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Results for “Synthetic Biology”

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bioRxiv2022

Quantitative sequence basis for the E. coli transcriptional regulatory network

The transcriptional regulatory network (TRN) of E. coli consists of thousands of interactions between regulators and DNA sequences. Inherently the DNA sequence is the primary determinant of the TRN; however, it is well established that the presence of a DNA binding motif does not guarantee a functional regulatory protein binding site. Thus, the extent to which the TRN architecture can be predicted by the genome DNA sequence alone remains unclear. Here, we developed machine learning models that predict the TRN structure of E. coli based on genome sequence. Models were constructed successfully (cross-validation AUROC >= 0.8) for 84% (57/68) of valid E. coli regulons identified from top-down analysis of RNA-seq data. We found that: 1) While regulatory motif strength is the most important sequence feature for determining regulon membership, additional features such as DNA shape substantially influence membership; 2) complex regulons involving multiple interacting regulators could be unraveled by machine learning; 3) investigating regulons where initial ML models failed revealed new regulator-specific sequence features that improved model accuracy. Finally, while regulon structure can appear to be variable across estimation methods and strains, we found that strong regulatory sequence features underlie both the genes that appear most consistently in regulons across estimation methods as well as the core regulon genes in the Fur pan-regulon. This work develops a quantitative understanding of the sequence basis of the TRN and suggests a path towards computationally-guided control of transcriptional regulation for synthetic biology applications.

bioinformatics↗

bioRxiv2022

Microcompartment assembly around multicomponent fluid cargoes

This article describes dynamical simulations of the assembly of an icosahedral protein shell around a bicomponent fluid cargo. Our simulations are motivated by bacterial microcompartments, which are protein shells found in bacteria that assemble around a complex of enzymes and other components involved in certain metabolic processes. The simulations demonstrate that the relative interaction strengths among the different cargo species play a key role in determining the amount of each species that is encapsulated, their spatial organization, and the nature of the shell assembly pathways. However, the shell protein-shell protein and shell protein-cargo component interactions that help drive assembly and encapsulation also influence cargo composition within certain parameter regimes. These behaviors are governed by a combination of thermodynamic and kinetic effects. In addition to elucidating how natural microcompartments encapsulate multiple components involved within reaction cascades, these results have implications for efforts in synthetic biology to colocalize alternative sets of molecules within microcompartments to accelerate specific reactions. More broadly, the results suggest that coupling between self-assembly and multicomponent liquid-liquid phase separation may play a role in the organization of the cellular cytoplasm.

biophysics↗

bioRxiv2022

Stepwise remodelling and subcompartment formation in individual vesicles by three ESCRT-III proteins

The endosomal sorting complex required for transport (ESCRT) is a multi-protein complex involved in several membrane remodelling processes. Different approaches have been used to dissect the mechanism by which ESCRT proteins produce scission in the membranes. However, the underlying mechanisms generating the membrane deformations remain poorly understood. In this study, giant unilamellar vesicles (GUVs), microfluidic technology and micropipette aspiration are combined to continuously follow the ESCRT-III-mediated membrane remodelling on the single-vesicle level for the first time. With this approach, we identify different mechanisms by which a minimal set of three ESCRT-III proteins from the phagocytic parasite Entamoeba histolytica reshape the membrane. These proteins modulate the membrane stiffness and spontaneous curvature to regulate the bud size and generate intraluminal vesicles in GUVs even in the absence of ATP. We show that the bud stability depends on the protein concentration and membrane tension. The approach introduced here should open the road to diverse applications in synthetic biology for establishing artificial cells with several membrane compartments.

biophysics↗

bioRxiv2022

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 E. coli. 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. Expression of the reporter construct is also affected by proximity to highly expressed ribosomal RNA operons depending on the relative orientation of transcription despite being insulated by strong transcriptional terminators, in a manner consistent with supercoiling competition. 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 specific rules for optimizing control of synthetic biology constructs.

systems biology↗

bioRxiv2022

Non-ribosomal peptide synthetase domain boundary identification and new motifs discovery based on motif-intermotifs standardized architecture

Non-ribosomal peptide synthetase (NRPS) is a diverse family of biosynthetic enzymes for the assembly of bioactive peptides. Despite advances in microbial sequencing, the lack of a consistent standard for annotating NRPS domains and modules has made data-driven discoveries challenging. To address this, we introduced a standardized architecture for NRPS, by using known conserved motifs to partition typical domains. This motif-and-intermotif standardization allowed for systematic evaluations of sequence properties from a large number of NRPS pathways, resulting in the most comprehensive cross-kingdom C domain subtype classifications to date, as well as the discovery and experimental validation of novel conserved motifs with functional significance. Furthermore, our coevolution analysis revealed important barriers associated with reengineering NRPSs and uncovered the entanglement between phylogeny and substrate specificity in NRPS sequences. Our findings provide a comprehensive and statistically insightful analysis of NRPS sequences, opening avenues for future data-driven discoveries. Author SummaryNRPS, a gigantic enzyme that produces diverse microbial secondary metabolites, provides a rich source for important medical products including antibiotics. Despite the extensive knowledge gained about its structure and the large amount of sequencing data available, the frequent failure of reengineering NRPS in synthetic biology highlights the fact that much is still unknown. In this work, we applied existing knowledge to data mining of NRPS sequences, using well-known conserved motifs to partition NRPS sequences into motif-intermotif architectures. This standardization allows for integrating large amounts of sequences from different sources, providing a comprehensive overview of NRPSs across different kingdoms. Our findings included new C domain subtypes, novel conserved motifs with implication in structural flexibility, and insights into why NRPSs are so difficult to reengineer. To facilitate researchers in related fields, we constructed an online platform "NRPS Motif Finder" for parsing the motif-and-intermotif architecture and C domain subtype classification (http://www.bdainformatics.org/page?type=NRPSMotifFinder). We believe that this knowledge-guided approach not only advances our understanding of NRPSs but also provides a useful methodology for data mining in large-scale biological sequences.

bioinformatics↗

bioRxiv2022

Effect of ambient fluid rheology on oscillatory instabilities in filament-motor systems

Filaments and filament bundles such as microtubules or actin interacting with molecular motors such as dynein or myosin constitute a common motif in biology. Synthetic mimics, examples being artificial muscles and reconstituted active networks, also feature active filaments. A common feature of these filament-motor systems is the emergence of stable oscillations as a collective dynamic response. Here, using a combination of classical linear stability analysis and non-linear numerical solutions, we study the dynamics of a minimal filament-motor system immersed in model viscoelastic fluids. We identify steady states, test the linear stability of these states, derive analytical stability boundaries, and investigate emergent oscillatory solutions and their properties. We show that the interplay between motor activity, aggregate elasticity and fluid viscoelasticity allows for stable oscillations or limit cycles to bifurcate from steady states. For highly viscous Newtonian media, frequencies at onset decay with viscosity as [Formula]. In viscoelastic fluids that have the same viscosity as the Newtonian fluid but additionally allow for temporary energy storage, emergent limit cycles are associated with higher frequencies. The magnitude of the increase in the frequency depends on motor mechanochemistry and the interplay between fluid relaxation time-scales and time-scales associated with motor binding and unbinding. Our results suggest that stability and dynamical response in filamentous active systems may be controlled by tailoring the rheology of the ambient environment.

biophysics↗

bioRxiv2022

Strain design optimization using reinforcement learning

Engineered microbial cells present a sustainable alternative to fossil-based synthesis of chemicals and fuels. Cellular synthesis routes are readily assembled and introduced into microbial strains using state-of-the-art synthetic biology tools. However, the optimization of the strains required to reach industrially feasible production levels is far less efficient. It typically relies on trial-and-error leading into high uncertainty in total duration and cost. New techniques that can cope with the complexity and limited mechanistic knowledge of the cellular regulation are called for guiding the strain optimization. In this paper, we put forward a multi-agent reinforcement learning (MARL) approach that learns from experiments to tune the metabolic enzyme levels so that the production is improved. Our method is model-free and does not assume prior knowledge of the microbes metabolic network or its regulation. The multi-agent approach is well-suited to make use of parallel experiments such as multi-well plates commonly used for screening microbial strains. We demonstrate the methods capabilities using the genome-scale kinetic model of Escherichia coli, k-ecoli457, as a surrogate for an in vivo cell behaviour in cultivation experiments. We investigate the methods performance relevant for practical applicability in strain engineering i.e. the speed of convergence towards the optimum response, noise tolerance, and the statistical stability of the solutions found. We further evaluate the proposed MARL approach in improving L-tryptophan production by yeast Saccharomyces cerevisiae, using publicly available experimental data on the performance of a combinatorial strain library. Overall, our results show that multi-agent reinforcement learning is a promising approach for guiding the strain optimization beyond mechanistic knowledge, with the goal of faster and more reliably obtaining industrially attractive production levels. Author summaryEngineered microbial cells offer a sustainable alternative solution to chemical production from fossil resources. However, to make the chemical production using microbial cells economically feasible, they need to be substantially optimized. Due to the biological complexity, this optimization to reach sufficiently high production is typically a costly trial and error process. This paper presents an Artificial Intelligence (AI) approach to guide this task. Our tool learns a model from previous experiments and uses the model to suggest improvements to the engineering design, until a satisfactory production performance is reached. This paper evaluates the behaviour of the proposed AI method from several angles, including the amount of experiments needed, the tolerance to noise as well as the stability of the proposed designs.

bioinformatics↗

bioRxiv2022

Genetically engineered MRI-trackable extracellular vesicles as SARS-CoV-2 mimetics for mapping ACE2 binding in vivo

The elucidation of viral-receptor interactions and an understanding of virus-spreading mechanisms are of great importance, particularly in the era of pandemic. Indeed, advances in computational chemistry, synthetic biology, and protein engineering have allowed precise prediction and characterization of such interactions. Nevertheless, the hazards of the infectiousness of viruses, their rapid mutagenesis, and the need to study viral-receptor interactions in a complex in vivo setup, call for further developments. Here, we show the development of biocompatible genetically engineered extracellular vesicles (EVs) that display the receptor binding domain (RBD) of SARS-CoV-2 on their surface as coronavirus mimetics (EVsRBD). Loading EVsRBD with iron oxide nanoparticles makes them MRI-visible, and thus, allows mapping of the binding of RBD to ACE2 receptors non-invasively in live subjects. Importantly, the proposed mimetics can be easily modified to display the RBD of SARS-CoV-2mutants, namely Delta and Omicron, allowing rapid screening of newly raised variants of the virus. The proposed platform thus shows relevance and cruciality in the examination of quickly evolving pathogenic viruses in an adjustable, fast, and safe manner.

bioengineering↗

bioRxiv2022

Monitoring promoter activity by RNA editing based reporter

Traditional methods monitoring the promoter activity require the insertion of reporter protein (e.g. fluorescent protein) downstream of a targeted promoter. These approaches suffer from low sensitivity and potential interference to endogenous transcripts especially when the targeted transcript is a noncoding RNA. Here, we develop a mechanistically different reporter system to monitor promoter activity based on ribozyme processed ADAR engaging RNA directed editing (REDDIT). We show that REDDIT can be used to monitor the promoter activity of protein coding, long noncoding RNA, and microRNA (miRNA) genes. Furthermore, REDDIT reporter can also be adapted to use bioluminescence imaging to monitor the promoter activity, which is more suitable for in vivo live imaging. Finally, the reporter sensitivity may be further increased by the circularization of ADAR recruiting RNA. REDDIT provides a powerful platform that is promising in a variety of applications such as monitoring the promoter activity, cell lineage tracing, and processing and manipulating genetic information for synthetic biology applications.

molecular biology↗

bioRxiv2022

Classifying enthalpy-neutral DNA strand displacement cascades: trading off desired and undesired signal propagation

Molecular control circuits embedded within chemical systems to direct molecular events have transformative applications in synthetic biology, medicine, and other fields. However, it is challenging to understand the collective behavior of components due to the combinatorial complexity of possible interactions. Some of the largest engineered molecular systems to date have been constructed from DNA strand displacement reactions, in which signals can be propagated without a net change in base pairs. For linear chains of such enthalpy-neutral displacement reactions, we develop a rigorous framework to reason about interactions between regions that must be complementary. We then analyze desired and undesired properties affecting speed and correctness of such systems, including the spurious release of output (leak) and reversible unproductive binding (toehold occlusion), and experimentally confirm the predictions. Our approach, analogous to the rigorous proofs of algorithm correctness in computer science, can guide engineering of robust and efficient molecular algorithms.

bioengineering↗

bioRxiv2022

Global and gene-specific translational regulation in Escherichia coli across different conditions

How well mRNA transcript levels represent protein abundances has been a controversial issue. Particularly across different environments, correlations between mRNA and protein exhibit remarkable variability from gene to gene. Translational regulation is likely to be one of the key factors contributing to mismatches between mRNA level and protein abundance in bacteria. Here, we quantified genome-wide transcriptome and relative translation efficiency (RTE) under 12 different conditions in Escherichia coli. By quantifying the mRNA-RTE correlation both across genes and across conditions, we uncovered a diversity of gene-specific translational regulations, cooperating with transcriptional regulations, in response to carbon (C), nitrogen (N), and phosphate (P) limitations. Intriguingly, we found that many genes regulating translation are themselves subject to translational regulation, suggesting possible feedbacks. Furthermore, a random forest model suggests that codon usage partially predicts a genes cross-condition variability in translation efficiency; such cross-condition variability tends to be an inherent quality of a gene, independent of the specific nutrient limitations. These findings broaden the understanding of translational regulation under different environments, and provide novel strategies for the control of translation in synthetic biology.

genomics↗

bioRxiv2022

Resource allocation accounts for the large variabilityof rate-yield phenotypes across bacterial strains

Different strains of a microorganism growing in the same environment display a wide variety of growth rates and growth yields. We developed a coarse-grained model to test the hypothesis that different resource allocation strategies, corresponding to different compositions of the proteome, can account for the observed rate-yield variability. The model predictions were verified by means of a database of hundreds of published rate-yield and uptake-secretion phenotypes of Escherichia coli strains grown in standard laboratory conditions. We found a very good quantitative agreement between the range of predicted and observed growth rates, growth yields, and glucose uptake and acetate secretion rates. These results support the hypothesis that resource allocation is a major explanatory factor of the observed variability of growth rates and growth yields across different bacterial strains. An interesting prediction of our model, supported by the experimental data, is that high growth rates are not necessarily accompanied by low growth yields. The resource allocation strategies enabling high-rate, high-yield growth of E. coli lead to a higher saturation of enzymes and ribosomes, and thus to a more efficient utilization of proteomic resources. Our model thus contributes to a fundamental understanding of the quantitative relationship between rate and yield in E. coli and other microorganisms. It may also be useful for the rapid screening of strains in metabolic engineering and synthetic biology.

systems biology↗

bioRxiv2022

A single helix repression domain is functional across eukaryotes

The corepressor TOPLESS (TPL) and its paralogs coordinately regulate a large number of genes critical to plant development and immunity. As in many members of the larger pan-eukaryotic Tup1/TLE/Groucho corepressor family, TPL contains a Lis1 Homology domain (LisH), whose function is not well understood. We have previously found that the LisH in TPL--and specifically the N-terminal 18 amino acid alpha-helical region (TPL-H1) --can act as an autonomous repression domain. We hypothesized that homologous domains across diverse LisH-containing proteins could share the same function. To test that hypothesis, we built a library of H1s that broadly sampled the sequence and evolutionary space of LisH domains, and tested their activity in a synthetic transcriptional repression assay in Saccharomyces cerevisiae. Using this approach, we found that repression activity was highly conserved and likely the ancestral function of this motif. We also identified key residues that contribute to repressive function. We leveraged this new knowledge for two applications. First, we tested the role of mutations found in somatic cancers on repression function in two human LisH-containing proteins. Second, we validated function of many of our repression domains in plants, confirming that these sequences should be of use to synthetic biology applications across eukaryotes.

plant biology↗

bioRxiv2022

RNA sequence to structure analysis from comprehensive pairwise mutagenesis of multiple self-cleaving ribozymes

Self-cleaving ribozymes are RNA molecules that catalyze the cleavage of their own phosphodiester backbones. These ribozymes are found in all domains of life and are also a tool for biotechnical and synthetic biology applications. Self-cleaving ribozymes are also an important model of sequence to function relationships for RNA because their small size simplifies synthesis of genetic variants and self-cleaving activity is an accessible readout of the functional consequence of the mutation. Here we used a high-throughput experimental approach to determine the relative activity for every possible single and double mutant of five self-cleaving ribozymes. From this data, we comprehensively identified non-additive effects between pairs of mutations (epistasis) for all five ribozymes. We analyzed how changes in activity and trends in epistasis map to the ribozyme structures. The variety of structures studied provided opportunities to observe several examples of common structural elements, and the data was collected under identical experimental conditions to enable direct comparison. Heat-map based visualization of the data revealed patterns indicating structural features of the ribozymes including paired regions, unpaired loops, non-canonical structures and tertiary structural contacts. The data also revealed signatures of functionally critical nucleotides involved in catalysis. The results demonstrate that the data sets provide structural information similar to chemical or enzymatic probing experiments, but with additional quantitative functional information. The large-scale data sets can be used for models predicting structure and function and for efforts to engineer self-cleaving ribozymes.

molecular biology↗

bioRxiv2022

Molecular Mechanism of Substrate Transport and Dynamics of the Cyanobacterial Bicarbonate Transporter BicA

Cyanobacteria are responsible for up to 80% of aquatic carbon dioxide fixation and have evolved specialized carbon concentrating mechanism to increase photosynthetic yield. As such, cyanobacteria are attractive targets for synthetic biology and engineering approaches to address the demands of global energy security, food production, and climate change for an increasing worlds population. The bicarbonate transporter BicA is a sodium-dependent, low-affinity, high-flux bicarbonate symporter expressed in the plasma membrane of cyanobacteria. Despite extensive biochemical characterization of BicA, including the resolution of the BicA crystal structure, the dynamic understanding of the bicarbonate transport mechanism remains elusive. To this end, we have collected over 1 ms of all-atom molecular dynamics simulation data of the BicA dimer to elucidate the structural rearrangements involved in the substrate transport process. We further characterized the energetics of the cooperativity between BicA protomers and investigated potential mutations that are shown to decrease the free energy barrier of conformational transitions. In all, our study illuminates a detailed mechanistic understanding of the conformational dynamics of bicarbonate transporters and provide atomistic insights to engineering these transporters for enhanced photosynthetic production.

biophysics↗

bioRxiv2022

Multiple ParA/MinD ATPases coordinate the positioning of disparate cargos in a bacterial cell

In eukaryotes, linear motor proteins govern intracellular transport and organization. In bacteria, where linear motors are absent, the ParA/MinD (A/D) family of ATPases spatially organize an array of genetic- and protein-based cellular cargos. ParA is well known to segregate plasmids and chromosomes, as is MinD for its role in divisome positioning. Less studied is the growing list of ParA/MinD-like ATPases found across prokaryotes and involved in the spatial organization of diverse protein-based organelles, such as Bacterial Microcompartments (BMCs), flagella, chemotaxis clusters, and conjugation machinery. Given the fundamental nature of these processes in both cell survival and pathogenesis, the positioning of these cargos has been independently investigated to varying degrees in several organisms. However, it remains unknown whether multiple A/D ATPases can coexist and coordinate the positioning of such a diverse set of fundamental cargos in the same cell. If so, what are the mechanistic commonalities, variation, and specificity determinants that govern the positioning reaction for each cargo? Here, we find that over a third of sequenced bacteria encode multiple A/D ATPases. Among these bacteria, we identified several human pathogens as well as the experimentally tractable organism, Halothiobacillus neapolitanus, which encodes seven A/D ATPases. We directly demonstrate that five of these A/D ATPases are each dedicated to the spatial regulation of a single cellular cargo: the chromosome, the divisome, the carboxysome BMC, the flagellum, and the chemotaxis cluster. We identify putative specificity determinants that allow each A/D ATPase to position its respective cargo. Finally, we show how the deletion of one A/D ATPase can have indirect effects on the inheritance of a cargo actively positioned by another A/D ATPase, stressing the importance of understanding how organelle trafficking, chromosome segregation, and cell division are coordinated in bacterial cells. Together, our data show how multiple A/D ATPases coexist and function to position a diverse set of fundamental cargos in the same bacterial cell. With this knowledge, we anticipate the design of minimal autonomous positioning systems for natural- and synthetic-cargos in bacteria for synthetic biology and biomedical applications.

microbiology↗

bioRxiv2022

Precision Discovery of Novel Inhibitors of Human Cancer Target HsMetAP1 from Vast Unexplored Metagenomic Diversity

Microbial natural products have long been a rich source of human therapeutics. While the chemical diversity encoded in the genomes of microbes is large, this modality has waned as fermentation-based discovery methods have suffered from rediscovery, inefficient scaling, and incompatibility with target-based discovery paradigms. Here, we leverage a metagenomic partitioning strategy to sequence soil microbiomes at unprecedented depth and quality. We then couple these data with target-focused, in silico search strategies and synthetic biology to discover multiple novel natural product inhibitors of human methionine aminopeptidase-1 (HsMetAP1), a validated oncology target. For one of these, metapeptin B, we demonstrate sub-micromolar potency, strong selectivity for HsMetAP1 over HsMetAP2 and elucidate structure-activity relationships. Our approach overcomes challenges of traditional natural product methods, accesses vast, untapped chemical diversity in uncultured microbes, and demonstrates computationally-enabled precision mining of modulators of human proteins.

microbiology↗

bioRxiv2022

A library of promoter-gfp fusion reporters for studying systemic expression pattern of cyclic-di-GMP metabolism-related genes in Pseudomonas aeruginosa

The opportunistic pathogen Pseudomonas aeruginosa is an environmental microorganism, which is notorious for its resistance or tolerance to antibiotics due to the formation of biofilms. Cyclic diguanosine monophosphate (c-di-GMP) is a bacterial second messenger that plays critical roles in biofilm formation. P. aeruginosa contains 41 genes that encode enzymes to participate in the metabolism of c-di-GMP (biosynthesis or degradation), yet it lacks tools to investigate the systemic expression pattern of those genes. Here, we constructed a promoter-gfp transcriptional fusion reporters library that consists of 41 reporter plasmids. Each plasmid contains a promoter of corresponding c-di-GMP metabolism-related (CMR) genes from P. aeruginosa PAO1 strain, thus each promoter-Gfp fusion reporter can be used to detect the promotor activity as well as the transcription of corresponding gene. The promoters activity was tested in P. aeruginosa and Escherichia coli respectively. Among the 41 genes, the promoter of 26 genes showed activity in both P. aeruginosa and E. coli. The library was applied to determine the influence of different temperatures, growth media, and sub-inhibitory concentrations of antibiotics on transcriptional profile of the 41 CMR genes in P. aeruginosa. The results showed different growth conditions did impact different genes transcription, while the promoter activity of a few genes kept at the same level under several different growth conditions. In summary, we provided a promoter-gfp fusion reporters library for systemic monitoring or study of the regulation of CMR genes in P. aeruginosa and the functional promoters can also be used as a bio-brick for synthetic biology studies. ImportanceThe opportunistic pathogen P. aeruginosa can cause acute and chronic infections in humans and it is one of main pathogens in nosocomial infections. Biofilm formation is one of most important causes for P. aeruginosa to persist in hosts and evade immune and antibiotic attacks. c-di-GMP is an important second messenger to control biofilm formation. In P. aeruginosa, there are 41 genes that are predicted to participate in the making and breaking this dinucleotide. A major missing information in this field is the systemic expression profile of those genes in response to changing environment. Toward this goal, we constructed a promoter-gfp transcriptional fusion reporters library that consists of 41 reporter plasmids, each of which contains a promoter of corresponding c-di-GMP metabolism-related genes in P. aeruginosa. This library provides a helpful tool to understand the complex regulation network related to c-di-GMP and to discover potential therapeutic targets.

microbiology↗