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

Advances in stress-tolerance elements for microbial cell factories

Microorganisms, particularly extremophiles, have evolved multiple adaptation mechanisms to address diverse stress conditions during survival in unique environments. Their responses to environmental coercion decide not only survival in severe conditions but are also an essential factor determining bioproduction performance. The design of robust cell factories should take the balance of their growing and bioproduction into account. Thus, mining and redesigning stress-tolerance elements to optimize the performance of cell factories under various extreme conditions is necessary. Here, we reviewed several stress-tolerance elements, including acid-tolerant elements, saline-alkali-resistant elements, thermotolerant elements, antioxidant elements, and so on, providing potential materials for the construction of cell factories and the development of synthetic biology. Strategies for mining and redesigning stress-tolerance elements were also discussed. Moreover, several applications of stress-tolerance elements were provided, and perspectives and discussions for potential strategies for screening stress-tolerance elements were made.

Energy Aware Technology Mapping of Genetic Logic Circuits

Energy and its dissipation are fundamental to all living systems, including cells. Insufficient abundance of energy carriers─as caused by the additional burden of artificial genetic circuits─shifts a cell's priority to survival, also impairing the functionality of the genetic circuit. Moreover, recent works have shown the importance of energy expenditure in information transmission. Despite living organisms being non-equilibrium systems, non-equilibrium models capable of accounting for energy dissipation and non-equilibrium response curves are not yet employed in genetic design automation (GDA) software. To this end, we introduce Energy Aware Technology Mapping, the automated design of genetic logic circuits with respect to energy efficiency and functionality. The basis for this is an energy aware non-equilibrium steady state model of gene expression, capturing characteristics like energy dissipation─which we link to the entropy production rate─and transcriptional bursting, relevant to eukaryotes as well as prokaryotes. Our evaluation shows that a genetic logic circuit's functional performance and energy efficiency are disjoint optimization goals. For our benchmark, energy efficiency improves by 37.2% on average when comparing to functionally optimized variants. We discover a linear increase in energy expenditure and overall protein expression with the circuit size, where Energy Aware Technology Mapping allows for designing genetic logic circuits with the energetic costs of circuits that are one to two gates smaller. Structural variants improve this further, while results show the Pareto dominance among structures of a single Boolean function. By incorporating energy demand into the design, Energy Aware Technology Mapping enables energy efficiency by design. This extends current GDA tools and complements approaches coping with burden in vivo.

Energy Aware Technology Mapping of Genetic Logic Circuits

Energy and its dissipation are fundamental to all living systems, including cells. Insufficient abundance of energy carriers -as caused by the additional burden of artificial genetic circuits-shifts a cell's priority to survival, also impairing the functionality of the genetic circuit. Moreover, recent works have shown the importance of energy expenditure in information transmission. Despite living organisms being non-equilibrium systems, non-equilibrium models capable of accounting for energy dissipation and non-equilibrium response curves are not yet employed in genetic design automation (GDA) software. To this end, we introduce Energy Aware Technology Mapping, the automated design of genetic logic circuits with respect to energy efficiency and functionality. The basis for this is an energy aware non-equilibrium steady state (NESS) model of gene expression, capturing characteristics like energy dissipation -which we link to the entropy production rate- and transcriptional bursting, relevant to eukaryotes as well as prokaryotes. Our evaluation shows that a genetic logic circuit's functional performance and energy efficiency are disjoint optimization goals. For our benchmark, energy efficiency improves by 37.2% on average when comparing to functionally optimized variants. We discover a linear increase in energy expenditure and overall protein expression with the circuit size, where Energy Aware Technology Mapping allows for designing genetic logic circuits with the energy efficiency of circuits that are one to two gates smaller. Structural variants improve this further, while results show the Pareto dominance among structures of a single Boolean function. By incorporating energy demand into the design, Energy Aware Technology Mapping enables energy efficiency by design. This extends current GDA tools and complements approaches coping with burden in vivo .

TOC Graphic

From straight to curved: A historical perspective of DNA shape

DNA is the macromolecule responsible for storing the genetic information of a cell and it has intrinsic properties such as deformability, stability and curvature. DNA Curvature plays an important role in gene transcription and, consequently, in the subsequent production of proteins, a fundamental process of cells. With recent advances in bioinformatics and theoretical biology, it became possible to analyze and understand the involvement of DNA Curvature as a discriminatory characteristic of gene-promoting regions. These regions act as sites where RNAp (ribonucleic acid-polymerase) binds to initiate transcription. This review aims to describe the formation of Curvature, as well as highlight its importance in predicting promoters. Furthermore, this article provides the potential of DNA Curvature as a distinguishing feature for promoter prediction tools, as well as outlining the calculation procedures that have been described by other researchers. This work may support further studies directed towards the enhancement of promoter prediction software.

Synthetic redesign of Escherichia coli W for faster metabolism of sugarcane molasses

BACKGROUND

Sugarcane molasses, rich in sucrose, glucose, and fructose, offers a promising carbon source for industrial fermentation due to its abundance and low cost. However, challenges arise from the simultaneous utilization of multiple sugars and carbon catabolite repression (CCR). Despite its nutritional content, sucrose metabolism in Escherichia coli, except for W strain, remains poorly understood, hindering its use in microbial fermentation. In this study, E. coli W was engineered to enhance sugar consumption rates and overcome CCR. This was achieved through the integration of a synthetically designed csc operon and the optimization of glucose and fructose co-utilization pathways. These advancements facilitate efficient utilization of sugarcane molasses for the production of 3-hydroxypropionic acid (3-HP), contributing to sustainable biochemical production processes.

RESULTS

In this study, we addressed challenges associated with sugar metabolism in E. coli W, focusing on enhancing sucrose consumption and improving glucose-fructose co-utilization. Through targeted engineering of the sucrose utilization system, we achieved accelerated sucrose consumption rates by modulating the expression of the csc operon components, cscB, cscK, cscA, and cscR. Our findings revealed that monocistronic expression of the csc genes with the deletion of cscR, led to optimal sucrose utilization without significant growth burden. Furthermore, we successfully alleviated fructose catabolite repression by modulating the binding dynamics of FruR with the fructose PTS regulon, enabling near-equivalent co-utilization of glucose and fructose. To validate the industrial applicability of our engineered strain, we pursued 3-HP production from sugarcane molasses. By integrating heterologous genes and optimizing metabolic pathways, we achieved improvements in 3-HP titers compared to previous studies. Additionally, glyceraldehyde-3-phosphate dehydrogenase (gapA) repression aids in carbon flux redistribution, enhancing molasses conversion to 3-HP.

CONCLUSIONS

Despite limitations in sucrose metabolism, the redesigned E. coli W strain, adept at utilizing sugarcane molasses, is a valuable asset for industrial fermentation. Its synthetic csc operon enhances sucrose consumption, while mitigating CCR improves glucose-fructose co-utilization. These enhancements, coupled with repression of gapA, aim to efficiently convert sugarcane molasses into 3-HP, addressing limitations in sucrose and fructose metabolism for industrial applications.

CryptKeeper: a negative design tool for reducing unintentional gene expression in bacteria

Foundational techniques in molecular biology-such as cloning genes, tagging biomolecules for purification or identification, and overexpressing recombinant proteins-rely on introducing non-native or synthetic DNA sequences into organisms. These sequences may be recognized by the transcription and translation machinery in their new context in unintended ways. The cryptic gene expression that sometimes results has been shown to produce genetic instability and mask experimental signals. Computational tools have been developed to predict individual types of gene expression elements, but it can be difficult for researchers to contextualize their collective output. Here, we introduce CryptKeeper, a software pipeline that visualizes predictions of bacterial gene expression signals and estimates the translational burden possible from a DNA sequence. We investigate several published examples where cryptic gene expression in E. coli interfered with experiments. CryptKeeper accurately postdicts unwanted gene expression from both eukaryotic virus infectious clones and individual proteins that led to genetic instability. It also identifies off-target gene expression elements that resulted in truncations that confounded protein purification. Incorporating negative design using CryptKeeper into reverse genetics and synthetic biology workflows can help to mitigate cloning challenges and avoid unexplained failures and complications that arise from unintentional gene expression.

Synonymous codon substitutions modulate transcription and translation of a divergent upstream gene by modulating antisense RNA production

Synonymous codons were originally viewed as interchangeable, with no phenotypic consequences. However, substantial evidence has now demonstrated that synonymous substitutions can perturb a variety of gene expression and protein homeostasis mechanisms, including translational efficiency, translational fidelity, and cotranslational folding of the encoded protein. To date, most studies of synonymous codon-derived perturbations have focused on effects within a single gene. Here, we show that synonymous codon substitutions made far within the coding sequence of Escherichia coli plasmid-encoded chloramphenicol acetyltransferase (cat) can significantly increase expression of the divergent upstream tetracycline resistance gene, tetR. In four out of nine synonymously recoded cat sequences tested, expression of the upstream tetR gene was significantly elevated due to transcription of a long antisense RNA (asRNA) originating from a transcription start site within cat. Surprisingly, transcription of this asRNA readily bypassed the native tet transcriptional repression mechanism. Even more surprisingly, accumulation of the TetR protein correlated with the level of asRNA, rather than total tetR RNA. These effects of synonymous codon substitutions on transcription and translation of a neighboring gene suggest that synonymous codon usage in bacteria may be under selection to both preserve the amino acid sequence of the encoded gene and avoid DNA sequence elements that can significantly perturb expression of neighboring genes. Avoiding such sequences may be especially important in plasmids and prokaryotic genomes, where genes and regulatory elements are often densely packed. Similar considerations may apply to the design of genetic circuits for synthetic biology applications.

Substitution Attacks: A Catalyst to Reframe the DNA Manufacturing Cyberbiosecurity Landscape in the Age of Benchtop Synthesizers

Background

The advent of easy-to-use benchtop DNA synthesizers has ushered in a transformative era in biotechnology, extending the capabilities of DNA synthesis to nonspecialists. However, this revolution in access to this technology exposes several vulnerabilities, notably in the form of substitution attacks. These attacks exploit the intricate interplay between the digital domain of DNA sequences and the physical reality of synthesis instruments, posing substantial threats to biosecurity.

Content

This article delves deeply into the dynamic and multifaceted landscape of cyberbiosecurity, specifically emphasizing a novel attack vector that evades traditional screening algorithms. To achieve this, the article explores algorithmic approaches designed to screen DNA sequences, shedding light on the vulnerabilities exposed by substitution attacks and recontextualizing the cyberbiosecurity actor landscape in the context of the entire DNA manufacturing process.

Summary

The exploration of cyberbiosecurity brings existing vulnerabilities in DNA screening algorithms to light and sets the stage for future research and policy considerations. By emphasizing opportunities for a comprehensive, multipronged approach rooted in end-to-end practical DNA manufacturing, this study provides a foundation for advancing both knowledge and strategies in the realm of cyberbiosecurity.

Recommendations

This article serves as a clarion call for increased vigilance and innovation in navigating the intricate landscape of cyberbiosecurity. Effectively understanding and mitigating substitution attacks is necessary to safeguard the integrity of synthesized genetic material, particularly in the context of the democratization of DNA synthesis technology.

Multivariate modular metabolic engineering and medium optimization for vitamin B12 production by Escherichia coli

Vitamin B12 is a complex compound synthesized by microorganisms. The industrial production of vitamin B12 relies on specific microbial fermentation processes. E. coli has been utilized as a host for the de novo biosynthesis of vitamin B12, incorporating approximately 30 heterologous genes. However, a metabolic imbalance in the intricate pathway significantly limits vitamin B12 production. In this study, we employed multivariate modular metabolic engineering to enhance vitamin B12 production in E. coli by manipulating two modules comprising a total of 10 genes within the vitamin B12 biosynthetic pathway. These two modules were integrated into the chromosome of a chassis cell, regulated by T7, J23119, and J23106 promoters to achieve combinatorial pathway optimization. The highest vitamin B12 titer was attained by engineering the two modules controlled by J23119 and T7 promoters. The inclusion of yeast powder to the fermentation medium increased the vitamin B12 titer to 1.52 mg/L. This enhancement was attributed to the effect of yeast powder on elevating the oxygen transfer rate and augmenting the strain's isopropyl-β-d-1-thiogalactopyranoside (IPTG) tolerance. Ultimately, vitamin B12 titer of 2.89 mg/L was achieved through scaled-up fermentation in a 5-liter fermenter. The strategies reported herein will expedite the development of industry-scale vitamin B12 production utilizing E. coli.

Expanding the synthetic biology repertoire of a fast-growing cyanobacterium Synechococcus elongatus PCC 11801

Synechococcus elongatus PCC 11801 is a fast-growing cyanobacterium, exhibiting high tolerance to environmental stresses. We have earlier characterized its genome and analysed its transcriptome and proteome. However, to deploy it as a potential cell factory, it is necessary to expand its synthetic biology toolbox, including promoter elements and ribosome binding sites (RBSs). Here, based on the global transcriptome analysis, 48 native promoters of the genes with high transcript count were characterized using a fluorescent reporter system. The promoters PcpcB, PpsbA1, and P11770 exhibited consistently high fluorescence under all the cultivation conditions. Similarly, from the genome data and proteome analysis, 534 operons were identified. Fifteen intergenic regions exhibiting higher protein expression from the downstream gene were systematically characterized for identifying RBSs, using an operon construct comprising fluorescent protein genes eyfp and mTurq under PcpcB (PcpcB:eyfp:RBS:mTurq:TrrnB). Overall, the work presents promoter and RBS sequence libraries, with varying strengths, to expedite bioengineering of PCC 11801.

Heterologous Biosynthesis of Methanobactin from Methylocystis sp. Strain SB2 in Methylosinus trichosporium OB3b

Aerobic methanotrophs, or methane-consuming microbes, are strongly dependent on copper for their activity. To satisfy this requirement, some methanotrophs produce a copper-binding compound, or chalkophore, called methanobactin (MB). In addition to playing a critical role in methanotrophy, MB has also been shown to have great promise in treating copper-related human diseases, perhaps most significantly Wilson's disease. In this congenital disorder, copper builds up in the liver, leading to irreversible damage and, in severe cases, complete organ failure. Remarkably, MB has been shown to reverse such damage in animal models, and there is a great deal of interest in upscaling MB production for expanded clinical trials. Such efforts, however, are currently hampered as (1) the natural rate of MB production rate by methanotrophs is low, (2) the use of methane as a substrate for MB production is problematic as it is explosive in air, (3) there is limited understanding of the entire pathway of MB biosynthesis, and (4) the most attractive form of MB is produced by Methylocystis sp. strain SB2, a methanotroph that is genetically intractable. Herein, we report heterologous biosynthesis of MB from Methylocystis sp. strain SB2 in an alternative methanotroph, Methylosinus trichosporium OB3b, not only on methane but also on methanol. As a result, the strategy described herein not only facilitates enhanced MB production but also provides opportunities to construct various mutants to delineate the entire pathway of MB biosynthesis, as well as the creation of modified forms of MB that may have enhanced therapeutic value.

Engineering artificial cross-species promoters with different transcriptional strengths

As a fundamental tool in synthetic biology, promoters are pivotal in regulating gene expression, enabling precise genetic control and spurring innovation across diverse biotechnological applications. However, most advances in engineered genetic systems rely on host-specific regulation of the genetic portion. With the burgeoning diversity of synthetic biology chassis cells, there emerges a pressing necessity to broaden the universal promoter toolkit spectrum, ensuring adaptability across various microbial chassis cells for enhanced applicability and customization in the evolving landscape of synthetic biology. In this study, we analyzed and validated the primary structures of natural endogenous promoters from Escherichia coli, Bacillus subtilis, Corynebacterium glutamicum, Saccharomyces cerevisiae, and Pichia pastoris, and through strategic integration and rational modification of promoter motifs, we developed a series of cross-species promoters (Psh) with transcriptional activity in five strains (prokaryotic and eukaryotic). This series of cross species promoters can significantly expand the synthetic biology promoter toolkit while providing a foundation and inspiration for standardized development of universal components The combinatorial use of key elements from prokaryotic and eukaryotic promoters presented in this study represents a novel strategy that may offer new insights and methods for future advancements in promoter engineering.

Strategies in engineering sustainable biochemical synthesis through microbial systems

Growing environmental concerns and the urgency to address climate change have increased demand for the development of sustainable alternatives to fossil-derived fuels and chemicals. Microbial systems, possessing inherent biosynthetic capabilities, present a promising approach for achieving this goal. This review discusses the coupling of systems and synthetic biology to enable the elucidation and manipulation of microbial phenotypes for the production of chemicals that can substitute for petroleum-derived counterparts and contribute to advancing green biotechnology. The integration of artificial intelligence with metabolic engineering to facilitate precise and data-driven design of biosynthetic pathways is also discussed, along with the identification of current limitations and proposition of strategies for optimizing biosystems, thereby propelling the field of chemical biology towards sustainable chemical production.

Measuring the burden of hundreds of BioBricks defines an evolutionary limit on constructability in synthetic biology

Engineered DNA will slow the growth of a host cell if it redirects limiting resources or otherwise interferes with homeostasis. Escape mutants that alleviate this burden can rapidly evolve and take over cell populations, making genetic engineering less reliable and predictable. Synthetic biologists often use genetic parts encoded on plasmids, but their burden is rarely characterized. We measured how 301 BioBrick plasmids affected Escherichia coli growth and found that 59 (19.6%) were burdensome, primarily because they depleted the limited gene expression resources of host cells. Overall, no BioBricks reduced the growth rate of E. coli by >45%, which agreed with a population genetic model that predicts such plasmids should be unclonable. We made this model available online for education ( https://barricklab.org/burden-model ) and added our burden measurements to the iGEM Registry. Our results establish a fundamental limit on what DNA constructs and genetic modifications can be successfully engineered into cells.

Phage anti-CRISPR control by an RNA- and DNA-binding helix-turn-helix protein

In all organisms, regulation of gene expression must be adjusted to meet cellular requirements and frequently involves helix-turn-helix (HTH) domain proteins1. For instance, in the arms race between bacteria and bacteriophages, rapid expression of phage anti-CRISPR (acr) genes upon infection enables evasion from CRISPR-Cas defence; transcription is then repressed by an HTH-domain-containing anti-CRISPR-associated (Aca) protein, probably to reduce fitness costs from excessive expression2-5. However, how a single HTH regulator adjusts anti-CRISPR production to cope with increasing phage genome copies and accumulating acr mRNA is unknown. Here we show that the HTH domain of the regulator Aca2, in addition to repressing Acr synthesis transcriptionally through DNA binding, inhibits translation of mRNAs by binding conserved RNA stem-loops and blocking ribosome access. The cryo-electron microscopy structure of the approximately 40 kDa Aca2-RNA complex demonstrates how the versatile HTH domain specifically discriminates RNA from DNA binding sites. These combined regulatory modes are widespread in the Aca2 family and facilitate CRISPR-Cas inhibition in the face of rapid phage DNA replication without toxic acr overexpression. Given the ubiquity of HTH-domain-containing proteins, it is anticipated that many more of them elicit regulatory control by dual DNA and RNA binding.

Generating information-dense promoter sequences with optimal string packing

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

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.

Accelerating Genetic Sensor Development, Scale-up, and Deployment Using Synthetic Biology

Living cells are exquisitely tuned to sense and respond to changes in their environment. Repurposing these systems to create engineered biosensors has seen growing interest in the field of synthetic biology and provides a foundation for many innovative applications spanning environmental monitoring to improved biobased production. In this review, we present a detailed overview of currently available biosensors and the methods that have supported their development, scale-up, and deployment. We focus on genetic sensors in living cells whose outputs affect gene expression. We find that emerging high-throughput experimental assays and evolutionary approaches combined with advanced bioinformatics and machine learning are establishing pipelines to produce genetic sensors for virtually any small molecule, protein, or nucleic acid. However, more complex sensing tasks based on classifying compositions of many stimuli and the reliable deployment of these systems into real-world settings remain challenges. We suggest that recent advances in our ability to precisely modify nonmodel organisms and the integration of proven control engineering principles (e.g., feedback) into the broader design of genetic sensing systems will be necessary to overcome these hurdles and realize the immense potential of the field.

Designing Cell-Type-Specific Promoter Sequences Using Conservative Model-Based Optimization

Gene therapies have the potential to treat disease by delivering therapeutic genetic cargo to disease-associated cells. One limitation to their widespread use is the lack of short regulatory sequences, or promoters, that differentially induce the expression of delivered genetic cargo in target cells, minimizing side effects in other cell types. Such cell-type-specific promoters are difficult to discover using existing methods, requiring either manual curation or access to large datasets of promoter-driven expression from both targeted and untargeted cells. Model-based optimization (MBO) has emerged as an effective method to design biological sequences in an automated manner, and has recently been used in promoter design methods. However, these methods have only been tested using large training datasets that are expensive to collect, and focus on designing promoters for markedly different cell types, overlooking the complexities associated with designing promoters for closely related cell types that share similar regulatory features. Therefore, we introduce a comprehensive framework for utilizing MBO to design promoters in a data-efficient manner, with an emphasis on discovering promoters for similar cell types. We use conservative objective models (COMs) for MBO and highlight practical considerations such as best practices for improving sequence diversity, getting estimates of model uncertainty, and choosing the optimal set of sequences for experimental validation. Using three relatively similar blood cancer cell lines (Jurkat, K562, and THP1), we show that our approach discovers many novel cell-type-specific promoters after experimentally validating the designed sequences. For K562 cells, in particular, we discover a promoter that has 75.85% higher cell-type-specificity than the best promoter from the initial dataset used to train our models.

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.

A high-throughput synthetic biology approach for studying combinatorial chromatin-based transcriptional regulation

The construction of synthetic gene circuits requires the rational combination of multiple regulatory components, but predicting their behavior can be challenging due to poorly understood component interactions and unexpected emergent behaviors. In eukaryotes, chromatin regulators (CRs) are essential regulatory components that orchestrate gene expression. Here, we develop a screening platform to investigate the impact of CR pairs on transcriptional activity in yeast. We construct a combinatorial library consisting of over 1,900 CR pairs and use a high-throughput workflow to characterize the impact of CR co-recruitment on gene expression. We recapitulate known interactions and discover several instances of CR pairs with emergent behaviors. We also demonstrate that supervised machine learning models trained with low-dimensional amino acid embeddings accurately predict the impact of CR co-recruitment on transcriptional activity. This work introduces a scalable platform and machine learning approach that can be used to study how networks of regulatory components impact gene expression.

Mutation of the peptide-regulated transcription factor ComR for amidated peptide specificity and heterologous function in Lactiplantibacillus plantarum WCFS1

There is a growing interest in the use of probiotic bacteria as biosensors for the detection of disease. However, there is a lack of bacterial receptors developed for specific disease biomarkers. Here, we have investigated the use of the peptide-regulated transcription factor ComR from Streptococcus spp. for specific peptide biomarker detection. ComR exhibits a number of attractive features that are potentially exploitable to create a biomolecular switch for engineered biosensor circuitry within the probiotic organism Lactiplantibacillus plantarum WCFS1. Through iterative design-build-test cycles, we developed a genomically integrated, ComR-based biosensor circuit that allowed WCFS1 to detect low nanomolar concentrations of ComR's cognate peptide XIP. By screening a library of ComR proteins with mutant residues substituted at the K100 position, we identified mutations that increased the specificity of ComR toward an amidated version of its cognate peptide, demonstrating the potential for ComR to detect this important class of biomarker.IMPORTANCEUsing bacteria to detect disease is an exciting possibility under active study. Detecting extracellular peptides with specific amino acid sequences would be particularly useful as these are important markers of health and disease (biomarkers). In this work, we show that a probiotic bacteria (Lactiplantibacillus plantarum) can be genetically engineered to detect specific extracellular peptides using the protein ComR from Streptococcus bacteria. In its natural form, ComR allowed the probiotic bacteria to detect a specific peptide, XIP. We then modified XIP to be more like the peptide biomarkers found in humans and engineered ComR so that it activated with this modified XIP and not the original XIP. This newly engineered ComR also worked in the probiotic bacteria, as expected. This suggests that with additional engineering, ComR might be able to activate with human peptide biomarkers and be used by genetically engineered probiotic bacteria to better detect disease.

Genome engineering of the human gut microbiome

The human gut microbiome, a complex ecosystem, significantly influences host health, impacting crucial aspects such as metabolism and immunity. To enhance our comprehension and control of the molecular mechanisms orchestrating the intricate interplay between gut commensal bacteria and human health, the exploration of genome engineering for gut microbes is a promising frontier. Nevertheless, the complexities and diversities inherent in the gut microbiome pose substantial challenges to the development of effective genome engineering tools for human gut microbes. In this comprehensive review, we provide an overview of the current progress and challenges in genome engineering of human gut commensal bacteria, whether executed in vitro or in situ. A specific focus is directed towards the advancements and prospects in cargo DNA delivery and high-throughput techniques. Additionally, we elucidate the immense potential of genome engineering methods to enhance our understanding of the human gut microbiome and engineer the microorganisms to enhance human health.

Promoter recruitment drives the emergence of proto-genes in a long-term evolution experiment with Escherichia coli

The phenomenon of de novo gene birth-the emergence of genes from non-genic sequences-has received considerable attention due to the widespread occurrence of genes that are unique to particular species or genomes. Most instances of de novo gene birth have been recognized through comparative analyses of genome sequences in eukaryotes, despite the abundance of novel, lineage-specific genes in bacteria and the relative ease with which bacteria can be studied in an experimental context. Here, we explore the genetic record of the Escherichia coli long-term evolution experiment (LTEE) for changes indicative of "proto-genic" phases of new gene birth in which non-genic sequences evolve stable transcription and/or translation. Over the time span of the LTEE, non-genic regions are frequently transcribed, translated and differentially expressed, with levels of transcription across low-expressed regions increasing in later generations of the experiment. Proto-genes formed downstream of new mutations result either from insertion element activity or chromosomal translocations that fused preexisting regulatory sequences to regions that were not expressed in the LTEE ancestor. Additionally, we identified instances of proto-gene emergence in which a previously unexpressed sequence was transcribed after formation of an upstream promoter, although such cases were rare compared to those caused by recruitment of preexisting promoters. Tracing the origin of the causative mutations, we discovered that most occurred early in the history of the LTEE, often within the first 20,000 generations, and became fixed soon after emergence. Our findings show that proto-genes emerge frequently within evolving populations, can persist stably, and can serve as potential substrates for new gene formation.

Promoter engineering of natural product biosynthetic gene clusters in actinomycetes: concepts and applications

Covering 2011 to 2022Low titers of natural products in laboratory culture or fermentation conditions have been one of the challenging issues in natural products research. Many natural product biosynthetic gene clusters (BGCs) are also transcriptionally silent in laboratory culture conditions, making it challenging to characterize the structures and activities of their metabolites. Promoter engineering offers a potential solution to this problem by providing tools for transcriptional activation or optimization of biosynthetic genes. In this review, we summarize the 10 years of progress in promoter engineering approaches in natural products research focusing on the most metabolically talented group of bacteria actinomycetes.

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.

Identification of promoter activity in gene-less cassettes from Vibrionaceae superintegrons

Integrons are genetic platforms that acquire new genes encoded in integron cassettes (ICs), building arrays of adaptive functions. ICs generally encode promoterless genes, whose expression relies on the platform-associated Pc promoter, with the cassette array functioning as an operon-like structure regulated by the distance to the Pc. This is relevant in large sedentary chromosomal integrons (SCIs) carrying hundreds of ICs, like those in Vibrio species. We selected 29 gene-less cassettes in four Vibrio SCIs, and explored whether their function could be related to the transcription regulation of adjacent ICs. We show that most gene-less cassettes have promoter activity on the sense strand, enhancing the expression of downstream cassettes. Additionally, we identified the transcription start sites of gene-less ICs through 5'-RACE. Accordingly, we found that most of the superintegron in Vibrio cholerae is not silent. These promoter cassettes can trigger the expression of a silent dfrB9 cassette downstream, increasing trimethoprim resistance >512-fold in V. cholerae and Escherichia coli. Furthermore, one cassette with an antisense promoter can reduce trimethoprim resistance when cloned downstream. Our findings highlight the regulatory role of gene-less cassettes in the expression of adjacent cassettes, emphasizing their significance in SCIs and their clinical importance if captured by mobile integrons.

Metabolic flux optimization of iterative pathways through orthogonal gene expression control: Application to the β-oxidation reversal

Balancing relative expression of pathway genes to minimize flux bottlenecks and metabolic burden is one of the key challenges in metabolic engineering. This is especially relevant for iterative pathways, such as reverse β-oxidation (rBOX) pathway, which require control of flux partition at multiple nodes to achieve efficient synthesis of target products. Here, we develop a plasmid-based inducible system for orthogonal control of gene expression (referred to as the TriO system) and demonstrate its utility in the rBOX pathway. Leveraging effortless construction of TriO vectors in a plug-and-play manner, we simultaneously explored the solution space for enzyme choice and relative expression levels. Remarkably, varying individual expression levels led to substantial change in product specificity ranging from no production to optimal performance of about 90% of the theoretical yield of the desired products. We obtained titers of 6.3 g/L butyrate, 2.2 g/L butanol and 4.0 g/L hexanoate from glycerol in E. coli, which exceed the best titers previously reported using equivalent enzyme combinations. Since a similar system behavior was observed with alternative termination routes and higher-order iterations, we envision our approach to be broadly applicable to other iterative pathways besides the rBOX. Considering that high throughput, automated strain construction using combinatorial promoter and RBS libraries remain out of reach for many researchers, especially in academia, tools like the TriO system could democratize the testing and evaluation of pathway designs by reducing cost, time and infrastructure requirements.

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.

Combinatorial optimization of gene expression through recombinase-mediated promoter and terminator shuffling in yeast

Microbes are increasingly employed as cell factories to produce biomolecules. This often involves the expression of complex heterologous biosynthesis pathways in host strains. Achieving maximal product yields and avoiding build-up of (toxic) intermediates requires balanced expression of every pathway gene. However, despite progress in metabolic modeling, the optimization of gene expression still heavily relies on trial-and-error. Here, we report an approach for in vivo, multiplexed Gene Expression Modification by LoxPsym-Cre Recombination (GEMbLeR). GEMbLeR exploits orthogonal LoxPsym sites to independently shuffle promoter and terminator modules at distinct genomic loci. This approach facilitates creation of large strain libraries, in which expression of every pathway gene ranges over 120-fold and each strain harbors a unique expression profile. When applied to the biosynthetic pathway of astaxanthin, an industrially relevant antioxidant, a single round of GEMbLeR improved pathway flux and doubled production titers. Together, this shows that GEMbLeR allows rapid and efficient gene expression optimization in heterologous biosynthetic pathways, offering possibilities for enhancing the performance of microbial cell factories.

Generating information-dense promoter sequences with optimal string packing

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

Author Summary

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

The principle of uncertainty in biology: Will machine learning/artificial intelligence lead to the end of mechanistic studies?

Molecular Biology has long tried to discover mechanisms, considering that unless we understand the principles, we cannot develop applications. Now machine learning and artificial intelligence enable direct leaps to application without understanding the principles. Will this herald a decline in mechanistic studies?

A genetic circuit on a single DNA molecule as an autonomous dissipative nanodevice

Realizing genetic circuits on single DNA molecules as self-encoded dissipative nanodevices is a major step toward miniaturization of autonomous biological systems. A circuit operating on a single DNA implies that genetically encoded proteins localize during coupled transcription-translation to DNA, but a single-molecule measurement demonstrating this has remained a challenge. Here, we use a genetically encoded fluorescent reporter system with improved temporal resolution and observe the synthesis of individual proteins tethered to a DNA molecule by transient complexes of RNA polymerase, messenger RNA, and ribosome. Against expectations in dilute cell-free conditions where equilibrium considerations favor dispersion, these nascent proteins linger long enough to regulate cascaded reactions on the same DNA. We rationally design a pulsatile genetic circuit by encoding an activator and repressor in feedback on the same DNA molecule. Driven by the local synthesis of only several proteins per hour and gene, the circuit dynamics exhibit enhanced variability between individual DNA molecules, and fluctuations with a broad power spectrum. Our results demonstrate that co-expressional localization, as a nonequilibrium process, facilitates single-DNA genetic circuits as dissipative nanodevices, with implications for nanobiotechnology applications and artificial cell design.

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.

Improving Cell-Free Expression of Model Membrane Proteins by Tuning Ribosome Cotranslational Membrane Association and Nascent Chain Aggregation

Cell-free gene expression (CFE) systems are powerful tools for transcribing and translating genes outside of a living cell. Synthesis of membrane proteins is of particular interest, but their yield in CFE is substantially lower than that for soluble proteins. In this paper, we study the CFE of membrane proteins and develop a quantitative kinetic model. We identify that ribosome stalling during the translation of membrane proteins is a strong predictor of membrane protein synthesis due to aggregation between the ribosome nascent chains. Synthesis can be improved by the addition of lipid membranes, which incorporate protein nascent chains and, therefore, kinetically compete with aggregation. We show that the balance between peptide-membrane association and peptide aggregation rates determines the yield of the synthesized membrane protein. We define a membrane protein expression score that can be used to rationalize the engineering of lipid composition and the N-terminal domain of a native and computationally designed membrane proteins produced through CFE.

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.

Using design of experiments to guide genetic optimization of engineered metabolic pathways

Design of experiments (DoE) is a term used to describe the application of statistical approaches to interrogate the impact of many variables on the performance of a multivariate system. It is commonly used for process optimization in fields such as chemical engineering and material science. Recent advances in the ability to quantitatively control the expression of genes in biological systems open up the possibility to apply DoE for genetic optimization. In this review targeted to genetic and metabolic engineers, we introduce several approaches in DoE at a high level and describe instances wherein these were applied to interrogate or optimize engineered genetic systems. We discuss the challenges of applying DoE and propose strategies to mitigate these challenges.

ONE-SENTENCE SUMMARY

This is a review of literature related to applying Design of Experiments for genetic optimization.

An Improved Method for Eliminating or Creating Intragenic Bacterial Promoters

Recent advances in genomic refactoring have been hindered by the ever-present complication of internal or cryptic transcriptional regulation. Typical approaches to these features have been to randomize or perform mass alterations to the gene sequences thought to contain the regulatory motifs; however, this approach can cause problems by altering translational speeds, introducing long distance DNA-DNA interaction effects, and inducing RNA toxicity. Previously, we developed a rational design approach named COdon Restrained Promoter SilEncing (CORPSE) which takes externally identified promoter sequences and uses position-specific scoring matrices as proxy promoter strengths to make minimal changes to promoter sequences to disable their activity. Additionally, through inverting our system we were also able to modify weak internal promoters to increase their activity. In this chapter, we augment our previous process with the biophysical model Promoter Calculator v1.0 developed by LaFleur et al. to combine promoter identification and activity prediction, with our algorithm to silently modify promoter sequences, to provide more robust promoter elimination and creation.

Enabling pathway design by multiplex experimentation and machine learning

The remarkable metabolic diversity observed in nature has provided a foundation for sustainable production of a wide array of valuable molecules. However, transferring the biosynthetic pathway to the desired host often runs into inherent failures that arise from intermediate accumulation and reduced flux resulting from competing pathways within the host cell. Moreover, the conventional trial and error methods utilized in pathway optimization struggle to fully grasp the intricacies of installed pathways, leading to time-consuming and labor-intensive experiments, ultimately resulting in suboptimal yields. Considering these obstacles, there is a pressing need to explore the enzyme expression landscape and identify the optimal pathway configuration for enhanced production of molecules. This review delves into recent advancements in pathway engineering, with a focus on multiplex experimentation and machine learning techniques. These approaches play a pivotal role in overcoming the limitations of traditional methods, enabling exploration of a broader design space and increasing the likelihood of discovering optimal pathway configurations for enhanced production of molecules. We discuss several tools and strategies for pathway design, construction, and optimization for sustainable and cost-effective microbial production of molecules ranging from bulk to fine chemicals. We also highlight major successes in academia and industry through compelling case studies.

Fine-Tuning Gene Expression in Bacteria by Synthetic Promoters

Promoters are key genetic elements in the initiation and regulation of gene expression. A limited number of natural promoters has been described for the control of gene expression in synthetic biology applications. Therefore, synthetic promoters have been developed to fine-tune the transcription for the desired amount of gene product. Mostly, synthetic promoters are characterized using promoter libraries that are constructed via mutagenesis of promoter sequences. The strength of promoters in the library is determined according to the expression of a reporter gene such as gfp encoding green fluorescent protein. Gene expression can be controlled using inducers. The majority of the studies on gram-negative bacteria are conducted using the expression system of the model organism Escherichia coli while that of the model organism Bacillus subtilis is mostly used in the studies on gram-positive bacteria. Additionally, synthetic promoters for the cyanobacteria, which are phototrophic microorganisms, are evaluated, especially using the model cyanobacterium Synechocystis sp. PCC 6803. Moreover, a variety of algorithms based on machine learning methods were developed to characterize the features of promoter elements. Some of these in silico models were verified using in vitro or in vivo experiments. Identification of novel synthetic promoters with improved features compared to natural ones contributes much to the synthetic biology approaches in terms of fine-tuning gene expression.

CRISPRpi: Inducing and Curing Prophage Using the CRISPR Interference

We present here a CRISPR-interference-based protocol to trigger prophage induction, even for non-inducible prophages. This method can also be used to cure the prophage from the bacterial host. The method is based on silencing of the phage's repressor transcription, thanks to CRISPR interference. Plasmid electroporation is used to bring the CRISPRi system into the bacteria, specifically on a plasmid carrying spacers targeting the prophage repressor. This method enables prophage induction and curation in a week or two with a high efficiency.

MmoD regulates soluble methane monooxygenase and methanobactin production in Methylosinus trichosporium OB3b

IMPORTANCE

Aerobic methanotrophs play a critical role in the global carbon cycle, particularly in controlling net emissions of methane to the atmosphere. As methane is a much more potent greenhouse gas than carbon dioxide, there is increasing interest in utilizing these microbes to mitigate future climate change by increasing their ability to consume methane. Any such efforts, however, require a detailed understanding of how to manipulate methanotrophic activity. Herein, we show that methanotrophic activity is strongly controlled by MmoD, i.e., MmoD regulates methanotrophy through the post-transcriptional regulation of the soluble methane monooxygenase and controls the ability of methanotrophs to collect copper. Such data are likely to prove quite useful in future strategies to enhance the use of methanotrophs to not only reduce methane emissions but also remove methane from the atmosphere.

Deep Learning-Assisted Design of Novel Promoters in Escherichia coli

Deep learning (DL) approaches have the ability to accurately recognize promoter regions and predict their strength. Here, the potential for controllably designing active Escherichia coli promoter is explored by combining multiple deep learning models. First, "DRSAdesign," which relies on a diffusion model to generate different types of novel promoters is created, followed by predicting whether they are real or fake and strength. Experimental validation showed that 45 out of 50 generated promoters are active with high diversity, but most promoters have relatively low activity. Next, "Ndesign," which relies on generating random sequences carrying functional -35 and -10 motifs of the sigma70 promoter is introduced, and their strength is predicted using the designed DL model. The DL model is trained and validated using 200 and 50 generated promoters, and displays Pearson correlation coefficients of 0.49 and 0.43, respectively. Taking advantage of the DL models developed in this work, possible 6-mers are predicted as key functional motifs of the sigma70 promoter, suggesting that promoter recognition and strength prediction mainly rely on the accommodation of functional motifs. This work provides DL tools to design promoters and assess their functions, paving the way for DL-assisted metabolic engineering.

Design and deep learning of synthetic B-cell-specific promoters

Synthetic biology and deep learning synergistically revolutionize our ability for decoding and recoding DNA regulatory grammar. The B-cell-specific transcriptional regulation is intricate, and unlock the potential of B-cell-specific promoters as synthetic elements is important for B-cell engineering. Here, we designed and pooled synthesized 23 640 B-cell-specific promoters that exhibit larger sequence space, B-cell-specific expression, and enable diverse transcriptional patterns in B-cells. By MPRA (Massively parallel reporter assays), we deciphered the sequence features that regulate promoter transcriptional, including motifs and motif syntax (their combination and distance). Finally, we built and trained a deep learning model capable of predicting the transcriptional strength of the immunoglobulin V gene promoter directly from sequence. Prediction of thousands of promoter variants identified in the global human population shows that polymorphisms in promoters influence the transcription of immunoglobulin V genes, which may contribute to individual differences in adaptive humoral immune responses. Our work helps to decipher the transcription mechanism in immunoglobulin genes and offers thousands of non-similar promoters for B-cell engineering.

Global pathogenomic analysis identifies known and candidate genetic antimicrobial resistance determinants in twelve species

Surveillance programs for managing antimicrobial resistance (AMR) have yielded thousands of genomes suited for data-driven mechanism discovery. We present a workflow integrating pangenomics, gene annotation, and machine learning to identify AMR genes at scale. When applied to 12 species, 27,155 genomes, and 69 drugs, we 1) find AMR gene transfer mostly confined within related species, with 925 genes in multiple species but just eight in multiple phylogenetic classes, 2) demonstrate that discovery-oriented support vector machines outperform contemporary methods at recovering known AMR genes, recovering 263 genes compared to 145 by Pyseer, and 3) identify 142 AMR gene candidates. Validation of two candidates in E. coli BW25113 reveals cases of conditional resistance: ΔcycA confers ciprofloxacin resistance in minimal media with D-serine, and frdD V111D confers ampicillin resistance in the presence of ampC by modifying the overlapping promoter. We expect this approach to be adaptable to other species and phenotypes.

Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals

Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market.

Engineering synthetic phosphorylation signaling networks in human cells

Protein phosphorylation signaling networks play a central role in how cells sense and respond to their environment. Here, we describe the engineering of artificial phosphorylation networks in which "push-pull" motifs-reversible enzymatic phosphorylation cycles consisting of opposing kinase and phosphatase activities-are assembled from modular protein domain parts and then wired together to create synthetic phosphorylation circuits in human cells. We demonstrate that the composability of our design scheme enables model-guided tuning of circuit function and the ability to make diverse network connections; synthetic phosphorylation circuits can be coupled to upstream cell surface receptors to enable fast-timescale sensing of extracellular ligands, while downstream connections can regulate gene expression. We leverage these capabilities to engineer cell-based cytokine controllers that dynamically sense and suppress activated T cells. Our work introduces a generalizable approach for designing and building phosphorylation signaling circuits that enable user-defined sense-and-respond function for diverse biosensing and therapeutic applications.

Clinically relevant antibiotic resistance genes are linked to a limited set of taxa within gut microbiome worldwide

The acquisition of antimicrobial resistance (AR) genes has rendered important pathogens nearly or fully unresponsive to antibiotics. It has been suggested that pathogens acquire AR traits from the gut microbiota, which collectively serve as a global reservoir for AR genes conferring resistance to all classes of antibiotics. However, only a subset of AR genes confers resistance to clinically relevant antibiotics, and, although these AR gene profiles are well-characterized for common pathogens, less is known about their taxonomic associations and transfer potential within diverse members of the gut microbiota. We examined a collection of 14,850 human metagenomes and 1666 environmental metagenomes from 33 countries, in addition to nearly 600,000 isolate genomes, to gain insight into the global prevalence and taxonomic range of clinically relevant AR genes. We find that several of the most concerning AR genes, such as those encoding the cephalosporinase CTX-M and carbapenemases KPC, IMP, NDM, and VIM, remain taxonomically restricted to Proteobacteria. Even cfiA, the most common carbapenemase gene within the human gut microbiome, remains tightly restricted to Bacteroides, despite being found on a mobilizable plasmid. We confirmed these findings in gut microbiome samples from India, Honduras, Pakistan, and Vietnam, using a high-sensitivity single-cell fusion PCR approach. Focusing on a set of genes encoding carbapenemases and cephalosporinases, thus far restricted to Bacteroides species, we find that few mutations are required for efficacy in a different phylum, raising the question of why these genes have not spread more widely. Overall, these data suggest that globally prevalent, clinically relevant AR genes have not yet established themselves across diverse commensal gut microbiota.

Advancing reproducibility can ease the 'hard truths' of synthetic biology

Reproducibility has been identified as an outstanding challenge in science, and the field of synthetic biology is no exception. Meeting this challenge is critical to allow the transformative technological capabilities emerging from this field to reach their full potential to benefit the society. We discuss the current state of reproducibility in synthetic biology and how improvements can address some of the central shortcomings in the field. We argue that the successful adoption of reproducibility as a routine aspect of research and development requires commitment spanning researchers and relevant institutions via education, incentivization and investment in related infrastructure. The urgency of this topic pervades synthetic biology as it strives to advance fundamental insights and unlock new capabilities for safe, secure and scalable applications of biotechnology. Graphical Abstract.

Engineering activatable promoters for scalable and multi-input CRISPRa/i circuits

Dynamic, multi-input gene regulatory networks (GRNs) are ubiquitous in nature. Multilayer CRISPR-based genetic circuits hold great promise for building GRNs akin to those found in naturally occurring biological systems. We develop an approach for creating high-performing activatable promoters that can be assembled into deep, wide, and multi-input CRISPR-activation and -interference (CRISPRa/i) GRNs. By integrating sequence-based design and in vivo screening, we engineer activatable promoters that achieve up to 1,000-fold dynamic range in an Escherichia coli-based cell-free system. These components enable CRISPRa GRNs that are six layers deep and four branches wide. We show the generalizability of the promoter engineering workflow by improving the dynamic range of the light-dependent EL222 optogenetic system from 6-fold to 34-fold. Additionally, high dynamic range promoters enable CRISPRa systems mediated by small molecules and protein-protein interactions. We apply these tools to build input-responsive CRISPRa/i GRNs, including feedback loops, logic gates, multilayer cascades, and dynamic pulse modulators. Our work provides a generalizable approach for the design of high dynamic range activatable promoters and enables classes of gene regulatory functions in cell-free systems.