From in vivo measurements, we found that these promoters in tandem formation can have attenuated transcription rates. The attenuation strength can be widely fine-tuned by the promoters' positioning, natural regulatory mechanisms, and other factors, including the antibiotic rifampicin, which is known to hamper RNAP promoter escape. From this, and supported by in silico models, we concluded that the attenuation in these constructs emerges from premature terminations generated by collisions between RNAPs elongating from upstream promoters and RNAPs occupying downstream promoters. Moreover, we found that these collisions can cause one or both RNAPs to falloff. Finally, the broad spectrum of possible, externally regulated, attenuation strengths observed in our synthetic tandem promoters suggests that they could become useful as externally controllable regulators of future synthetic circuits.

Deciphering regulatory architectures from synthetic single-cell expression patterns

For the vast majority of genes in sequenced genomes, there is limited understanding of how they are regulated. Without such knowledge, it is not possible to perform a quantitative theory-experiment dialogue on how such genes give rise to physiological and evolutionary adaptation. One category of high-throughput experiments used to understand the sequence-phenotype relationship of the transcriptome is massively parallel reporter assays (MPRAs). However, to improve the versatility and scalability of MPRA pipelines, we need a "theory of the experiment" to help us better understand the impact of various biological and experimental parameters on the interpretation of experimental data. These parameters include binding site copy number, where a large number of specific binding sites may titrate away transcription factors, as well as the presence of overlapping binding sites, which may affect analysis of the degree of mutual dependence between mutations in the regulatory region and expression levels. To that end, in this paper we create tens of thousands of synthetic single-cell gene expression outputs using both equilibrium and out-of-equilibrium models. These models make it possible to imitate the summary statistics (information footprints and expression shift matrices) used to characterize the output of MPRAs and from this summary statistic to infer the underlying regulatory architecture. Specifically, we use a more refined implementation of the so-called thermodynamic models in which the binding energies of each sequence variant are derived from energy matrices. Our simulations reveal important effects of the parameters on MPRA data and we demonstrate our ability to optimize MPRA experimental designs with the goal of generating thermodynamic models of the transcriptome with base-pair specificity. Further, this approach makes it possible to carefully examine the mapping between mutations in binding sites and their corresponding expression profiles, a tool useful not only for better designing MPRAs, but also for exploring regulatory evolution.

Deciphering regulatory architectures from synthetic single-cell expression patterns

For the vast majority of genes in sequenced genomes, there is limited understanding of how they are regulated. Without such knowledge, it is not possible to perform a quantitative theory-experiment dialogue on how such genes give rise to physiological and evolutionary adaptation. One category of high-throughput experiments used to understand the sequence-phenotype relationship of the transcriptome is massively parallel reporter assays (MPRAs). However, to improve the versatility and scalability of MPRA pipelines, we need a "theory of the experiment" to help us better understand the impact of various biological and experimental parameters on the interpretation of experimental data. These parameters include binding site copy number, where a large number of specific binding sites may titrate away transcription factors, as well as the presence of overlapping binding sites, which may affect analysis of the degree of mutual dependence between mutations in the regulatory region and expression levels. To that end, in this paper we create tens of thousands of synthetic single-cell gene expression outputs using both equilibrium and out-of-equilibrium models. These models make it possible to imitate the summary statistics (information footprints and expression shift matrices) used to characterize the output of MPRAs and from this summary statistic to infer the underlying regulatory architecture. Specifically, we use a more refined implementation of the so-called thermodynamic models in which the binding energies of each sequence variant are derived from energy matrices. Our simulations reveal important effects of the parameters on MPRA data and we demonstrate our ability to optimize MPRA experimental designs with the goal of generating thermodynamic models of the transcriptome with base-pair specificity. Further, this approach makes it possible to carefully examine the mapping between mutations in binding sites and their corresponding expression profiles, a tool useful not only for better designing MPRAs, but also for exploring regulatory evolution.

ORBIT for E. coli: kilobase-scale oligonucleotide recombineering at high throughput and high efficiency

Microbiology and synthetic biology depend on reverse genetic approaches to manipulate bacterial genomes; however, existing methods require molecular biology to generate genomic homology, suffer from low efficiency, and are not easily scaled to high throughput. To overcome these limitations, we developed a system for creating kilobase-scale genomic modifications that uses DNA oligonucleotides to direct the integration of a non-replicating plasmid. This method, Oligonucleotide Recombineering followed by Bxb-1 Integrase Targeting (ORBIT) was pioneered in Mycobacteria, and here we adapt and expand it for Escherichia coli. Our redesigned plasmid toolkit for oligonucleotide recombineering achieved significantly higher efficiency than λ Red double-stranded DNA recombineering and enabled precise, stable knockouts (≤134 kb) and integrations (≤11 kb) of various sizes. Additionally, we constructed multi-mutants in a single transformation, using orthogonal attachment sites. At high throughput, we used pools of targeting oligonucleotides to knock out nearly all known transcription factor and small RNA genes, yielding accurate, genome-wide, single mutant libraries. By counting genomic barcodes, we also show ORBIT libraries can scale to thousands of unique members (>30k). This work demonstrates that ORBIT for E. coli is a flexible reverse genetic system that facilitates rapid construction of complex strains and readily scales to create sophisticated mutant libraries.

Optimizing Nucleic Acid Delivery Systems through Barcode Technology

Conventional biological experiments often focus on in vitro assays because of the inherent limitations when handling multiple variables in vivo, including labor-intensive and time-consuming procedures. Often only a subset of samples demonstrating significant efficacy in the in vitro assays can be evaluated in vivo. Nonetheless, because of the low correlation between the in vitro and in vivo tests, evaluation of the variables under examination in vivo and not solely in vitro is critical. An emerging approach to achieve high-throughput in vivo tests involves using a barcode system consisting of various nucleotide combinations. Unique barcodes for each variant enable the simultaneous testing of multiple entities, eliminating the need for separate individual tests. Subsequently, to identify crucial parameters, samples were collected and analyzed using barcode sequencing. This review explores the development of barcode design and its applications, including the evaluation of nucleic acid delivery systems and the optimization of gene expression in vivo.

Towards a rational approach to promoter engineering: understanding the complexity of transcription initiation in prokaryotes

Promoter sequences are important genetic control elements. Through their interaction with RNA polymerase they determine transcription strength and specificity, thereby regulating the first step in gene expression. Consequently, they can be targeted as elements to control predictability and tuneability of a genetic circuit, which is essential in applications such as the development of robust microbial cell factories. This review considers the promoter elements implicated in the three stages of transcription initiation, detailing the complex interplay of sequence-specific interactions that are involved, and highlighting that DNA sequence features beyond the core promoter elements work in a combinatorial manner to determine transcriptional strength. In particular, we emphasize that, aside from promoter recognition, transcription initiation is also defined by the kinetics of open complex formation and promoter escape, which are also known to be highly sequence specific. Significantly, we focus on how insights into these interactions can be manipulated to lay the foundation for a more rational approach to promoter engineering.

Synthetic Homoserine Lactone Sensors for Gram-Positive Bacillus subtilis Using LuxR-Type Regulators

A universal biochemical signal for bacterial cell-cell communication could facilitate programming dynamic responses in diverse bacterial consortia. However, the classical quorum sensing paradigm is that Gram-negative and Gram-positive bacteria generally communicate via homoserine lactones (HSLs) or oligopeptide molecular signals, respectively, to elicit population responses. Here, we create synthetic HSL sensors for Gram-positive Bacillus subtilis 168 using allosteric LuxR-type regulators (RpaR, LuxR, RhlR, and CinR) and synthetic promoters. Promoters were combinatorially designed from different sequence elements (-35, -16, -10, and transcriptional start regions). We quantified the effects of these combinatorial promoters on sensor activity and determined how regulator expression affects its activation, achieving up to 293-fold activation. Using the statistical design of experiments, we identified significant effects of promoter regions and pairwise interactions on sensor activity, which helped to understand the sequence-function relationships for synthetic promoter design. We present the first known set of functional HSL sensors (≥20-fold dynamic range) in B. subtilis for four different HSL chemical signals: p-coumaroyl-HSL, 3-oxohexanoyl-HSL, n-butyryl-HSL, and n-(3-hydroxytetradecanoyl)-HSL. This set of synthetic HSL sensors for a Gram-positive bacterium can pave the way for designable interspecies communication within microbial consortia.

Alteration of the -35 and -10 sequences and deletion the upstream sequence of the -35 region of the promoter A1 of the phage T7 in dsDNA confirm the contribution of non-specific interactions with E. coli RNA polymerase to the transcription initiation process

Transcription initiation is a multi-step process, in which the RNA polymerase holoenzyme binds to the specific promoter sequences to form a closed complex, which, through intermediate stages, isomerizes into an open complex capable of initiating the productive phase of transcription. The aim of this work was to determine the contribution of the -10 and -35 regions of the promoter, as well as the role of non-specific interactions, in the binding of RNA polymerase and the formation of an active initiation complex capable of transcription. Therefore, fragments of promoter DNA, derived from the strong promoter A1 of the phage T7, containing completely and partially altered elements -35 and -10, and devoid of an upstream region, were constructed using genetic engineering methods. Functional analyses of modified promoter fragments were carried out, checking their ability to form binary complexes with Escherichia coli RNA polymerase (RNAP) and the efficiency of converting binary complexes into triple complexes characteristic of the productive phase of transcription. The obtained results suggest that, in relation to the A1 promoter of the T7 phage, the most important role of the -35 region is carrying the open complex through the next phases of transcription initiation. The weakening of specific impacts within the region -35 is a reason for the defect associated with the transformation of the open complex, formed by a DNA fragment containing the completely altered -35 region, into elongation and the impairment of RNA synthesis. This leads to breaking contacts with the RNA polymerase holoenzyme, and destabilization and disintegration of the complex in the initial phase of productive transcription. This confirms the hypothesis of the so-called stressed intermediate state associated with the stage of transition from the open complex to the elongation complex. The experiments carried out in this work confirm also that the process of promoter localization and recognition, as well as the formation of binary complexes, is sequential in nature, and that the region located upstream of the -35 hexamer, and the hexamer itself, plays here an additive role.

Deep flanking sequence engineering for efficient promoter design using DeepSEED

Designing promoters with desirable properties is essential in synthetic biology. Human experts are skilled at identifying strong explicit patterns in small samples, while deep learning models excel at detecting implicit weak patterns in large datasets. Biologists have described the sequence patterns of promoters via transcription factor binding sites (TFBSs). However, the flanking sequences of cis-regulatory elements, have long been overlooked and often arbitrarily decided in promoter design. To address this limitation, we introduce DeepSEED, an AI-aided framework that efficiently designs synthetic promoters by combining expert knowledge with deep learning techniques. DeepSEED has demonstrated success in improving the properties of Escherichia coli constitutive, IPTG-inducible, and mammalian cell doxycycline (Dox)-inducible promoters. Furthermore, our results show that DeepSEED captures the implicit features in flanking sequences, such as k-mer frequencies and DNA shape features, which are crucial for determining promoter properties.

Genetic context effects can override canonical cis regulatory elements in Escherichia coli

Recent experiments have shown that in addition to control by cis regulatory elements, the local chromosomal context of a gene also has a profound impact on its transcription. Although this chromosome-position dependent expression variation has been empirically mapped at high-resolution, the underlying causes of the variation have not been elucidated. Here, we demonstrate that 1 kb of flanking, non-coding synthetic sequences with a low frequency of guanosine and cytosine (GC) can dramatically reduce reporter expression compared to neutral and high GC-content flanks in Escherichia coli. Natural and artificial genetic context can have a similarly strong effect on reporter expression, regardless of cell growth phase or medium. Despite the strong reduction in the maximal expression level from the fully-induced reporter, low GC synthetic flanks do not affect the time required to reach the maximal expression level after induction. Overall, we demonstrate key determinants of transcriptional propensity that appear to act as tunable modulators of transcription, independent of regulatory sequences such as the promoter. These findings provide insight into the regulation of naturally occurring genes and an independent control for optimizing expression of synthetic biology constructs.

Automated model-predictive design of synthetic promoters to control transcriptional profiles in bacteria

Transcription rates are regulated by the interactions between RNA polymerase, sigma factor, and promoter DNA sequences in bacteria. However, it remains unclear how non-canonical sequence motifs collectively control transcription rates. Here, we combine massively parallel assays, biophysics, and machine learning to develop a 346-parameter model that predicts site-specific transcription initiation rates for any σ70 promoter sequence, validated across 22132 bacterial promoters with diverse sequences. We apply the model to predict genetic context effects, design σ70 promoters with desired transcription rates, and identify undesired promoters inside engineered genetic systems. The model provides a biophysical basis for understanding gene regulation in natural genetic systems and precise transcriptional control for engineering synthetic genetic systems.

Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism

Simple Summary

The influence of data incompleteness on the correctness of conclusions about the structure and functions of the objects under study is widely discussed in the literature. It was noted that even a small percentage of missing data can lead to incorrect conclusions and imperfect knowledge. In particular, incompleteness can lead to critical errors in the qualitative and quantitative assessments of interactions in biological systems and a distorted understanding of the functioning mechanisms of living systems. In this brief review, we attempt to demonstrate the extent of this incompleteness in functional information about living systems using the best-studied examples. We suggest that this incompleteness may form seemingly insurmountable barriers in deciphering the mechanisms of the functioning of complex systems with unpredictable properties arising from the interaction of the system components.

Abstract

In this brief review, we attempt to demonstrate that the incompleteness of data, as well as the intrinsic heterogeneity of biological systems, may form very strong and possibly insurmountable barriers for researchers trying to decipher the mechanisms of the functioning of live systems. We illustrate this challenge using the two most studied organisms: E. coli, with 34.6% genes lacking experimental evidence of function, and C. elegans, with identified proteins for approximately 50% of its genes. Another striking example is an artificial unicellular entity named JCVI-syn3.0, with a minimal set of genes. A total of 31.5% of the genes of JCVI-syn3.0 cannot be ascribed a specific biological function. The human interactome mapping project identified only 5–10% of all protein interactions in humans. In addition, most of the available data are static snapshots, and it is barely possible to generate realistic models of the dynamic processes within cells. Moreover, the existing interactomes reflect the de facto interaction but not its functional result, which is an unpredictable emerging property. Perhaps the completeness of molecular data on any living organism is beyond our reach and represents an unsolvable problem in biology.

Synthetic biology and bioelectrochemical tools for electrogenetic system engineering

Synthetic biology research and its industrial applications rely on deterministic spatiotemporal control of gene expression. Recently, electrochemical control of gene expression has been demonstrated in electrogenetic systems (redox-responsive promoters used alongside redox inducers and electrodes), allowing for the direct integration of electronics with biological processes. However, the use of electrogenetic systems is limited by poor activity, tunability, and standardization. In this work, we developed a strong, unidirectional, redox-responsive promoter before deriving a mutant promoter library with a spectrum of strengths. We constructed genetic circuits with these parts and demonstrated their activation by multiple classes of redox molecules. Last, we demonstrated electrochemical activation of gene expression under aerobic conditions using a novel, modular bioelectrochemical device. These genetic and electrochemical tools facilitate the design and improve the performance of electrogenetic systems. Furthermore, the genetic design strategies used can be applied to other redox-responsive promoters to further expand the available tools for electrogenetics.

Sequence-dependent model of genes with dual σ factor preference

Escherichia coli uses σ factors to quickly control large gene cohorts during stress conditions. While most of its genes respond to a single σ factor, approximately 5% of them have dual σ factor preference. The most common are those responsive to both σ⁷⁰, which controls housekeeping genes, and σ³⁸, which activates genes during stationary growth and stresses. Using RNA-seq and flow-cytometry measurements, we show that 'σ⁷⁰⁺³⁸ genes' are nearly as upregulated in stationary growth as 'σ³⁸ genes'. Moreover, we find a clear quantitative relationship between their promoter sequence and their response strength to changes in σ³⁸ levels. We then propose and validate a sequence dependent model of σ⁷⁰⁺³⁸ genes, with dual sensitivity to σ³⁸ and σ⁷⁰, that is applicable in the exponential and stationary growth phases, as well in the transient period in between. We further propose a general model, applicable to other stresses and σ factor combinations. Given this, promoters controlling σ⁷⁰⁺³⁸ genes (and variants) could become important building blocks of synthetic circuits with predictable, sequence-dependent sensitivity to transitions between the exponential and stationary growth phases.

Predicting bacterial promoter function and evolution from random sequences

Predicting function from sequence is a central problem of biology. Currently, this is possible only locally in a narrow mutational neighborhood around a wildtype sequence rather than globally from any sequence. Using random mutant libraries, we developed a biophysical model that accounts for multiple features of σ⁷⁰ binding bacterial promoters to predict constitutive gene expression levels from any sequence. We experimentally and theoretically estimated that 10–20% of random sequences lead to expression and ~80% of non-expressing sequences are one mutation away from a functional promoter. The potential for generating expression from random sequences is so pervasive that selection acts against σ⁷⁰-RNA polymerase binding sites even within inter-genic, promoter-containing regions. This pervasiveness of σ⁷⁰-binding sites implies that emergence of promoters is not the limiting step in gene regulatory evolution. Ultimately, the inclusion of novel features of promoter function into a mechanistic model enabled not only more accurate predictions of gene expression levels, but also identified that promoters evolve more rapidly than previously thought.

Escherichia coli Promoters with Consistent Expression throughout the Murine Gut

Advanced microbial therapeutics have great potential as a novel modality to diagnose and treat a wide range of diseases. Yet, to realize this potential, robust parts for regulating gene expression and consequent therapeutic activity in situ are needed. In this study, we characterized the expression level of more than 8000 variants of the Escherichia coli sigma factor 70 (σ70) promoter in a range of different environmental conditions and growth states using fluorescence-activated cell sorting and deep sequencing. Sampled conditions include aerobic and anaerobic culture in the laboratory as well as growth in several locations of the murine gastrointestinal tract. We found that σ70 promoters in E. coli generally maintain consistent expression levels across the murine gut (R²: 0.55-0.85, p value < 1 × 10^-5), suggesting a limited environmental influence but a higher variability between in vitro and in vivo expression levels, highlighting the challenges of translating in vitro promoter activity to in vivo applications. Based on these data, we design the Schantzetta library, composed of eight promoters spanning a wide expression range and displaying a high degree of robustness in both laboratory and in vivo conditions (R² = 0.98, p = 0.000827). This study provides a systematic assessment of the σ70 promoter activity in E. coli as it transits the murine gut leading to the definition of robust expression cassettes that could be a valuable tool for reliable engineering and development of advanced microbial therapeutics.

Reconstructing the transcription regulatory network to optimize resource allocation for robust biosynthesis

An ideal microbial cell factory (MCF) should deliver maximal resources to production, which conflicts with the microbe's native growth-oriented resource allocation strategy and can therefore lead to early termination of the high-yield period. Reallocating resources from growth to production has become a critical factor in constructing robust MCFs. Instead of strengthening specific biosynthetic pathways, emerging endeavors are focused on rearranging the gene regulatory network to fundamentally reprogram the resource allocation pattern. Combining this idea with transcriptional regulation within the hierarchical regulatory network, this review discusses recent engineering strategies targeting the transcription machinery, module networks, regulatory edges, and bottom network layer. This global view will help to construct a production-oriented phenotype that fully harnesses the potential of MCFs.

Importance of the 5' regulatory region to bacterial synthetic biology applications

The field of synthetic biology is evolving at a fast pace. It is advancing beyond single-gene alterations in single hosts to the logical design of complex circuits and the development of integrated synthetic genomes. Recent breakthroughs in deep learning, which is increasingly used in de novo assembly of DNA components with predictable effects, are also aiding the discipline. Despite advances in computing, the field is still reliant on the availability of pre-characterized DNA parts, whether natural or synthetic, to regulate gene expression in bacteria and make valuable compounds. In this review, we discuss the different bacterial synthetic biology methodologies employed in the creation of 5' regulatory regions - promoters, untranslated regions and 5'-end of coding sequences. We summarize methodologies and discuss their significance for each of the functional DNA components, and highlight the key advances made in bacterial engineering by concentrating on their flaws and strengths. We end the review by outlining the issues that the discipline may face in the near future.

The spatial position effect: synthetic biology enters the era of 3D genomics

Microbial cell factories are critical to achieving green biomanufacturing. A position effect occurs when a synthetic gene circuit is expressed from different positions in the chassis strain genome. Here, we propose the concept of the 'spatial position effect,' which uses technologies in 3D genomics to reveal the spatial structure characteristics of the 3D genome of the chassis. On this basis, we propose to rationally design the integration sites of synthetic gene circuits, use reporter genes for preliminary screening, and integrate synthetic gene circuits into promising sites for further experiments. This approach can produce stable and efficient chassis strains for green biomanufacturing. The proposed spatial position effect brings synthetic biology into the era of 3D genomics.

Improved Dynamic Range of a Rhamnose-Inducible Promoter for Gene Expression in Burkholderia spp

A diverse genetic toolkit is critical for understanding bacterial physiology and genotype-phenotype relationships. Inducible promoter systems are an integral part of this toolkit. In Burkholderia and related species, the l-rhamnose-inducible promoter is among the first choices due to its tight control and the lack of viable alternatives. To improve upon its maximum activity and dynamic range, we explored the effect of promoter system modifications in Burkholderia cenocepacia with a LacZ-based reporter. By combining the bacteriophage T7 gene 10 stem-loop and engineered rhaI transcription factor-binding sites, we obtained a rhamnose-inducible system with a 6.5-fold and 3.0-fold increases in maximum activity and dynamic range, respectively, compared to the native promoter. We then added the modified promoter system to pSCrhaB2 and pSC201, common genetic tools used for plasmid-based and chromosome-based gene expression, respectively, in Burkholderia, creating pSCrhaB2plus and pSC201plus. We demonstrated the utility of pSCrhaB2plus for gene expression in B. thailandensis, B. multivorans, and B. vietnamiensis and used pSC201plus to control highly expressed essential genes from the chromosome of B. cenocepacia. The utility of the modified system was demonstrated as we recovered viable mutants to control ftsZ, rpoBC, and rpsF, whereas the unmodified promoter was unable to control rpsF. The modified expression system allowed control of an essential gene depletion phenotype at lower levels of l-rhamnose, the inducer. pSCRhaB2plus and pSC201plus are expected to be valuable additions to the genetic toolkit for Burkholderia and related species. IMPORTANCE Species of Burkholderia are dually recognized as being of attractive biotechnological potential but also opportunistic pathogens for immunocompromised individuals. Understanding the genotype-phenotype relationship is critical for synthetic biology approaches in Burkholderia to disentangle pathogenic from beneficial traits. A diverse genetic toolkit, including inducible promoters, is the foundation for these investigations. Thus, we sought to improve on the commonly used rhamnose-inducible promoter system. Our modifications resulted in both higher levels of heterologous protein expression and broader control over highly expressed essential genes in B. cenocepacia. The significance of our work is in expanding the genetic toolkit to enable more comprehensive studies into Burkholderia and related bacteria.

Systematic dissection of σ70 sequence diversity and function in bacteria

Primary σ⁷⁰ factors are key conserved bacterial regulatory proteins that interact with regulatory DNA to control gene expression. It is, however, poorly understood whether σ⁷⁰ sequence diversity in different bacteria reflects functional differences. Here, we employ comparative and functional genomics to explore the sequence and function relationship of primary σ⁷⁰. Using multiplex automated genome engineering and deep sequencing (MAGE-seq), we generate a saturation mutagenesis library and high-resolution fitness map of E. coli σ⁷⁰ in domains 2-4. Mapping natural σ⁷⁰ sequence diversity to the E. coli σ⁷⁰ fitness landscape reveals significant predicted fitness deficits across σ⁷⁰ orthologs. Interestingly, these predicted deficits are larger than observed fitness changes for 15 σ⁷⁰ orthologs introduced into E. coli. Finally, we use a multiplexed transcriptional reporter assay and RNA sequencing (RNA-seq) to explore functional differences of several σ⁷⁰ orthologs. This work provides an in-depth analysis of σ⁷⁰ sequence and function to improve efforts to understand the evolution and engineering potential of this global regulator.

High-Throughput Transcriptional Characterization of Regulatory Sequences from Bacterial Biosynthetic Gene Clusters

Recent efforts to sequence, survey, and functionally characterize the diverse biosynthetic capabilities of bacteria have identified numerous Biosynthetic Gene Clusters (BGCs). Genes found within BGCs are typically transcriptionally silent, suggesting their expression is tightly regulated. To better elucidate the underlying mechanisms and principles that govern BGC regulation on a DNA sequence level, we employed high-throughput DNA synthesis and multiplexed reporter assays to build and to characterize a library of BGC-derived regulatory sequences. Regulatory sequence transcription levels were measured in the Actinobacteria Streptomyces albidoflavus J1074, a popular model strain from a genus rich in BGC diversity. Transcriptional activities varied over 1000-fold in range and were used to identify key features associated with expression, including GC content, transcription start sites, and sequence motifs. Furthermore, we demonstrated that transcription levels could be modulated through coexpression of global regulatory proteins. Lastly, we developed and optimized a S. albidoflavus cell-free expression system for rapid characterization of regulatory sequences. This work helps to elucidate the regulatory landscape of BGCs and provides a diverse library of characterized regulatory sequences for rational engineering and activation of cryptic BGCs.

Advances in promoter engineering: novel applications and predefined transcriptional control

Synthetic biology continues to progress by relying on more robust tools for transcriptional control, of which promoters are the most fundamental component. Numerous studies have sought to characterize promoter function, determine principles to guide their engineering, and create promoters with stronger expression or tailored inducible control. In this review, we will summarize promoter architecture and highlight recent advances in the field, focusing on the novel applications of inducible promoter design and engineering towards metabolic engineering and cellular therapeutic development. Additionally, we will highlight how the expansion of new, machine learning techniques for modeling and engineering promoter sequences are enabling more accurate prediction of promoter characteristics. This article is protected by copyright. All rights reserved.

Quantitative Control for Stoichiometric Protein Synthesis

Bacterial protein synthesis rates have evolved to maintain preferred stoichiometries at striking precision, from the components of protein complexes to constituents of entire pathways. Setting relative protein production rates to be well within a factor of two requires concerted tuning of transcription, RNA turnover, and translation, allowing many potential regulatory strategies to achieve the preferred output. The last decade has seen a greatly expanded capacity for precise interrogation of each step of the central dogma genome-wide. Here, we summarize how these technologies have shaped the current understanding of diverse bacterial regulatory architectures underpinning stoichiometric protein synthesis. We focus on the emerging expanded view of bacterial operons, which encode diverse primary and secondary mRNA structures for tuning protein stoichiometry. Emphasis is placed on how quantitative tuning is achieved. We discuss the challenges and open questions in the application of quantitative, genome-wide methodologies to the problem of precise protein production. Expected final online publication date for the Annual Review of Microbiology, Volume 75 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Efficiency of the synthetic self-splicing RiboJ ribozyme is robust to cis- and trans-changes in genetic background

The expanding knowledge of the variety of synthetic genetic elements has enabled the construction of new and more efficient genetic circuits and yielded novel insights into molecular mechanisms. However, context dependence, in which interactions between cis- or trans-genetic elements affect the behavior of these elements, can reduce their general applicability or predictability. Genetic insulators, which mitigate unintended context-dependent cis-interactions, have been used to address this issue. One of the most commonly used genetic insulators is a self-splicing ribozyme called RiboJ, which can be used to decouple upstream 5' UTR in mRNA from downstream sequences (e.g., open reading frames). Despite its general use as an insulator, there has been no systematic study quantifying the efficiency of RiboJ splicing or whether this autocatalytic activity is robust to trans- and cis-genetic context. Here, we determine the robustness of RiboJ splicing in the genetic context of six widely divergent E. coli strains. We also check for possible cis-effects by assessing two SNP versions close to the catalytic site of RiboJ. We show that mRNA molecules containing RiboJ are rapidly spliced even during rapid exponential growth and high levels of gene expression, with a mean efficiency of 98%. We also show that neither the cis- nor trans-genetic context has a significant impact on RiboJ activity, suggesting this element is robust to both cis- and trans-genetic changes.

Learning the Regulatory Code of Gene Expression

Data-driven machine learning is the method of choice for predicting molecular phenotypes from nucleotide sequence, modeling gene expression events including protein-DNA binding, chromatin states as well as mRNA and protein levels. Deep neural networks automatically learn informative sequence representations and interpreting them enables us to improve our understanding of the regulatory code governing gene expression. Here, we review the latest developments that apply shallow or deep learning to quantify molecular phenotypes and decode the cis-regulatory grammar from prokaryotic and eukaryotic sequencing data. Our approach is to build from the ground up, first focusing on the initiating protein-DNA interactions, then specific coding and non-coding regions, and finally on advances that combine multiple parts of the gene and mRNA regulatory structures, achieving unprecedented performance. We thus provide a quantitative view of gene expression regulation from nucleotide sequence, concluding with an information-centric overview of the central dogma of molecular biology.

A navigation guide of synthetic biology tools for Pseudomonas putida

Pseudomonas putida is a microbial chassis of huge potential for industrial and environmental biotechnology, owing to its remarkable metabolic versatility and ability to sustain difficult redox reactions and operational stresses, among other attractive characteristics. A wealth of genetic and in silico tools have been developed to enable the unravelling of its physiology and improvement of its performance. However, the rise of this microbe as a promising platform for biotechnological applications has resulted in diversification of tools and methods rather than standardization and convergence. As a consequence, multiple tools for the same purpose have been generated, whilst most of them have not been embraced by the scientific community, which has led to compartmentalization and inefficient use of resources. Inspired by this and by the substantial increase in popularity of P. putida, we aim herein to bring together and assess all currently available (wet and dry) synthetic biology tools specific for this microbe, focusing on the last 5 years. We provide information on the principles, functionality, advantages and limitations, with special focus on their use in metabolic engineering. Additionally, we compare the tool portfolio for P. putida with those for other bacterial chassis and discuss potential future directions for tool development. Therefore, this review is intended as a reference guide for experts and new 'users' of this promising chassis.

The Context-Dependent Influence of Promoter Sequence Motifs on Transcription Initiation Kinetics and Regulation

The fitness of an individual bacterial cell is highly dependent upon the temporal tuning of gene expression levels when subjected to different environmental cues. Kinetic regulation of transcription initiation is a key step in modulating the levels of transcribed genes to promote bacterial survival. The initiation phase encompasses the binding of RNA polymerase (RNAP) to promoter DNA and a series of coupled protein-DNA conformational changes prior to entry into processive elongation. The time required to complete the initiation phase can vary by orders of magnitude and is ultimately dictated by the DNA sequence of the promoter. In this review, we aim to provide the required background to understand how promoter sequence motifs may affect initiation kinetics during promoter recognition and binding, subsequent conformational changes which lead to DNA opening around the transcription start site, and promoter escape. By calculating the steady-state flux of RNA production as a function of these effects, we illustrate that the presence/absence of a consensus promoter motif cannot be used in isolation to make conclusions regarding promoter strength. Instead, the entire series of linked, sequence-dependent structural transitions must be considered holistically. Finally, we describe how individual transcription factors take advantage of the broad distribution of sequence-dependent basal kinetics to either increase or decrease RNA flux.

Overcoming the design, build, test bottleneck for synthesis of nonrepetitive protein-RNA cassettes

We apply an oligo-library and machine learning-approach to characterize the sequence and structural determinants of binding of the phage coat proteins (CPs) of bacteriophages MS2 (MCP), PP7 (PCP), and Qβ (QCP) to RNA. Using the oligo library, we generate thousands of candidate binding sites for each CP, and screen for binding using a high-throughput dose-response Sort-seq assay (iSort-seq). We then apply a neural network to expand this space of binding sites, which allowed us to identify the critical structural and sequence features for binding of each CP. To verify our model and experimental findings, we design several non-repetitive binding site cassettes and validate their functionality in mammalian cells. We find that the binding of each CP to RNA is characterized by a unique space of sequence and structural determinants, thus providing a more complete description of CP-RNA interaction as compared with previous low-throughput findings. Finally, based on the binding spaces we demonstrate a computational tool for the successful design and rapid synthesis of functional non-repetitive binding-site cassettes.

Phage-coat proteins can be used to build synthetic biology parts. Here the authors use an oligo library and machine learning to predict and verify sequences based on binding scores.

Binding Revisited-Avidity in Cellular Function and Signaling

When characterizing biomolecular interactions, avidity, is an umbrella term used to describe the accumulated strength of multiple specific and unspecific interactions between two or more interaction partners. In contrast to the affinity, which is often sufficient to describe monovalent interactions in solution and where the binding strength can be accurately determined by considering only the relationship between the microscopic association and dissociation rates, the avidity is a phenomenological macroscopic parameter linked to several microscopic events. Avidity also covers potential effects of reduced dimensionality and/or hindered diffusion observed at or near surfaces e.g., at the cell membrane. Avidity is often used to describe the discrepancy or the "extra on top" when cellular interactions display binding that are several orders of magnitude stronger than those estimated in vitro. Here we review the principles and theoretical frameworks governing avidity in biological systems and the methods for predicting and simulating avidity. While the avidity and effects thereof are well-understood for extracellular biomolecular interactions, we present here examples of, and discuss how, avidity and the underlying kinetics influences intracellular signaling processes.

Multiplexed characterization of rationally designed promoter architectures deconstructs combinatorial logic for IPTG-inducible systems

A crucial step towards engineering biological systems is the ability to precisely tune the genetic response to environmental stimuli. In the case of Escherichia coli inducible promoters, our incomplete understanding of the relationship between sequence composition and gene expression hinders our ability to predictably control transcriptional responses. Here, we profile the expression dynamics of 8269 rationally designed, IPTG-inducible promoters that collectively explore the individual and combinatorial effects of RNA polymerase and LacI repressor binding site strengths. We then fit a statistical mechanics model to measured expression that accurately models gene expression and reveals properties of theoretically optimal inducible promoters. Furthermore, we characterize three alternative promoter architectures and show that repositioning binding sites within promoters influences the types of combinatorial effects observed between promoter elements. In total, this approach enables us to deconstruct relationships between inducible promoter elements and discover practical insights for engineering inducible promoters with desirable characteristics.

RNA Polymerase and Transcription Mechanisms: The Forefront of Physicochemical Studies of Chemical Reactions

The study of transcription and its regulation is an interdisciplinary field that is closely connected with genetics, structural biology, and reaction theory. Among these, although less attention has been paid to reaction theory, it is becoming increasingly useful for research on transcription. Rate equations are commonly used to describe reactions involved in transcription, but they tend to be used unaware of the timescales of relevant physical processes. In this review, we discuss the limitation of rate equation for describing three-dimensional diffusion and one-dimensional diffusion along DNA. We then introduce the chemical ratchet mechanism recently proposed for explaining the antenna effect, an enhancement of the binding affinity to a specific site on longer DNA, which deviates from a thermodynamic rule. We show that chemical ratchet cannot be described with a single set of rate equations but alternative sets of rate equations that temporally switch no faster than the binding reaction.

Predictive design of sigma factor-specific promoters

To engineer synthetic gene circuits, molecular building blocks are developed which can modulate gene expression without interference, mutually or with the host's cell machinery. As the complexity of gene circuits increases, automated design tools and tailored building blocks to ensure perfect tuning of all components in the network are required. Despite the efforts to develop prediction tools that allow forward engineering of promoter transcription initiation frequency (TIF), such a tool is still lacking. Here, we use promoter libraries of E. coli sigma factor 70 (σ70)- and B. subtilis σB-, σF- and σW-dependent promoters to construct prediction models, capable of both predicting promoter TIF and orthogonality of the σ-specific promoters. This is achieved by training a convolutional neural network with high-throughput DNA sequencing data from fluorescence-activated cell sorted promoter libraries. This model functions as the base of the online promoter design tool (ProD), providing tailored promoters for tailored genetic systems.

The Ongoing Quest to Crack the Genetic Code for Protein Production

Understanding the genetic design principles that determine protein production remains a major challenge. Although the key principles of gene expression were discovered 50 years ago, additional factors are still being uncovered. Both protein-coding and non-coding sequences harbor elements that collectively influence the efficiency of protein production by modulating transcription, mRNA decay, and translation. The influences of many contributing elements are intertwined, which complicates a full understanding of the individual factors. In natural genes, a functional balance between these factors has been obtained in the course of evolution, whereas for genetic-engineering projects, our incomplete understanding still limits optimal design of synthetic genes. However, notable advances have recently been made, supported by high-throughput analysis of synthetic gene libraries as well as by state-of-the-art biomolecular techniques. We discuss here how these advances further strengthen understanding of the gene expression process and how they can be harnessed to optimize protein production.

Deciphering the regulatory genome of Escherichia coli, one hundred promoters at a time

Advances in DNA sequencing have revolutionized our ability to read genomes. However, even in the most well-studied of organisms, the bacterium Escherichia coli, for ≈65% of promoters we remain ignorant of their regulation. Until we crack this regulatory Rosetta Stone, efforts to read and write genomes will remain haphazard. We introduce a new method, Reg-Seq, that links massively parallel reporter assays with mass spectrometry to produce a base pair resolution dissection of more than a E. coli promoters in 12 growth conditions. We demonstrate that the method recapitulates known regulatory information. Then, we examine regulatory architectures for more than 80 promoters which previously had no known regulatory information. In many cases, we also identify which transcription factors mediate their regulation. This method clears a path for highly multiplexed investigations of the regulatory genome of model organisms, with the potential of moving to an array of microbes of ecological and medical relevance.

Benchmarking Bacterial Promoter Prediction Tools: Potentialities and Limitations

The correct mapping of promoter elements is a crucial step in microbial genomics. Also, when combining new DNA elements into synthetic sequences, predicting the potential generation of new promoter sequences is critical. Over the last years, many bioinformatics tools have been created to allow users to predict promoter elements in a sequence or genome of interest. Here, we assess the predictive power of some of the main prediction tools available using well-defined promoter data sets. Using Escherichia coli as a model organism, we demonstrated that while some tools are biased toward AT-rich sequences, others are very efficient in identifying real promoters with low false-negative rates. We hope the potentials and limitations presented here will help the microbiology community to choose promoter prediction tools among many available alternatives.

The promoter region is a key element required for the production of RNA in bacteria. While new high-throughput technology allows massively parallel mapping of promoter elements, we still mainly rely on bioinformatics tools to predict such elements in bacterial genomes. Additionally, despite many different prediction tools having become popular to identify bacterial promoters, no systematic comparison of such tools has been performed. Here, we performed a systematic comparison between several widely used promoter prediction tools (BPROM, bTSSfinder, BacPP, CNNProm, IBBP, Virtual Footprint, iPro70-FMWin, 70ProPred, iPromoter-2L, and MULTiPly) using well-defined sequence data sets and standardized metrics to determine how well those tools performed related to each other. For this, we used data sets of experimentally validated promoters from Escherichia coli and a control data set composed of randomly generated sequences with similar nucleotide distributions. We compared the performance of the tools using metrics such as specificity, sensitivity, accuracy, and Matthews correlation coefficient (MCC). We show that the widely used BPROM presented the worse performance among the compared tools, while four tools (CNNProm, iPro70-FMWin, 70ProPred, and iPromoter-2L) offered high predictive power. Of these tools, iPro70-FMWin exhibited the best results for most of the metrics used. We present here some potentials and limitations of available tools, and we hope that future work can build upon our effort to systematically characterize this useful class of bioinformatics tools.

IMPORTANCE The correct mapping of promoter elements is a crucial step in microbial genomics. Also, when combining new DNA elements into synthetic sequences, predicting the potential generation of new promoter sequences is critical. Over the last years, many bioinformatics tools have been created to allow users to predict promoter elements in a sequence or genome of interest. Here, we assess the predictive power of some of the main prediction tools available using well-defined promoter data sets. Using Escherichia coli as a model organism, we demonstrated that while some tools are biased toward AT-rich sequences, others are very efficient in identifying real promoters with low false-negative rates. We hope the potentials and limitations presented here will help the microbiology community to choose promoter prediction tools among many available alternatives.

Comprehensive study on Escherichia coli genomic expression: Does position really matter?

As a biorefinery platform host, Escherichia coli has been used extensively to produce metabolites of commercial interest. Integration of foreign DNA onto the bacterial genome allows for stable expression overcoming the need for plasmid expression and its associated instability. Despite the development of numerous tools and genome editing technologies, the question of where to incorporate a synthetic pathway remains unanswered. To address this issue, we studied the genomic expression in E. coli and linked it not only to 26 rationally selected genomic locations, but also to the gene direction in relation to the DNA replication fork, to the carbon and nitrogen source, to DNA folding and supercoiling, and to metabolic burden. To enable these experiments, we have designed a fluorescent expression cassette to eliminate specific local effects on gene expression. Overall it can be concluded that although the expression range obtained by changing the genomic location of a pathway is small compared to the range typically seen in promoter-RBS libraries, the effect of culture medium, environmental stress and metabolic burden can be substantial. The characterization of multiple effects on genomic expression, and the associated libraries of well-characterized strains, will only stimulate and improve the creation of stable production hosts fit for industrial settings.

Automated design of thousands of nonrepetitive parts for engineering stable genetic systems

Engineered genetic systems are prone to failure when their genetic parts contain repetitive sequences. Designing many nonrepetitive genetic parts with desired functionalities remains a difficult challenge with high computational complexity. To overcome this challenge, we developed the Nonrepetitive Parts Calculator to rapidly generate thousands of highly nonrepetitive genetic parts from specified design constraints, including promoters, ribosome-binding sites and terminators. As a demonstration, we designed and experimentally characterized 4,350 nonrepetitive bacterial promoters with transcription rates that varied across a 820,000-fold range, and 1,722 highly nonrepetitive yeast promoters with transcription rates that varied across a 25,000-fold range. We applied machine learning to explain how specific interactions controlled the promoters' transcription rates. We also show that using nonrepetitive genetic parts substantially reduces homologous recombination, resulting in greater genetic stability.

Genome and sequence determinants governing the expression of horizontally acquired DNA in bacteria

While horizontal gene transfer is prevalent across the biosphere, the regulatory features that enable expression and functionalization of foreign DNA remain poorly understood. Here, we combine high-throughput promoter activity measurements and large-scale genomic analysis of regulatory regions to investigate the cross-compatibility of regulatory elements (REs) in bacteria. Functional characterization of thousands of natural REs in three distinct bacterial species revealed distinct expression patterns according to RE and recipient phylogeny. Host capacity to activate foreign promoters was proportional to their genomic GC content, while many low GC regulatory elements were both broadly active and had more transcription start sites across hosts. The difference in expression capabilities could be explained by the influence of the host GC content on the stringency of the AT-rich canonical σ70 motif necessary for transcription initiation. We further confirm the generalizability of this model and find widespread GC content adaptation of the σ70 motif in a set of 1,545 genomes from all major bacterial phyla. Our analysis identifies a key mechanism by which the strength of the AT-rich σ70 motif relative to a host's genomic GC content governs the capacity for expression of acquired DNA. These findings shed light on regulatory adaptation in the context of evolving genomic composition.

Transcript Barcoding Illuminates the Expression Level of Synthetic Constructs in E. coli Nissle Residing in the Mammalian Gut

The development of robust engineered probiotic therapies demands accurate knowledge of genetic construct expression in the gut. However, the monetary and ethical costs of testing engineered strains in vertebrate hosts are incompatible with current high-throughput design-build-test cycles. To enable parallel measurement of multiple construct designs, we placed unique DNA barcodes in engineered transcripts and measured barcode abundances via sequencing. In standard curve experiments, the barcode sequences exhibited consistent relationships between input and measured abundances, which allowed us to use transcript barcoding to measure expression levels of 30 GFP-expressing strains of E. coli Nissle in parallel. Applying this technology in culture and in the mouse gut, we found that GFP expression in the gut could often be predicted from expression levels in culture, but several strains exhibited gut-specific expression. This work establishes the experimental design parameters and advantages of transcript barcoding to measure the performance of many engineered probiotic designs in mammalian hosts.

Realization of Robust and Precise Regulation of Gene Expression by Multiple Sigma Recognizable Artificial Promoters

Precise regulation of gene expression is fundamental for tailor-made gene circuit design in synthetic biology. Current strategies for this type of development are mainly based on directed evolution beginning with a native promoter template. The performances of engineered promoters are usually limited by the growth phase because only one promoter is recognized by one type of sigma factor (σ). Here, we constructed multiple-σ recognizable artificial hybrid promoters (AHPs) composed of tandems of dual and triple natural minimal promoters (NMPs). These NMPs, which use σ^A, σ^H and σ^W, had stable functions in different growth phases. The functions of these NMPs resulted from an effect called transcription compensation, in which AHPs sequentially use one type of σ in the corresponding growth phase. The strength of the AHPs was influenced by the combinatorial order of each NMP and the length of the spacers between the NMPs. More importantly, the output of the precise regulation was achieved by equipping AHPs with synthetic ribosome binding sites and by redesigning them for induced systems. This strategy might offer promising applications to rationally design robust synthetic promoters in diverse chassis to spur the construction of more complex gene circuits, which will further the development of synthetic biology.

Context-dependency of synthetic minimal promoters in driving gene expression: a case study

Synthetic promoters are considered ideal candidates in driving robust gene expression. Most of the available synthetic promoters are minimal promoters, for which the upstream sequence of the 5' end of the core region is usually excluded. Although the upstream sequence has been shown to mediate transcription of natural promoters, its impact on synthetic promoters has not been widely studied. Here, a library of chromosomal DNA fragments is randomly fused with the 5' end of the J23119 synthetic promoter, and the transcriptional performance of the promoter is evaluated through β-galactosidase assay, fluorescence intensity and chemical biosynthesis. Results show that changes in the upstream sequence can induce significant variation in the promoter strength of up to 5.8-fold. The effect is independent of the length of the insertions and the number of potential transcription factor binding sites. Several DNA fragments that are able to enhance the transcription of both the natural and the synthetic promoters are identified. This study indicates that the synthetic minimal promoters are susceptible to the surrounding sequence context. Therefore, the upstream sequence should be treated as an indispensable component in the design and application of synthetic promoters, or as an independent genetic part for the fine-tuning of gene expression.

Multiplex transcriptional characterizations across diverse bacterial species using cell-free systems

Cell-free expression systems enable rapid prototyping of genetic programs in vitro. However, current throughput of cell-free measurements is limited by the use of channel-limited fluorescent readouts. Here, we describe DNA Regulatory element Analysis by cell-Free Transcription and Sequencing (DRAFTS), a rapid and robust in vitro approach for multiplexed measurement of transcriptional activities from thousands of regulatory sequences in a single reaction. We employ this method in active cell lysates developed from ten diverse bacterial species. Interspecies analysis of transcriptional profiles from > 1,000 diverse regulatory sequences reveals functional differences in promoter activity that can be quantitatively modeled, providing a rich resource for tuning gene expression in diverse bacterial species. Finally, we examine the transcriptional capacities of dual-species hybrid lysates that can simultaneously harness gene expression properties of multiple organisms. We expect that this cell-free multiplex transcriptional measurement approach will improve genetic part prototyping in new bacterial chassis for synthetic biology.

How the avidity of polymerase binding to the -35/-10 promoter sites affects gene expression

Although the key promoter elements necessary to drive transcription in Escherichia coli have long been understood, we still cannot predict the behavior of arbitrary novel promoters, hampering our ability to characterize the myriad sequenced regulatory architectures as well as to design new synthetic circuits. This work builds upon a beautiful recent experiment by Urtecho et al. [G. Urtecho, et al, Biochemistry, 68, 1539-1551 (2019)] who measured the gene expression of over 10,000 promoters spanning all possible combinations of a small set of regulatory elements. Using these data, we demonstrate that a central claim in energy matrix models of gene expression-that each promoter element contributes independently and additively to gene expression-contradicts experimental measurements. We propose that a key missing ingredient from such models is the avidity between the -35 and -10 RNA polymerase binding sites and develop what we call a multivalent model that incorporates this effect and can successfully characterize the full suite of gene expression data. We explore several applications of this framework, namely, how multivalent binding at the -35 and -10 sites can buffer RNA polymerase (RNAP) kinetics against mutations and how promoters that bind overly tightly to RNA polymerase can inhibit gene expression. The success of our approach suggests that avidity represents a key physical principle governing the interaction of RNA polymerase to its promoter.

Mapping DNA sequence to transcription factor binding energy in vivo

Despite the central importance of transcriptional regulation in biology, it has proven difficult to determine the regulatory mechanisms of individual genes, let alone entire gene networks. It is particularly difficult to decipher the biophysical mechanisms of transcriptional regulation in living cells and determine the energetic properties of binding sites for transcription factors and RNA polymerase. In this work, we present a strategy for dissecting transcriptional regulatory sequences using in vivo methods (massively parallel reporter assays) to formulate quantitative models that map a transcription factor binding site’s DNA sequence to transcription factor-DNA binding energy. We use these models to predict the binding energies of transcription factor binding sites to within 1 k_BT of their measured values. We further explore how such a sequence-energy mapping relates to the mechanisms of trancriptional regulation in various promoter contexts. Specifically, we show that our models can be used to design specific induction responses, analyze the effects of amino acid mutations on DNA sequence preference, and determine how regulatory context affects a transcription factor’s sequence specificity.

It has been said that we live in the “genomic era,” a time where we can readily sequence full genomes at will. However, it remains difficult to interpret much of the information within a genome. This is especially true of non-coding sequences such as promoters, which contain a number of features such as transcription factor binding sites that determine how genes are regulated. There is no straightforward regulatory “code” that tells us how transcription factor binding sites are organized within a promoter. In this work we examine how DNA sequence determines one of the most important features of a promoter, the strength with which a transcription factor binds to its DNA binding site. We discuss an approach to modeling DNA sequence-specific transcription factor binding energies in vivo using a massively parellel reporter assay. We develop models that allow us to predict the binding energy between a transcription factor and a mutated version of its binding site. We then show that this modeling technique can be used to address a number of scientific and design questions, such as engineering the behavior of genetic circuit elements or examining how transcription factors and their binding sites co-evolve.

Systematic approach for dissecting the molecular mechanisms of transcriptional regulation in bacteria

Organisms must constantly make regulatory decisions in response to a change in cellular state or environment. However, while the catalog of genomes expands rapidly, we remain ignorant about how the genes in these genomes are regulated. Here, we show how a massively parallel reporter assay, Sort-Seq, and information-theoretic modeling can be used to identify regulatory sequences. We then use chromatography and mass spectrometry to identify the regulatory proteins that bind these sequences. The approach results in quantitative base pair-resolution models of promoter mechanism and was shown in both well-characterized and unannotated promoters in Escherichia coli. Given the generality of the approach, it opens up the possibility of quantitatively dissecting the mechanisms of promoter function in a wide range of bacteria.

Gene regulation is one of the most ubiquitous processes in biology. However, while the catalog of bacterial genomes continues to expand rapidly, we remain ignorant about how almost all of the genes in these genomes are regulated. At present, characterizing the molecular mechanisms by which individual regulatory sequences operate requires focused efforts using low-throughput methods. Here, we take a first step toward multipromoter dissection and show how a combination of massively parallel reporter assays, mass spectrometry, and information-theoretic modeling can be used to dissect multiple bacterial promoters in a systematic way. We show this approach on both well-studied and previously uncharacterized promoters in the enteric bacterium Escherichia coli. In all cases, we recover nucleotide-resolution models of promoter mechanism. For some promoters, including previously unannotated ones, the approach allowed us to further extract quantitative biophysical models describing input–output relationships. Given the generality of the approach presented here, it opens up the possibility of quantitatively dissecting the mechanisms of promoter function in E. coli and a wide range of other bacteria.