Composability of regulatory sequences controlling transcription and translation in Escherichia coli

Nucleotide distribution analysis of 5'UTRs in genome-scale directs their redesign and expression regulation in yeast

Non-conventional yeasts have emerged as important sources of valuable products in bioindustries. However, tools for the control of expression are limited in these hosts. In this study, we aimed to excavate the tools for the regulation of translation that are often overlooked. 5'UTR analysis of genome-scale annotated genes of four yeast species revealed a distinct decreasing 'G' frequency in -100 ∼ -1 region from 5040 5'UTRs in Komagataella phaffii. New 5'UTRs were regenerated by base substitutions in defined regions, and replacement of 'G' by 'A' or 'T' in the -50 ∼ -1 region highly facilitated gene expression. Preference analysis of all nucleotide triplets in 5'UTRs revealed a KZ3 (-3 ∼ -1) that dominantly affected gene expression. A total of 128 KZ3 variants were constructed to work with promoters of methanol-inducible PAOX1 and constitutive PGAP, of which 58 KZ3 variants increased gene expression and maximum difference in strength was 15-fold among all variants. Polysome profiling analysis clarified that 5'UTR-KZ3 enhanced gene expression at translational but not transcriptional levels. Finally, improved production of three industrial proteins and one platform compound were achieved by ready-made 5'UTR-KZ3 or in situ modification of the 5'UTR. This study provides new references and tools for the fine-tuning of translational regulation in yeast and other fungi.

Systematic representation and optimization enable the inverse design of cross-species regulatory sequences in bacteria

Regulatory sequences encode crucial gene expression signals, yet the sequence characteristics that determine their functionality across species remain obscure. Deep generative models have demonstrated considerable potential in various inverse design applications, especially in engineering genetic elements. Here, we introduce DeepCROSS, a generative artificial intelligence framework for the inverse design of cross-species and species-preferred 5' regulatory sequences in bacteria. DeepCROSS constructs a meta-representation using 1.8 million regulatory sequences from thousands of bacterial genomes to depict the general constraints of regulatory sequences, employs artificial intelligence-guided massively parallel reporter assay experiments in E. coli and P. aeruginosa to explore the potential sequence space, and performs multi-task optimization to obtain de novo regulatory sequences. The optimized regulatory sequences achieve similar or better performance to functional natural regulatory sequences, with high success rates and low sequence similarities with the natural genome. Collectively, DeepCROSS efficiently navigates the sequence-function landscape and enables the inverse design of cross-species and species-preferred 5' regulatory sequences.

RNA folding kinetics control riboswitch sensitivity in vivo

Riboswitches are ligand-responsive gene-regulatory RNA elements that perform key roles in maintaining cellular homeostasis. Understanding how riboswitch sensitivity to ligand (EC50) is controlled is critical to explain how highly conserved aptamer domains are deployed in a variety of contexts with different sensitivity demands. Here we uncover roles by which RNA folding dynamics control riboswitch sensitivity in cells. By investigating the Clostridium beijerinckii pfl ZTP riboswitch, we identify multiple mechanistic routes of altering expression platform sequence and structure to slow RNA folding, all of which enhance riboswitch sensitivity. Applying these methods to riboswitches with diverse aptamer architectures and regulatory mechanisms demonstrates the generality of our findings, indicating that any riboswitch that operates in a kinetic regime can be sensitized by slowing expression platform folding. Our results add to the growing suite of knowledge and approaches that can be used to rationally program cotranscriptional RNA folding for biotechnology applications, and suggest general RNA folding principles for understanding dynamic RNA systems in other areas of biology.

Automated Design of Oligopools and Rapid Analysis of Massively Parallel Barcoded Measurements

Oligopool synthesis and next-generation sequencing enable the construction and characterization of large libraries of designed genetic parts and systems. As library sizes grow, it becomes computationally challenging to optimally design large numbers of primer binding sites, barcode sequences, and overlap regions to obtain efficient assemblies and precise measurements. We present the Oligopool Calculator, an end-to-end suite of algorithms and data structures that rapidly designs many thousands of oligonucleotides within an oligopool and rapidly analyzes many billions of barcoded sequencing reads. We introduce several novel concepts that greatly increase the design and analysis throughput, including orthogonally symmetric barcode design, adaptive decision trees for primer design, a Scry barcode classifier, and efficient read packing. We demonstrate the Oligopool Calculator's capabilities across computational benchmarks and real-data projects, including the design of over four million highly unique and compact barcodes in 1.2 h, the design of universal primer binding sites for one million 200-mer oligos in 15 min, and the analysis of about 500 million deep sequencing reads per hour, all on an 8-core desktop computer. Overall, the Oligopool Calculator accelerates the creative use of massively parallel experiments by eliminating the computational complexity of their design and analysis.

Unlocking the potential of cyanobacteria: a high-throughput strategy for enhancing biocatalytic performance through genetic optimization

Cyanobacteria show promise as hosts for whole-cell biocatalysis. Their photoautotrophic metabolism can be leveraged for a sustainable production process. Despite advancements, performance still lags behind heterotrophic hosts. A key challenge is the limited ability to overexpress recombinant enzymes, which also hinders their biocatalytic efficiency. To address this, we generated large-scale expression libraries and developed a high-throughput method combining fluorescence-activated cell sorting (FACS) and deep sequencing in Synechocystis sp. PCC 6803 (Syn. 6803) to screen and optimize its genetic background. We apply this approach to enhance expression and biocatalyst performance for three enzymes: the ketoreductase LfSDR1M50, enoate reductase YqjM, and Baeyer-Villiger monooxygenase (BVMO) CHMOmut. Diverse genetic combinations yielded significant improvements: optimizing LfSDR1M50 expression showed a 17-fold increase to 39.2 U gcell dry weight (CDW)-1. In vivo activity of Syn. YqjM was improved 16-fold to 58.7 U gCDW-1 and, for Syn. CHMOmut, a 1.5-fold increase to 7.3 U gCDW-1 was achieved by tailored genetic design. Thus, this strategy offers a pathway to optimize cyanobacteria as expression hosts, paving the way for broader applications in other cyanobacteria strains and larger libraries.

Automated characterization and analysis of expression compatibility between regulatory sequences and metabolic genes in Escherichia coli

Utilizing standardized artificial regulatory sequences to fine-tuning the expression of multiple metabolic pathways/genes is a key strategy in the creation of efficient microbial cell factories. However, when regulatory sequence expression strengths are characterized using only a few reporter genes, they may not be applicable across diverse genes. This introduces great uncertainty into the precise regulation of multiple genes at multiple expression levels. To address this, our study adopted a fluorescent protein fusion strategy for a more accurate assessment of target protein expression levels. We combined 41 commonly-used metabolic genes with 15 regulatory sequences, yielding an expression dataset encompassing 520 unique combinations. This dataset highlighted substantial variation in protein expression level under identical regulatory sequences, with relative expression levels ranging from 2.8 to 176-fold. It also demonstrated that improving the strength of regulatory sequences does not necessarily lead to significant improvements in the expression levels of target proteins. Utilizing this dataset, we have developed various machine learning models and discovered that the integration of promoter regions, ribosome binding sites, and coding sequences significantly improves the accuracy of predicting protein expression levels, with a Spearman correlation coefficient of 0.72, where the promoter sequence exerts a predominant influence. Our study aims not only to provide a detailed guide for fine-tuning gene expression in the metabolic engineering of Escherichia coli but also to deepen our understanding of the compatibility issues between regulatory sequences and target genes.

Highly multiplexed design of an allosteric transcription factor to sense new ligands

Allosteric transcription factors (aTF) regulate gene expression through conformational changes induced by small molecule binding. Although widely used as biosensors, aTFs have proven challenging to design for detecting new molecules because mutation of ligand-binding residues often disrupts allostery. Here, we develop Sensor-seq, a high-throughput platform to design and identify aTF biosensors that bind to non-native ligands. We screen a library of 17,737 variants of the aTF TtgR, a regulator of a multidrug exporter, against six non-native ligands of diverse chemical structures - four derivatives of the cancer therapeutic tamoxifen, the antimalarial drug quinine, and the opiate analog naltrexone - as well as two native flavonoid ligands, naringenin and phloretin. Sensor-seq identifies biosensors for each of these ligands with high dynamic range and diverse specificity profiles. The structure of a naltrexone-bound design shows shape-complementary methionine-aromatic interactions driving ligand specificity. To demonstrate practical utility, we develop cell-free detection systems for naltrexone and quinine. Sensor-seq enables rapid and scalable design of new biosensors, overcoming constraints of natural biosensors.

Rational Design of Untranslated Regions to Enhance Gene Expression

How to improve gene expression by optimizing mRNA structures is a crucial question for various medical and biotechnological applications. Previous efforts focus largely on investigation of the 5' UTR hairpin structures. In this study, we present a rational strategy that enhances mRNA stability and translation by engineering both the 5' and 3' UTR sequences. We have successfully demonstrated this strategy using green fluorescent protein (GFP) as a model in Escherichia coli and across different expression vectors. We further validated it with luciferase and Plasmodium falciparum lactate dehydrogenase (PfLDH). To elucidate the underlying mechanism, we have quantitatively analyzed both protein, mRNA levels and half-life time. We have identified several key aspects of UTRs that significantly influence mRNA stability and protein expression in our system: (1) The optimal length of the single-stranded spacer between the stabilizer hairpin and ribosome binding site (RBS) in the 5' UTR is 25-30 nucleotide (nt) long. An optimal 32% GC content in the spacer yielded the highest levels of GFP protein production. (2) The insertion of a homodimerdizable, G-quadruplex structure containing RNA aptamer, "Corn", in the 3' UTR markedly increased the protein expression. Our findings indicated that the carefully engineered 5' UTRs and 3' UTRs significantly boosted gene expression. Specifically, the inclusion of 5 × Corn in the 3' UTR appeared to facilitate the local aggregation of mRNA, leading to the formation of mRNA condensates. Aside from shedding light on the regulation of mRNA stability and expression, this study is expected to substantially increase biological protein production.

Sequence modeling and design from molecular to genome scale with Evo

The genome is a sequence that encodes the DNA, RNA, and proteins that orchestrate an organism's function. We present Evo, a long-context genomic foundation model with a frontier architecture trained on millions of prokaryotic and phage genomes, and report scaling laws on DNA to complement observations in language and vision. Evo generalizes across DNA, RNA, and proteins, enabling zero-shot function prediction competitive with domain-specific language models and the generation of functional CRISPR-Cas and transposon systems, representing the first examples of protein-RNA and protein-DNA codesign with a language model. Evo also learns how small mutations affect whole-organism fitness and generates megabase-scale sequences with plausible genomic architecture. These prediction and generation capabilities span molecular to genomic scales of complexity, advancing our understanding and control of biology.

Decoding biology with massively parallel reporter assays and machine learning

Massively parallel reporter assays (MPRAs) are powerful tools for quantifying the impacts of sequence variation on gene expression. Reading out molecular phenotypes with sequencing enables interrogating the impact of sequence variation beyond genome scale. Machine learning models integrate and codify information learned from MPRAs and enable generalization by predicting sequences outside the training data set. Models can provide a quantitative understanding of cis-regulatory codes controlling gene expression, enable variant stratification, and guide the design of synthetic regulatory elements for applications from synthetic biology to mRNA and gene therapy. This review focuses on cis-regulatory MPRAs, particularly those that interrogate cotranscriptional and post-transcriptional processes: alternative splicing, cleavage and polyadenylation, translation, and mRNA decay.

A promoter-RBS library for fine-tuning gene expression in Methanosarcina acetivorans

Methanogens are the main biological producers of methane on Earth. Methanosarcina acetivorans is one of the best characterized methanogens that has powerful genetic tools for genome editing. To study the physiology of this methanogen in further detail as well as to effectively balance the flux of their engineered metabolic pathways in expansive project undertakings, there is the need for controlled gene expression, which then requires the availability of well-characterized promoters and ribosome-binding sites (RBS). In this study, we constructed a library of 33 promoter-RBS combinations that includes 13 wild-type and 14 hybrid combinations, as well as six combination variants in which the 5'-untranslated region (5'UTR) was rationally engineered. The expression strength for each combination was calculated by inducing the expression of the β-glucuronidase reporter gene in M. acetivorans cells in the presence of the two most used growth substrates, either methanol (MeOH) or trimethyl amine (TMA). In this study, the constructed library covers a relatively wide range (140-fold) between the weakest and strongest promoter-RBS combination as well as shows a steady increase and allows different levels of gene expression. Effects on the gene expression strength were also assessed by making measurements at three distinct growth phases for all 33 promoter-RBS combinations. Our promoter-RBS library is effective in enabling the fine-tuning of gene expression in M. acetivorans for physiological studies and the design of metabolic engineering projects that, e.g., aim for the biotechnological valorization of one-carbon compounds.

IMPORTANCE

Methanogenic archaea are potent producers of the greenhouse gas methane and thus contribute substantially to global warming. Under controlled conditions, these microbes can catalyze the production of biogas, which is a renewable fuel, and might help counter global warming and its effects. Engineering the primary metabolism of Methanosarcina acetivorans to render it better and more useful requires controllable gene expression, yet only a few well-characterized promoters and RBSs are presently available. Our study rectifies this situation by providing a library of 33 different promoter-RBS combinations with a 140-fold dynamic range in expression strength. Future metabolic engineering projects can take advantage of this library by using these promoter-RBS combinations as an efficient and tunable gene expression system for M. acetivorans. Furthermore, the methodologies we developed in this study could also be utilized to construct promoter libraries for other types of methanogens.

Engineering bacteriophages through deep mining of metagenomic motifs

Bacteriophages can adapt to new hosts by altering sequence motifs through recombination or convergent evolution. Where such motifs exist and what fitness advantage they confer remains largely unknown. We report a new method, Metagenomic Sequence Informed Functional Scoring (Meta-SIFT), to discover sequence motifs in metagenomic datasets that can be used to engineer phage activity. Meta-SIFT uses experimental deep mutational scanning data to create sequence profiles to enable deep mining of metagenomes for functional motifs which are otherwise invisible to searches. We experimentally tested over 17,000 Meta-SIFT derived sequence motifs in the receptor-binding protein of the T7 phage. The screen revealed thousands of T7 variants with novel host specificity with functional motifs sourced from distant families. Position, substitution and location preferences dictated specificity across a panel of 20 hosts and conditions. To demonstrate therapeutic utility, we engineered active T7 variants against foodborne pathogen E. coli O121. Meta-SIFT is a powerful tool to unlock the functional potential encoded in phage metagenomes to engineer bacteriophages.

Engineering Escherichia coli with a symbiotic plasmid for the production of phenylpyruvic acid

Plasmid-based microbial systems have become a major avenue for the production of pharmaceutical and chemical products; however, antibiotics are often required to maintain the stability of the plasmid. To eliminate the need for antibiotics, we developed a symbiotic system between plasmids and hosts by knocking out the essential gene of folP on the chromosome and placing it on the same plasmid as l-amino acid dehydrogenase (aadL); the resulting strain was named E. coli A06ΔfolP. To increase the copy number of aadL, different strengths of promoters were used for the expression of folP, resulting in the creation of a mutant E. coli A17ΔfolP. The yield of phenylpyruvic acid (PPA) from E. coli A17ΔfolP (4.1 ± 0.3 g L-1) was 1.9-fold that of E. coli A06ΔfolP (2.1 ± 0.2 g L-1). Next, the stability of plasmids was tested, and results showed that the plasmids could be maintained stably for 10 transfer numbers under antibiotic-free conditions. Finally, E. coli A17ΔfolP was used to produce PPA; the yield of PPA was 18.7 g L-1 within 14 h.

Guide RNA structure design enables combinatorial CRISPRa programs for biosynthetic profiling

Engineering metabolism to efficiently produce chemicals from multi-step pathways requires optimizing multi-gene expression programs to achieve enzyme balance. CRISPR-Cas transcriptional control systems are emerging as important tools for programming multi-gene expression, but poor predictability of guide RNA folding can disrupt expression control. Here, we correlate efficacy of modified guide RNAs (scRNAs) for CRISPR activation (CRISPRa) in E. coli with a computational kinetic parameter describing scRNA folding rate into the active structure (rS = 0.8). This parameter also enables forward design of scRNAs, allowing us to design a system of three synthetic CRISPRa promoters that can orthogonally activate (>35-fold) expression of chosen outputs. Through combinatorial activation tuning, we profile a three-dimensional design space expressing two different biosynthetic pathways, demonstrating variable production of pteridine and human milk oligosaccharide products. This RNA design approach aids combinatorial optimization of metabolic pathways and may accelerate routine design of effective multi-gene regulation programs in bacterial hosts.

Transfer learning for cross-context prediction of protein expression from 5'UTR sequence

Model-guided DNA sequence design can accelerate the reprogramming of living cells. It allows us to engineer more complex biological systems by removing the need to physically assemble and test each potential design. While mechanistic models of gene expression have seen some success in supporting this goal, data-centric, deep learning-based approaches often provide more accurate predictions. This accuracy, however, comes at a cost - a lack of generalization across genetic and experimental contexts that has limited their wider use outside the context in which they were trained. Here, we address this issue by demonstrating how a simple transfer learning procedure can effectively tune a pre-trained deep learning model to predict protein translation rate from 5' untranslated region (5'UTR) sequence for diverse contexts in Escherichia coli using a small number of new measurements. This allows for important model features learnt from expensive massively parallel reporter assays to be easily transferred to new settings. By releasing our trained deep learning model and complementary calibration procedure, this study acts as a starting point for continually refined model-based sequence design that builds on previous knowledge and future experimental efforts.

Context-dependent redesign of robust synthetic gene circuits

Cells provide dynamic platforms for executing exogenous genetic programs in synthetic biology, resulting in highly context-dependent circuit performance. Recent years have seen an increasing interest in understanding the intricacies of circuit-host relationships, their influence on the synthetic bioengineering workflow, and in devising strategies to alleviate undesired effects. We provide an overview of how emerging circuit-host interactions, such as growth feedback and resource competition, impact both deterministic and stochastic circuit behaviors. We also emphasize control strategies for mitigating these unwanted effects. This review summarizes the latest advances and the current state of host-aware and resource-aware design of synthetic gene circuits.

Data hazards in synthetic biology

Data science is playing an increasingly important role in the design and analysis of engineered biology. This has been fueled by the development of high-throughput methods like massively parallel reporter assays, data-rich microscopy techniques, computational protein structure prediction and design, and the development of whole-cell models able to generate huge volumes of data. Although the ability to apply data-centric analyses in these contexts is appealing and increasingly simple to do, it comes with potential risks. For example, how might biases in the underlying data affect the validity of a result and what might the environmental impact of large-scale data analyses be? Here, we present a community-developed framework for assessing data hazards to help address these concerns and demonstrate its application to two synthetic biology case studies. We show the diversity of considerations that arise in common types of bioengineering projects and provide some guidelines and mitigating steps. Understanding potential issues and dangers when working with data and proactively addressing them will be essential for ensuring the appropriate use of emerging data-intensive AI methods and help increase the trustworthiness of their applications in synthetic biology.

Optimizing multicopy chromosomal integration for stable high-performing strains

The copy number of genes in chromosomes can be modified by chromosomal integration to construct efficient microbial cell factories but the resulting genetic systems are prone to failure or instability from triggering homologous recombination in repetitive DNA sequences. Finding the optimal copy number of each gene in a pathway is also time and labor intensive. To overcome these challenges, we applied a multiple nonrepetitive coding sequence calculator that generates sets of coding DNA sequence (CDS) variants. A machine learning method was developed to calculate the optimal copy number combination of genes in a pathway. We obtained an engineered Yarrowia lipolytica strain for eicosapentaenoic acid biosynthesis in 6 months, producing the highest titer of 27.5 g l-1 in a 50-liter bioreactor. Moreover, the lycopene production in Escherichia coli was also greatly improved. Importantly, all engineered strains of Y. lipolytica, E. coli and Saccharomyces cerevisiae constructed with nonrepetitive CDSs maintained genetic stability.

A parameterized two-domain thermodynamic model explains diverse mutational effects on protein allostery

New experimental findings continue to challenge our understanding of protein allostery. Recent deep mutational scanning study showed that allosteric hotspots in the tetracycline repressor (TetR) and its homologous transcriptional factors are broadly distributed rather than spanning well-defined structural pathways as often assumed. Moreover, hotspot mutation-induced allostery loss was rescued by distributed additional mutations in a degenerate fashion. Here, we develop a two-domain thermodynamic model for TetR, which readily rationalizes these intriguing observations. The model accurately captures the in vivo activities of various mutants with changes in physically transparent parameters, allowing the data-based quantification of mutational effects using statistical inference. Our analysis reveals the intrinsic connection of intra- and inter-domain properties for allosteric regulation and illustrate epistatic interactions that are consistent with structural features of the protein. The insights gained from this study into the nature of two-domain allostery are expected to have broader implications for other multi-domain allosteric proteins.

Regulatory properties of transcription factors with diverse mechanistic function

Transcription factors (TFs) regulate the process of transcription through the modulation of different kinetic steps. Although models can often describe the observed transcriptional output of a measured gene, predicting a TFs role on a given promoter requires an understanding of how the TF alters each step of the transcription process. In this work, we use a simple model of transcription to assess the role of promoter identity, and the degree to which TFs alter binding of RNAP (stabilization) and initiation of transcription (acceleration) on three primary characteristics: the range of steady-state regulation, cell-to-cell variability in expression, and the dynamic response time of a regulated gene. We find that steady state regulation and the response time of a gene behave uniquely for TFs that regulate incoherently, i.e that speed up one step but slow the other. We also find that incoherent TFs have dynamic implications, with one type of incoherent mode configuring the promoter to respond more slowly at intermediate TF concentrations. We also demonstrate that the noise of gene expression for these TFs is sensitive to promoter strength, with a distinct non-monotonic profile that is apparent under stronger promoters. Taken together, our work uncovers the coupling between promoters and TF regulatory modes with implications for understanding natural promoters and engineering synthetic gene circuits with desired expression properties.

Highly multiplexed design of an allosteric transcription factor to sense novel ligands

Allosteric transcription factors (aTF), widely used as biosensors, have proven challenging to design for detecting novel molecules because mutation of ligand-binding residues often disrupts allostery. We developed Sensor-seq, a high-throughput platform to design and identify aTF biosensors that bind to non-native ligands. We screened a library of 17,737 variants of the aTF TtgR, a regulator of a multidrug exporter, against six non-native ligands of diverse chemical structures - four derivatives of the cancer therapeutic tamoxifen, the antimalarial drug quinine, and the opiate analog naltrexone - as well as two native flavonoid ligands, naringenin and phloretin. Sensor-seq identified novel biosensors for each of these ligands with high dynamic range and diverse specificity profiles. The structure of a naltrexone-bound design showed shape-complementary methionine-aromatic interactions driving ligand specificity. To demonstrate practical utility, we developed cell-free detection systems for naltrexone and quinine. Sensor-seq enables rapid, scalable design of new biosensors, overcoming constraints of natural biosensors.

RNA folding kinetics control riboswitch sensitivity in vivo

Riboswitches are ligand-responsive gene-regulatory RNA elements that perform key roles in maintaining cellular homeostasis. Understanding how riboswitch sensitivity is controlled is critical to understanding how highly conserved aptamer domains are deployed in a variety of contexts with different sensitivity demands. Here we uncover new roles by which RNA folding dynamics control riboswitch sensitivity in cells. By investigating the Clostridium beijerinckii pfl ZTP riboswitch, we identify multiple mechanistic routes of altering expression platform sequence and structure to slow RNA folding, all of which enhance riboswitch sensitivity. Applying these methods to riboswitches with diverse aptamer architectures that regulate transcription and translation with ON and OFF logic demonstrates the generality of our findings, indicating that any riboswitch that operates in a kinetic regime can be sensitized by slowing expression platform folding. Comparison of the most sensitized versions of these switches to equilibrium aptamer:ligand dissociation constants suggests a limit to the sensitivities achievable by kinetic RNA switches. Our results add to the growing suite of knowledge and approaches that can be used to rationally program cotranscriptional RNA folding for biotechnology applications, and suggest general RNA folding principles for understanding dynamic RNA systems in other areas of biology.

A genetic toolbox to empower Paracoccus pantotrophus DSM 2944 as a metabolically versatile SynBio chassis

BACKGROUND

To contribute to the discovery of new microbial strains with metabolic and physiological robustness and develop them into successful chasses, Paracoccus pantotrophus DSM 2944, a Gram-negative bacterium from the phylum Alphaproteobacteria and the family Rhodobacteraceae, was chosen. The strain possesses an innate ability to tolerate high salt concentrations. It utilizes diverse substrates, including cheap and renewable feedstocks, such as C1 and C2 compounds. Also, it can consume short-chain alkanes, predominately found in hydrocarbon-rich environments, making it a potential bioremediation agent. The demonstrated metabolic versatility, coupled with the synthesis of the biodegradable polymer polyhydroxyalkanoate, positions this microbial strain as a noteworthy candidate for advancing the principles of a circular bioeconomy.

RESULTS

The study aims to follow the chassis roadmap, as depicted by Calero and Nikel, and de Lorenzo, to transform wild-type P. pantotrophus DSM 2944 into a proficient SynBio (Synthetic Biology) chassis. The initial findings highlight the antibiotic resistance profile of this prospective SynBio chassis. Subsequently, the best origin of replication (ori) was identified as RK2. In contrast, the non-replicative ori R6K was selected for the development of a suicide plasmid necessary for genome integration or gene deletion. Moreover, when assessing the most effective method for gene transfer, it was observed that conjugation had superior efficiency compared to electroporation, while transformation by heat shock was ineffective. Robust host fitness was demonstrated by stable plasmid maintenance, while standardized gene expression using an array of synthetic promoters could be shown. pEMG-based scarless gene deletion was successfully adapted, allowing gene deletion and integration. The successful integration of a gene cassette for terephthalic acid degradation is showcased. The resulting strain can grow on both monomers of polyethylene terephthalate (PET), with an increased growth rate achieved through adaptive laboratory evolution.

CONCLUSION

The chassis roadmap for the development of P. pantotrophus DSM 2944 into a proficient SynBio chassis was implemented. The presented genetic toolkit allows genome editing and therewith the possibility to exploit Paracoccus for a myriad of applications.

A parametrized two-domain thermodynamic model explains diverse mutational effects on protein allostery

New experimental findings continue to challenge our understanding of protein allostery. Recent deep mutational scanning study showed that allosteric hotspots in the tetracycline repressor (TetR) and its homologous transcriptional factors are broadly distributed rather than spanning well-defined structural pathways as often assumed. Moreover, hotspot mutation-induced allostery loss was rescued by distributed additional mutations in a degenerate fashion. Here, we develop a two-domain thermodynamic model for TetR, which readily rationalizes these intriguing observations. The model accurately captures the in vivo activities of various mutants with changes in physically transparent parameters, allowing the data-based quantification of mutational effects using statistical inference. Our analysis reveals the intrinsic connection of intra- and inter-domain properties for allosteric regulation and illustrate epistatic interactions that are consistent with structural features of the protein. The insights gained from this study into the nature of two-domain allostery are expected to have broader implications for other multidomain allosteric proteins.

Toolbox of Characterized Genetic Parts for Staphylococcus aureus

Staphylococcus aureus is an important clinical bacterium prevalent in human-associated microbiomes and the cause of many diseases. However, S. aureus has been intractable to synthetic biology approaches due to limited characterized genetic parts for this nonmodel Gram-positive bacterium. Moreover, genetic manipulation of S. aureus has relied on cumbersome and inefficient cloning strategies. Here, we report the first standardized genetic parts toolbox for S. aureus, which includes characterized promoters, ribosome binding sites, terminators, and plasmid replicons from a variety of bacteria for precise control of gene expression. We established a standard relative expression unit (REU) for S. aureus using a plasmid reference and characterized genetic parts in standardized REUs using S. aureus ATCC 12600. We constructed promoter and terminator part plasmids that are compatible with an efficient Type IIS DNA assembly strategy to effectively build multipart DNA constructs. A library of 24 constitutive promoters was built and characterized in S. aureus, which showed a 380-fold activity range. This promoter library was also assayed in Bacillus subtilis (122-fold activity range) to demonstrate the transferability of the constitutive promoters between these Gram-positive bacteria. By applying an iterative design-build-test-learn cycle, we demonstrated the use of our toolbox for the rational design and engineering of a tetracycline sensor in S. aureus using the PXyl-TetO aTc-inducible promoter that achieved 25.8-fold induction. This toolbox greatly expands the growing number of genetic parts for Gram-positive bacteria and will allow researchers to leverage synthetic biology approaches to study and engineer cellular processes in S. aureus.

Open-endedness in synthetic biology: A route to continual innovation for biological design

Design in synthetic biology is typically goal oriented, aiming to repurpose or optimize existing biological functions, augmenting biology with new-to-nature capabilities, or creating life-like systems from scratch. While the field has seen many advances, bottlenecks in the complexity of the systems built are emerging and designs that function in the lab often fail when used in real-world contexts. Here, we propose an open-ended approach to biological design, with the novelty of designed biology being at least as important as how well it fulfils its goal. Rather than solely focusing on optimization toward a single best design, designing with novelty in mind may allow us to move beyond the diminishing returns we see in performance for most engineered biology. Research from the artificial life community has demonstrated that embracing novelty can automatically generate innovative and unexpected solutions to challenging problems beyond local optima. Synthetic biology offers the ideal playground to explore more creative approaches to biological design.

Encoding Genetic Circuits with DNA Barcodes Paves the Way for High-Throughput Profiling of Dose-Response Curves of Metabolite Biosensors

Metabolite biosensors, through which the intracellular metabolite concentrations could be converted to changes in gene expression, are widely used in a variety of applications according to the different output signals. However, it remains challenging to fine-tune the dose-response relationships of biosensors to meet the needs of various scenarios. On the other hand, the short read length of next-generation sequencing (NGS) has greatly limited the design capability of sequence libraries. To address these issues, we describe a DNA trackable assembly method, coupled with fluorescence-activated cell sorting and NGS (Sort-Seq), to achieve the characterization of dose-response curves in a massively parallel manner. As a proof of the concept, we constructed a malonyl-CoA biosensor library containing 5184 combinations with six levels of transcription factor dosage, four different operator positions, and 216 possible upstream enhancer sequence (UAS) designs in Saccharomyces cerevisiae BY4700. By using Sort-Seq and machine learning approach, we obtained comprehensive dose-response relationships of the combinatorial sequence space. Therefore, our pipeline provides a platform for the design, tuning, and profiling of biosensor response curves and shows great potential to facilitate the rational design of genetic circuits.

Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision

Owing to the nondeterministic and nonlinear nature of gene expression, the steady-state intracellular protein abundance of a clonal population forms a distribution. The characteristics of this distribution, including expression strength and noise, are closely related to cellular behavior. However, quantitative description of these characteristics has so far relied on arrayed methods, which are time-consuming and labor-intensive. To address this issue, we propose a deep-learning-assisted Sort-Seq approach (dSort-Seq) in this work, enabling high-throughput profiling of expression properties with high precision. We demonstrated the validity of dSort-Seq for large-scale assaying of the dose-response relationships of biosensors. In addition, we comprehensively investigated the contribution of transcription and translation to noise production in Escherichia coli, from which we found that the expression noise is strongly coupled with the mean expression level. We also found that the transcriptional interference caused by overlapping RpoD-binding sites contributes to noise production, which suggested the existence of a simple and feasible noise control strategy in E. coli.

Harnessing the power of artificial intelligence to advance cell therapy

Cell therapies are powerful technologies in which human cells are reprogrammed for therapeutic applications such as killing cancer cells or replacing defective cells. The technologies underlying cell therapies are increasing in effectiveness and complexity, making rational engineering of cell therapies more difficult. Creating the next generation of cell therapies will require improved experimental approaches and predictive models. Artificial intelligence (AI) and machine learning (ML) methods have revolutionized several fields in biology including genome annotation, protein structure prediction, and enzyme design. In this review, we discuss the potential of combining experimental library screens and AI to build predictive models for the development of modular cell therapy technologies. Advances in DNA synthesis and high-throughput screening techniques enable the construction and screening of libraries of modular cell therapy constructs. AI and ML models trained on this screening data can accelerate the development of cell therapies by generating predictive models, design rules, and improved designs.

Assessing in vivo the impact of gene context on transcription through DNA supercoiling

Gene context can have significant impact on gene expression but is currently not integrated in quantitative models of gene regulation despite known biophysical principles and quantitative in vitro measurements. Conceptually, the simplest gene context consists of a single gene framed by two topological barriers, known as the twin transcriptional-loop model, which illustrates the interplay between transcription and DNA supercoiling. In vivo, DNA supercoiling is additionally modulated by topoisomerases, whose modus operandi remains to be quantified. Here, we bridge the gap between theory and in vivo properties by realizing in Escherichia coli the twin transcriptional-loop model and by measuring how gene expression varies with promoters and distances to the topological barriers. We find that gene expression depends on the distance to the upstream barrier but not to the downstream barrier, with a promoter-dependent intensity. We rationalize these findings with a first-principle biophysical model of DNA transcription. Our results are explained if TopoI and gyrase both act specifically, respectively upstream and downstream of the gene, with antagonistic effects of TopoI, which can repress initiation while facilitating elongation. Altogether, our work sets the foundations for a systematic and quantitative description of the impact of gene context on gene regulation.

AP profiling resolves co-translational folding pathway and chaperone interactions in vivo

Natural proteins have evolved to fold robustly along specific pathways. Folding begins during synthesis, guided by interactions of the nascent protein with the ribosome and molecular chaperones. However, the timing and progression of co-translational folding remain largely elusive, in part because the process is difficult to measure in the natural environment of the cytosol. We developed a high-throughput method to quantify co-translational folding in live cells that we term Arrest Peptide profiling (AP profiling). We employed AP profiling to delineate co-translational folding for a set of GTPase domains with very similar structures, defining how topology shapes folding pathways. Genetic ablation of major nascent chain-binding chaperones resulted in localized folding changes that suggest how functional redundancies among chaperones are achieved by distinct interactions with the nascent protein. Collectively, our studies provide a window into cellular folding pathways of complex proteins and pave the way for systematic studies on nascent protein folding at unprecedented resolution and throughput.

Retron-mediated multiplex genome editing and continuous evolution in Escherichia coli

While there are several genome editing techniques available, few are suitable for dynamic and simultaneous mutagenesis of arbitrary targeted sequences in prokaryotes. Here, to address these limitations, we present a versatile and multiplex retron-mediated genome editing system (REGES). First, through systematic optimization of REGES, we achieve efficiency of ∼100%, 85 ± 3%, 69 ± 14% and 25 ± 14% for single-, double-, triple- and quadruple-locus genome editing, respectively. In addition, we employ REGES to generate pooled and barcoded variant libraries with degenerate RBS sequences to fine-tune the expression level of endogenous and exogenous genes, such as transcriptional factors to improve ethanol tolerance and biotin biosynthesis. Finally, we demonstrate REGES-mediated continuous in vivo protein evolution, by combining retron, polymerase-mediated base editing and error-prone transcription. By these case studies, we demonstrate REGES as a powerful multiplex genome editing and continuous evolution tool with broad applications in synthetic biology and metabolic engineering.

oFlowSeq: a quantitative approach to identify protein coding mutations affecting cell type enrichment using mosaic CRISPR-Cas9 edited cerebral organoids

Cerebral organoids are comprised of diverse cell types found in the developing human brain, and can be leveraged in the identification of critical cell types perturbed by genetic risk variants in common, neuropsychiatric disorders. There is great interest in developing high-throughput technologies to associate genetic variants with cell types. Here, we describe a high-throughput, quantitative approach (oFlowSeq) by utilizing CRISPR-Cas9, FACS sorting, and next-generation sequencing. Using oFlowSeq, we found that deleterious mutations in autism-associated gene KCTD13 resulted in increased proportions of Nestin+ cells and decreased proportions of TRA-1-60+ cells within mosaic cerebral organoids. We further identified that a locus-wide CRISPR-Cas9 survey of another 18 genes in the 16p11.2 locus resulted in most genes with > 2% maximum editing efficiencies for short and long indels, suggesting a high feasibility for an unbiased, locus-wide experiment using oFlowSeq. Our approach presents a novel method to identify genotype-to-cell type imbalances in an unbiased, high-throughput, quantitative manner.

Ultra-high throughput mapping of genetic design space

Massively parallel genetic screens have been used to map sequence-to-function relationships for a variety of genetic elements. However, because these approaches only interrogate short sequences, it remains challenging to perform high throughput (HT) assays on constructs containing combinations of sequence elements arranged across multi-kb length scales. Overcoming this barrier could accelerate synthetic biology; by screening diverse gene circuit designs, "composition-to-function" mappings could be created that reveal genetic part composability rules and enable rapid identification of behavior-optimized variants. Here, we introduce CLASSIC, a generalizable genetic screening platform that combines long- and short-read next-generation sequencing (NGS) modalities to quantitatively assess pooled libraries of DNA constructs of arbitrary length. We show that CLASSIC can measure expression profiles of >10 5 drug-inducible gene circuit designs (ranging from 6-9 kb) in a single experiment in human cells. Using statistical inference and machine learning (ML) approaches, we demonstrate that data obtained with CLASSIC enables predictive modeling of an entire circuit design landscape, offering critical insight into underlying design principles. Our work shows that by expanding the throughput and understanding gained with each design-build-test-learn (DBTL) cycle, CLASSIC dramatically augments the pace and scale of synthetic biology and establishes an experimental basis for data-driven design of complex genetic systems.

Modulating Sensitivity of an Erythromycin Biosensor for Precise High-Throughput Screening of Strains with Different Characteristics

Genetically encoded biosensors are powerful tools for product-driven high-throughput screening in synthetic biology and metabolic engineering. However, most biosensors can only properly function in a limited concentration cutoff, and the incompatible performance characteristics of biosensors will lead to false positives or failure in screening. The transcription factor (TF)-based biosensors are usually organized in modular architecture and function in a regulator-depended manner, whose performance properties can be fine-tuned by modifying the expression level of the TF. In this study, we modulated the performance characteristics, including sensitivity and operating range, of an MphR-based erythromycin biosensor by fine-adjusting regulator expression levels via ribosome-binding site (RBS) engineering and obtained a panel of biosensors with varied sensitivities by iterative fluorescence-assisted cell sorting (FACS) in Escherichia coli to accommodate different screening purposes. To exemplify their application potential, two engineered biosensors with 10-fold different sensitivities were employed in the precise high-throughput screening by microfluidic-based fluorescence-activated droplet sorting (FADS) of Saccharopolyspora erythraea mutant libraries with different starting erythromycin productions, and mutants representing as high as 6.8 folds and over 100% of production improvements were obtained starting from the wild-type strain and the high-producing industrial strain, respectively. This work demonstrated a simple strategy to engineer biosensor performance properties, which was significant to stepwise strain engineering and production improvement.

A Suite of Constitutive Promoters for Tuning Gene Expression in Plants

The need for convenient tools to express transgenes over a large dynamic range is pervasive throughout plant synthetic biology; however, current efforts are largely limited by the heavy reliance on a small set of strong promoters, precluding more nuanced and refined engineering endeavors in planta. To address this technical gap, we characterize a suite of constitutive promoters that span a wide range of transcriptional levels and develop a GoldenGate-based plasmid toolkit named PCONS, optimized for versatile cloning and rapid testing of transgene expression at varying strengths. We demonstrate how easy access to a stepwise gradient of expression levels can be used for optimizing synthetic transcriptional systems and the production of small molecules in planta. We also systematically investigate the potential of using PCONS as an internal standard in plant biology experimental design, establishing the best practices for signal normalization in experiments. Although our library has primarily been developed for optimizing expression in N. benthamiana, we demonstrate the translatability of our promoters across distantly related species using a multiplexed reporter assay with barcoded transcripts. Our findings showcase the advantages of the PCONS library as an invaluable toolkit for plant synthetic biology.

Ultradeep characterisation of translational sequence determinants refutes rare-codon hypothesis and unveils quadruplet base pairing of initiator tRNA and transcript

Translation is a key determinant of gene expression and an important biotechnological engineering target. In bacteria, 5'-untranslated region (5'-UTR) and coding sequence (CDS) are well-known mRNA parts controlling translation and thus cellular protein levels. However, the complex interaction of 5'-UTR and CDS has so far only been studied for few sequences leading to non-generalisable and partly contradictory conclusions. Herein, we systematically assess the dynamic translation from over 1.2 million 5'-UTR-CDS pairs in Escherichia coli to investigate their collective effect using a new method for ultradeep sequence-function mapping. This allows us to disentangle and precisely quantify effects of various sequence determinants of translation. We find that 5'-UTR and CDS individually account for 53% and 20% of variance in translation, respectively, and show conclusively that, contrary to a common hypothesis, tRNA abundance does not explain expression changes between CDSs with different synonymous codons. Moreover, the obtained large-scale data provide clear experimental evidence for a base-pairing interaction between initiator tRNA and mRNA beyond the anticodon-codon interaction, an effect that is often masked for individual sequences and therefore inaccessible to low-throughput approaches. Our study highlights the indispensability of ultradeep sequence-function mapping to accurately determine the contribution of parts and phenomena involved in gene regulation.

An optimized electrotransformation protocol for Lactobacillus jensenii

Engineered bacteria are promising candidates for in situ detection and treatment of diseases. The female uro-genital tract presents several pathologies, such as sexually transmitted diseases or genital cancer, that could benefit from such technology. While bacteria from the gut microbiome are increasingly engineered, the use of chassis isolated from the female uro-genital resident flora has been limited. A major hurdle to implement the experimental throughput required for efficient engineering in these non-model bacteria is their low transformability. Here we report an optimized electrotransformation protocol for Lactobacillus jensenii, one the most widespread species across vaginal microflora. Starting from classical conditions, we optimized buffers, electric field parameters, cuvette type and DNA quantity to achieve an 80-fold improvement in transformation efficiency, with up to 3.5·103 CFUs/μg of DNA in L. jensenii ATCC 25258. We also identify several plasmids that are maintained and support reporter gene expression in L. jensenii. Finally, we demonstrate that our protocol provides increased transformability in three independent clinical isolates of L. jensenii. This work will facilitate the genetic engineering of L. jensenii and enable its use for addressing challenges in gynecological healthcare.

Current Metabolic Engineering Strategies for Photosynthetic Bioproduction in Cyanobacteria

Cyanobacteria are photosynthetic microorganisms capable of using solar energy to convert CO2 and H2O into O2 and energy-rich organic compounds, thus enabling sustainable production of a wide range of bio-products. More and more strains of cyanobacteria are identified that show great promise as cell platforms for the generation of bioproducts. However, strain development is still required to optimize their biosynthesis and increase titers for industrial applications. This review describes the most well-known, newest and most promising strains available to the community and gives an overview of current cyanobacterial biotechnology and the latest innovative strategies used for engineering cyanobacteria. We summarize advanced synthetic biology tools for modulating gene expression and their use in metabolic pathway engineering to increase the production of value-added compounds, such as terpenoids, fatty acids and sugars, to provide a go-to source for scientists starting research in cyanobacterial metabolic engineering.

The Influence of the Nucleotide Composition of Genes and Gene Regulatory Elements on the Efficiency of Protein Expression in Escherichia coli

Recombinant proteins expressed in Escherichia coli are widely used in biochemical research and industrial processes. At the same time, achieving higher protein expression levels and correct protein folding still remains the key problem, since optimization of nutrient media, growth conditions, and methods for induction of protein synthesis do not always lead to the desired result. Often, low protein expression is determined by the sequences of the expressed genes and their regulatory regions. The genetic code is degenerated; 18 out of 20 amino acids are encoded by more than one codon. Choosing between synonymous codons in the coding sequence can significantly affect the level of protein expression and protein folding due to the influence of the gene nucleotide composition on the probability of formation of secondary mRNA structures that affect the ribosome binding at the translation initiation phase, as well as the ribosome movement along the mRNA during elongation, which, in turn, influences the mRNA degradation and the folding of the nascent protein. The nucleotide composition of the mRNA untranslated regions, in particular the promoter and Shine-Dalgarno sequences, also affects the efficiency of mRNA transcription, translation, and degradation. In this review, we describe the genetic principles that determine the efficiency of protein production in Escherichia coli.

FACS-Based Sequencing Approach to Evaluate Cell Type to Genotype Associations Using Cerebral Organoids

Recent technological developments have led to widespread applications of large-scale transcriptomics-based sequencing methods to identify genotype-to-cell type associations. Here we describe a fluorescence-activated cell sorting (FACS)-based sequencing method to utilize CRISPR/Cas9 edited mosaic cerebral organoids to identify or validate genotype-to-cell type associations. Our approach is high-throughput and quantitative and uses internal controls to enable comparisons of the results across different antibody markers and experiments.

Design and Analysis of Massively Parallel Reporter Assays Using FORECAST

Machine learning is revolutionizing molecular biology and bioengineering by providing powerful insights and predictions. Massively parallel reporter assays (MPRAs) have emerged as a particularly valuable class of high-throughput technique to support such algorithms. MPRAs enable the simultaneous characterization of thousands or even millions of genetic constructs and provide the large amounts of data needed to train models. However, while the scale of this approach is impressive, the design of effective MPRA experiments is challenging due to the many factors that can be varied and the difficulty in predicting how these will impact the quality and quantity of data obtained. Here, we present a computational tool called FORECAST, which can simulate MPRA experiments based on fluorescence-activated cell sorting and subsequent sequencing (commonly referred to as Flow-seq or Sort-seq experiments), as well as carry out rigorous statistical estimation of construct performance from this type of experimental data. FORECAST can be used to develop workflows to aid the design of MPRA experiments and reanalyze existing MPRA data sets.

Autoinduction Expression Modules for Regulating Gene Expression in Bacillus subtilis

Although quorum sensing (QS) promoters that can autonomously activate gene expression have been identified and engineered in Bacillus subtilis, researchers focus on quantifying individual promoters while ignoring the interaction between other genetic regulatory elements. Here, we constructed the autoinduction expression modules consisting of promoters responsive to QS ComQXPA, ribosome binding sites (RBSs), and terminators. Using superfolder green fluorescent protein (sfGFP) as a reporter gene, three individual element libraries were generated from 945 promoters, 12,000 RBSs, and 425 terminators by random mutation, de novo design, and database mining strategies, respectively. Then, the efficiency of three libraries in regulating gene expression was further enhanced by engineering the core region of each optimal element. After hybridizing the element libraries, the generated expression modules exhibited a 627-fold range in regulating gene expression without significantly affecting the autoinduction initiation. Here, the hybrid modules with broad expression strength may benefit the application of QS-based autoinduction systems in B. subtilis.

Accuracy and data efficiency in deep learning models of protein expression

Synthetic biology often involves engineering microbial strains to express high-value proteins. Thanks to progress in rapid DNA synthesis and sequencing, deep learning has emerged as a promising approach to build sequence-to-expression models for strain optimization. But such models need large and costly training data that create steep entry barriers for many laboratories. Here we study the relation between accuracy and data efficiency in an atlas of machine learning models trained on datasets of varied size and sequence diversity. We show that deep learning can achieve good prediction accuracy with much smaller datasets than previously thought. We demonstrate that controlled sequence diversity leads to substantial gains in data efficiency and employed Explainable AI to show that convolutional neural networks can finely discriminate between input DNA sequences. Our results provide guidelines for designing genotype-phenotype screens that balance cost and quality of training data, thus helping promote the wider adoption of deep learning in the biotechnology sector.

Pooled screening of CAR T cells identifies diverse immune signaling domains for next-generation immunotherapies

Chimeric antigen receptors (CARs) repurpose natural signaling components to retarget T cells to refractory cancers but have shown limited efficacy in persistent, recurrent malignancies. Here, we introduce "CAR Pooling," a multiplexed approach to rapidly identify CAR designs with clinical potential. Forty CARs with signaling domains derived from a range of immune cell lineages were evaluated in pooled assays for their ability to stimulate critical T cell effector functions during repetitive stimulation that mimics long-term tumor antigen exposure. Several domains were identified from the tumor necrosis factor (TNF) receptor family that have been primarily associated with B cells. CD40 enhanced proliferation, whereas B cell-activating factor receptor (BAFF-R) and transmembrane activator and CAML interactor (TACI) promoted cytotoxicity. These functions were enhanced relative to clinical benchmarks after prolonged antigen stimulation, and CAR T cell signaling through these domains fell into distinct states of memory, cytotoxicity, and metabolism. BAFF-R CAR T cells were enriched for a highly cytotoxic transcriptional signature previously associated with positive clinical outcomes. We also observed that replacing the 4-1BB intracellular signaling domain with the BAFF-R signaling domain in a clinically validated B cell maturation antigen (BCMA)-specific CAR resulted in enhanced activity in a xenotransplant model of multiple myeloma. Together, these results show that CAR Pooling is a general approach for rapid exploration of CAR architecture and activity to improve the efficacy of CAR T cell therapies.

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.

Flow-Seq Evaluation of Translation Driven by a Set of Natural Escherichia coli 5'-UTR of Variable Length

Flow-seq is a method that combines fluorescently activated cell sorting and next-generation sequencing to deduce a large amount of data about translation efficiency from a single experiment. Here, we constructed a library of fluorescent protein-based reporters preceded by a set of 648 natural 5'-untranslated regions (5'-UTRs) of Escherichia coli genes. Usually, Flow-seq libraries are constructed using uniform-length sequence elements, in contrast to natural situations, where functional elements are of heterogenous lengths. Here, we demonstrated that a 5'-UTR library of variable length could be created and analyzed with Flow-seq. In line with previous Flow-seq experiments with randomized 5'-UTRs, we observed the influence of an RNA secondary structure and Shine-Dalgarno sequences on translation efficiency; however, the variability of these parameters for natural 5'-UTRs in our library was smaller in comparison with randomized libraries. In line with this, we only observed a 30-fold difference in translation efficiency between the best and worst bins sorted with this factor. The results correlated with those obtained with ribosome profiling.

Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins

A fundamental question in protein science is where allosteric hotspots - residues critical for allosteric signaling - are located, and what properties differentiate them. We carried out deep mutational scanning (DMS) of four homologous bacterial allosteric transcription factors (aTFs) to identify hotspots and built a machine learning model with this data to glean the structural and molecular properties of allosteric hotspots. We found hotspots to be distributed protein-wide rather than being restricted to 'pathways' linking allosteric and active sites as is commonly assumed. Despite structural homology, the location of hotspots was not superimposable across the aTFs. However, common signatures emerged when comparing hotspots coincident with long-range interactions, suggesting that the allosteric mechanism is conserved among the homologs despite differences in molecular details. Machine learning with our large DMS datasets revealed global structural and dynamic properties to be a strong predictor of whether a residue is a hotspot than local and physicochemical properties. Furthermore, a model trained on one protein can predict hotspots in a homolog. In summary, the overall allosteric mechanism is embedded in the structural fold of the aTF family, but the finer, molecular details are sequence-specific.

Flow-Seq Method: Features and Application in Bacterial Translation Studies

The Flow-seq method is based on using reporter construct libraries, where a certain element regulating the gene expression of fluorescent reporter proteins is represented in many thousands of variants. Reporter construct libraries are introduced into cells, sorted according to their fluorescence level, and then subjected to next-generation sequencing. Therefore, it turns out to be possible to identify patterns that determine the expression efficiency, based on tens and hundreds of thousands of reporter constructs in one experiment. This method has become common in evaluating the efficiency of protein synthesis simultaneously by multiple mRNA variants. However, its potential is not confined to this area. In the presented review, a comparative analysis of the Flow-seq method and other alternative approaches used for translation efficiency evaluation of mRNA was carried out; the features of its application and the results obtained by Flow-seq were also considered.