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Results for “Synthetic Biology”

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bioRxiv2023

A bacterial tungsten-containing aldehyde oxidoreductase forms an enzymatic decorated protein nanowire

Aldehyde oxidoreductases (AOR) are tungsten enzymes catalysing the oxidation of many different aldehydes to the corresponding carboxylic acids. In contrast to other known AORs, the enzyme from the denitrifying betaproteobacterium Aromatoleum aromaticum (AORAa) consists of three different subunits (AorABC) and utilizes NAD as electron acceptor. Here we reveal that the enzyme forms filaments of repeating AorAB protomers which are capped by a single NAD-binding AorC subunit, based on solving its structure via cryo-electron microscopy. The polyferredoxin-like subunit AorA oligomerizes to an electron-conducting nanowire that is decorated with enzymatically active and W-cofactor (W-co) containing AorB subunits. Our structure further reveals the binding mode of the native substrate benzoate in the AorB active site. This, together with QM:MM-based modelling for the coordination of the W-co, enables formulation of catalytic mechanism hypothesis that paves the way for further engineering of AOR for applications in synthetic biology and biotechnology.

biochemistry↗

bioRxiv2023

Learning to Generate 5' UTR Sequences for Optimized Ribosome Load and Gene Expression

The 5 untranslated region (5 UTR) of mRNA is crucial for the molecules translatability and stability, making it essential for designing synthetic biological circuits for high and stable protein expression. Several UTR sequences are patented and widely used in laboratories. This paper presents UTRGAN, a Generative Adversarial Network (GAN)-based model for generating 5 UTR sequences, coupled with an optimization procedure to ensure high expression for target gene sequences or high ribosome load and translation efficiency. The model generates sequences mimicking various properties of natural UTR sequences and optimizes them to achieve (i) up to 5-fold higher average expression on target genes, (ii) up to 2-fold higher mean ribosome load, and (iii) a 34-fold higher average translation efficiency compared to initial UTR sequences. UTRGAN-generated sequences also exhibit higher similarity to known regulatory motifs in regions such as internal ribosome entry sites, upstream open reading frames, G-quadruplexes, and Kozak and initiation start codon regions. In-vitro experiments show that the UTR sequences designed by UTRGAN result in a higher translation rate for the human TNF- protein compared to the human Beta Globin 5 UTR, a UTR with high production capacity.

bioinformatics↗

bioRxiv2023

Tunable biomimetic bacterial membranes from binary and ternary lipid mixtures and their application in antimicrobial testing

Reconstruction of accurate yet simplified mimetic models of cell membranes is a very challenging goal of synthetic biology. To date, most of the research focuses on the development of eukaryotic cell membranes, while reconstitution of their prokaryotic counterparts has not been fully addressed, and the proposed models do not reflect well the complexity of bacterial cell envelopes. Here, we describe the reconstitution of biomimetic bacterial membranes with an increasing level of complexity, developed from binary and ternary lipid mixtures. Giant unilamellar vesicles composed of phosphatidylcholine (PC) and phosphatidylethanolamine (PE); PC and phosphatidylglycerol (PG); PE and PG; PE, PG and cardiolipin (CA) at varying molar ratios were successfully prepared by the electroformation method. Each of the proposed mimetic models focuses on reproducing specific membrane features such as membrane charge, curvature, leaflets asymmetry, or the presence of phase separation. GUVs were characterized in terms of size distribution, surface charge, and lateral organization. Finally, the developed models were tested against the lipopeptide antibiotic daptomycin. The obtained results showed a clear dependency of daptomycin binding efficiency on the amount of negatively charged lipid species present in the membrane. We anticipate that the models proposed here can be applied not only in antimicrobial testing but also serve as platforms for studying fundamental biological processes in bacteria as well as their interaction with physiologically relevant biomolecules.

biophysics↗

bioRxiv2023

NetSyn: genomic context exploration of protein families

Background: The growing availability of large prokaryotic genomic datasets presents an opportunity to discover new metabolic pathways and enzymatic reactions useful for industrial or synthetic biological applications. Efforts to identify new enzyme functions in this vast number of sequences cannot be achieved without bioinformatics tools and the development of new strategies. Standard methods for assigning a biological function to a gene are based on sequence similarity. However, complementary approaches rely on mine databases to identify conserved gene clusters (i.e. syntenies). In prokaryotic genomes, genes involved in the same pathway are frequently encoded in a single locus with an operonic organisation. This genomic context conservation is considered as a reliable indicator of functional relationships, and is therefore a promising approach for improving the gene function prediction. Methods. Here we present NetSyn (Network Synteny), a tool to group protein sequences based on the conservation of their genomic context rather than solely on sequence similarity. From a list of protein sequence identifiers, NetSyn searches corresponding genome entries to retrieve neighboring genes. Corresponding protein sequences are grouped into families to define homology relationships and compute a synteny conservation score between the different extracted genomic contexts. A network is then created in which the nodes represent the input proteins and the edges indicate that two proteins share a conserved synteny. Finally, the network is partitioned into clusters grouping proteins with similar genomic contexts, using a community detection algorithm. Results. As a proof of concept, we used NetSyn on two different datasets. The first one is the BKACE protein family (formerly named DUF849) which has previously been divided into isofunctional sub-families. NetSyn was able to go a step further by providing additional sub-families beyond those already described. The second dataset corresponds to a set of non-homologous proteins belonging to three different glycoside hydrolase (GH) families. These GHs are known to work cooperatively in a Polysaccharide-Utilization Loci (PUL) and are therefore grouped together in the same genomic contexts. NetSyn was able to identify a locus grouping 3 GHs, involved in the degradation of xyloglucan, in 162 prokaryotic genomes. Discussion. By highlighting conserved synteny in distantly related prokaryotic species, NetSyn enables functional links between proteins to be established beyond sequence similarity alone. We showed that NetSyn is efficient for exploring large prokaryotic protein families, enabling the definition of isofunctional groups and the identification of functional interactions between non-homologous enzymes. These features enable the prediction of new genomic structures that have not yet been experimentally characterized. Finally, NetSyn is also useful for pinpointing annotation errors that have been propagated across databases, and for suggesting annotations on proteins lacking functional prediction. NetSyn is freely available at https://github.com/labgem/netsyn.

bioinformatics↗

bioRxiv2023

GenBank genomics highlight the genomic features, genetic diversity and regulation of morphological, metabolic and disease-resistance traits in Nicotiana tabacum

Nicotiana tabacum is a model organism in plant molecular and pathogenic research and has significant potential in the production of biofuels and active pharmaceutical compounds in synthetic biology. Because of the large allotetraploid genome of tobacco, its genomic features, genetic diversity and genetic regulation of many complex traits remain unknown. In this study, we present a nearly complete chromosome-scale assembly of N. tabacum and provide evidence that homoeologous exchange between subgenomes and epigenetic remodelling are likely mechanisms of genome stabilization and subgenome coordination following polyploidization. By leveraging GenBank-scale sequencing and phenotyping data from 5196 lines, geography at the continent scale, rather than types assigned on the basis of curing crop practices, was found to be the most important correlate of genetic structure. Using 178 marker{square}trait associations detected in genome-wide association analysis, a reference genotype-to-phenotype map was built for 39 morphological, developmental, and disease-resistance traits. A novel gene, auxin response factor 9 (Arf9), associated with wider leaves after being knocked out, was fine-mapped to a single nucleotide polymorphism (SNP). This point mutation alters the translated amino acid from Ala203 to Pro203, likely preventing homodimer formation during DNA binding. Our analysis also revealed signatures of positive and polygenic selection for multiple traits during the process of selective breeding. Overall, this study demonstrated the power of leveraging GenBank genomics to gain insights into the genomic features, genetic diversity, and regulation of complex traits in N. tabacum, laying a foundation for future research on plant functional genomics, crop breeding, and the production of biopharmaceuticals and biofuels.

genetics↗

bioRxiv2023

Clots reveal anomalous elastic behavior of fiber networks

The mechanical properties of many soft natural and synthetic biological materials are relevant to their function. The emergence of these properties from the collective response of the structural components of the material to external stress as well as to intrinsic cell traction, remains poorly understood. Here, we examine the nonlinear elastic behavior of blood clots by combining microscopy and rheological measurements with an elastic network model that accounts for the stretching, bending, and buckling of constituent fibrin fibers. We show that the inhibition of fibrin crosslinking reduces fiber bending stiffness and introduces an atypical fiber buckling-induced softening regime at intermediate shear, before the well-characterized stiffening regime. We also show that crosslinking and platelet contraction significantly alter force propagation in the network in a strain-dependent manner. Our mechanics-based model, supported by experiments, provides a framework to understand the origins of characteristic and anomalous regimes of non-linear elastic response not only in blood clots, but also more generally in active biopolymer networks.

biophysics↗

bioRxiv2023

Fitness cost associated with cell phenotypic switching drives population diversification dynamics and controllability

Isogenic cell populations can cope with stress conditions by switching to alternative phenotypes. Even if it can lead to increased fitness in a natural context, this feature is typically unwanted for a range of applications (e.g., bioproduction, synthetic biology, biomedicine...) where it tends to decrease the controllability of the cellular response. However, little is known about the diversification profiles that can be adopted by a cell population. We characterized the diversification dynamics for various systems (bacteria and yeast) and for different phenotypes (utilization of alternative carbon sources, general stress response and more complex development patterns). Interestingly, our results suggest that the diversification dynamics and the fitness cost associated with cell switching are coupled. For quantifying the contribution of the switching cost on population dynamics, we built a stochastic model that allowed us to reproduce the dynamics observed experimentally and identified three diversification regimes, i.e., constrained (at low switching cost), dispersed (at medium and high switching cost), and bursty (for very high switching cost). Furthermore, we used a cell-machine interface that we call the Segregostat to demonstrate that different levels of control can be applied to these diversification regimes, enabling applications involving more precise cellular responses.

microbiology↗

bioRxiv2023

Deep flanking sequence engineering for efficient promoter design

Human experts are good at summarizing explicit strong patterns from small samples, while deep learning models can learn implicit weak patterns from big data. Biologists have traditionally described the sequence patterns of promoters via transcription factor binding sites (TFBSs), while the flanking sequences among TFBSs, which can also significantly influence promoter activity and function, remain largely uncharacterized. Thus, current synthetic promoters are mainly designed by the manipulation of TFBSs, while the flanking sequence is often chosen arbitrarily or by previous experience due to a lack of well-summarized optimization criteria. Here, we introduced an AI-aided promoter design framework, DeepSEED, that employs expert knowledge and deep learning methods to efficiently design synthetic promoters that have various desirable functions. DeepSEED incorporates the user-defined cis-regulatory sequences as seeds and generates flanking sequences that match the seeds. We showed that DeepSEED can automatically capture k-mer frequencies and DNA shape features from active promoters in the training set and efficiently optimize the flanking sequences to better match desired properties in synthetic promoters. We validated the effectiveness of this framework for diverse synthetic promoter design tasks in both prokaryotic and eukaryotic cells. DeepSEED successfully designed E. coli constitutive, isopropyl-beta-D-1-thiogalactopyranoside (IPTG)-inducible, and mammalian cell doxycycline (Dox)-inducible promoters with significant performance improvements, suggesting that DeepSEED has potential as an efficient AI-aided flanking sequence optimization approach for promoter design that may be of great benefit in synthetic biology applications. Availabilityhttps://github.com/WangLabTHU/deepseed

bioengineering↗

bioRxiv2023

SeqImprove: Machine Learning Assisted Creation of Machine Readable Sequence Information

The progress and utility of synthetic biology is currently hindered by the lengthy process of studying literature and replicating poorly documented work. Reconstruction of crucial design information through post-hoc curation is highly noisy and error-prone. To combat this, author participation during the curation process is crucial. To encour-age author participation without overburdening them, an ML-assisted curation tool called SeqImprove has been developed. Using named entity recognition, named entity normalization, and sequence matching, SeqImprove creates machine-readable sequence data and metadata annotations, which authors can then review and edit before sub-mitting a final sequence file. SeqImprove makes it easier for authors to submit FAIR sequence data that is findable, accessible, interoperable, and reusable.

bioinformatics↗

bioRxiv2023

Time-resolved, deuterium-based fluxomics uncovers the hierarchy and dynamics of sugar processing by Pseudomonas putida

Pseudomonas putida, a soil bacterium widely used for synthetic biology and metabolic engineering, processes glucose through convergent peripheral pathways that ultimately yield 6-phosphogluconate. Such a periplasmic gluconate shunt (PGS), composed by glucose and gluconate dehydrogenases, sequentially transforms glucose into gluconate and 2-ketogluconate. Although the secretion of these organic acids by P. putida has been extensively recognized, the mechanism and spatiotemporal regulation of the PGS remained elusive thus far. To address this challenge, we have developed a novel methodology for metabolic flux analysis, D-fluxomics, based on deuterated sugar substrates. D-Fluxomics demonstrated that the PGS underscores a highly dynamic metabolic architecture in glucose-dependent batch cultures of P. putida, characterized by hierarchical carbon uptake by the PGS throughout the cultivation. Additionally, we show that gluconate and 2-ketogluconate accumulation and consumption can be solely explained as a result of the interplay between growth rate-coupled and decoupled metabolic fluxes. As a consequence, the formation of these acids in the PGS is inversely correlated to the bacterial growth rate--unlike the widely studied overflow metabolism of Escherichia coli and yeast. Our findings, which underline survival strategies of soil bacteria thriving in their natural environments, open new avenues for engineering P. putida towards efficient, sugar-based bioprocesses.

systems biology↗

bioRxiv2023

An RNA origami robot that traps and releases a fluorescent aptamer

RNA nanotechnology aims at using RNA as a programmable material to create self-assembling nanodevices for application in medicine and synthetic biology. RNA devices have been developed by adopting mechanisms such as allosteric binding and toehold-mediated strand displacement. There are, however, no examples of RNA "robotic" devices that sense, compute, and actuate through mechanical reconfiguration as has been demonstrated in DNA nanotechnology. Here we use the RNA origami method to prototype an RNA robotic device, named the "Traptamer", that senses two RNA key strands, acts as a Boolean AND gate, and activates the fluorescent aptamer iSpinach through release from a mechanical trap. The Traptamer depends on binding of two different RNA key strands to achieve full activation and can be reversed by addition of two complementary RNA anti-key strands. Cryo-EM of the closed Traptamer structure at 5.45 [A] resolution reveals a hinge-like mechanical distortion of the iSpinach motif. Our RNA robot prototype opens the door to build more sophisticated RNA machines that use sensing, computing, and acting modules to precisely control RNA functionalities.

bioengineering↗

bioRxiv2023

Differential functional coupling in Gp130-JAK complexes expands the plasticity of the interleukin-6 signaling axis

Cytokines dimerize/oligomerize surface receptors to activate signaling. While cytokine receptors preferentially bind only one member of the JAK family, ancestral cytokine receptors, such as Gp130, promiscuously recruit different JAKs to elicit their activities. Here, we have explored how the identity of JAKs in Gp130 signaling complexes can regulate functional outcomes. Using a synthetic biology approach, we show that Gp130 bound to different JAKs propagates distinct STAT activation profiles. While Gp130-JAK1 complexes activated both, STAT1 and STAT3 very potently, Gp130-JAK2 complexes exhibited a clear preference for STAT3 activation. Gp130-TYK2 complexes triggered overall weaker signaling but with diminished STAT specificity. The three JAKs competed for binding to Gp130 and led to differential activation of phospho-Tyr in the Gp130 intracellular domain. JAK1, JAK2 and to a lower extent TYK2 bound with comparable affinities to Gp130, and in response to IL-6 stimulation efficiently drove Gp130 dimerization. However, the three JAKs differentially affected Gp130 surface expression, identifying JAK-dependent receptor trafficking as a critical determinant of signaling plasticity. Our results provide new mechanistic insights into how differential functional coupling in Gp130-JAK complexes translates into unique signaling signatures that likely contribute to its large functional diversity.

cell biology↗

bioRxiv2023

The folding-limited nucleation of curli hints at an evolved safety mechanism for functional amyloid production

It is nearly two decades ago that the thin aggregative fimbriae which had been shown to enhance the biofilm formation of Salmonella enteriditis and Escherichia coli were identified as amyloid fibers. The realization that natural proteins can develop amyloidogenic traits as part of their functional repertoire instigated a search for similar proteins across all kingdoms of life. That pursuit has since unearthed dozens of candidates which now constitute the family of proteins referred to as functional amyloids (FA). FAs are promising candidates for future synthetic biology applications in that they marry the structural benefits of the amyloid fold (self-assembly and stability) while steering clear of the cytotoxicity issues that are typically linked to amyloid associated human pathologies. Unfortunately, the extreme aggregation propensity of FAs and the associated operational difficulties are restricting their adoption in real-world applications, underscoring the need for additional processes to control the amyloid reaction. Here we untangle the molecular mechanism of amyloid formation of the canonical functional amyloid curli using NMR, native mass spectrometry and cryo-electron microscopy. Our results are consistent with folding-limited one-step amyloid nucleation that has emerged as an evolutionary balance between efficient extracellular polymerization, while steering clear of pre-emptive nucleation in the periplasm. Sequence analysis of the amyloid curlin kernel suggests a finetuning of the rate of monomer folding via modulation of the secondary structure propensity of the pre-amyloid species, opening new potential avenues towards control of the amyloid reaction.

biochemistry↗

bioRxiv2023

Explaining Deep Neural Networks for the Prediction of Translation Rates

A recent convolutional neural network model accurately quantifies the relationship between massively parallel synthetic 5 untranslated regions (5UTRs) and translation levels, but the underlying sequence determinants remain elusive. Applying model interpretation, we extract representations of regulatory logic, revealing a complex interplay of regulatory sequence elements. Guided by insights from model interpretation, we adapt the model by human reporter data to obtain superior performance, which will promote applications in synthetic biology and precision medicine.

bioinformatics↗

bioRxiv2023

Trading acyls and swapping sugars: metabolic innovations in Solanum trichomes

Solanaceae (nightshade family) species synthesize a remarkable array of clade- and tissue-specific specialized metabolites. Protective acylsugars, one such class of structurally diverse metabolites, are produced by AcylSugar AcylTransferases from sugars and acyl-coenzyme A esters. Published research revealed trichome acylsugars composed of glucose and sucrose cores in species across the family. In addition, acylsugars were analyzed across a small fraction of the >1200 species in the phenotypically megadiverse Solanum genus, with a handful containing inositol and glycosylated inositol cores. The current study sampled several dozen species across subclades of the Solanum to get a more detailed view of acylsugar chemodiversity. In depth characterization of acylsugars from the Clade II species Solanum melongena (brinjal eggplant) led to the identification of eight unusual structures with inositol or inositol glycoside cores, and hydroxyacyl chains. Liquid chromatography-mass spectrometry analysis of 31 additional species in the Solanum genus revealed striking acylsugar diversity with some traits restricted to specific clades and species. Acylinositols and inositol-based acyldisaccharides were detected throughout much of the genus. In contrast, acylglucoses and acylsucroses were more restricted in distribution. Analysis of tissue-specific transcriptomes and interspecific acylsugar acetylation differences led to the identification of the S. melongena AcylSugar AcylTransferase 3-Like 1 (SmASAT3-L1; SMEL4.1_12g015780) enzyme. This enzyme is distinct from previously characterized acylsugar acetyltransferases, which are in the ASAT4 clade, and appears to be a functionally divergent ASAT3. This study provides a foundation for investigating the evolution and function of diverse Solanum acylsugar structures and harnessing this diversity in breeding and synthetic biology.

plant biology↗

bioRxiv2023

Molecular imaging with aquaporin-based reporter genes: quantitative considerations from Monte Carlo diffusion simulations

Aquaporins provide a new class of genetic tools for imaging molecular activity in deep tissues by increasing the rate of cellular water diffusion, which generates magnetic resonance contrast. However, distinguishing aquaporin contrast from the tissue background is challenging because water diffusion is also influenced by structural factors such as cell size and packing density. Here, we developed and experimentally validated a Monte Carlo model to analyze how cell radius and intracellular volume fraction quantitatively affect aquaporin signals. We demonstrated that a differential imaging approach based on time-dependent changes in diffusivity can improve specificity by unambiguously isolating aquaporin-driven contrast from the tissue background. Finally, we used Monte Carlo simulations to analyze the connection between diffusivity and the percentage of cells engineered to express aquaporin, and established a simple mapping that accurately determined the volume fraction of aquaporin-expressing cells in mixed populations. This study creates a framework for broad applications of aquaporins, particularly in biomedicine and in vivo synthetic biology, where quantitative methods to measure the location and performance of genetic devices in whole vertebrates are necessary.

bioengineering↗

bioRxiv2023

TidyTron: Reducing lab waste using validated wash-and-reuse protocols for common plasticware in Opentrons OT-2 lab robots

Every year biotechnology labs generate a combined total of [~]5.5 million tons of plastic waste. As the global bioeconomy expands, biofoundries will inevitably increase plastic consumption in-step with synthetic biology scaling. Decontamination and reuse of single-use plastics could increase sustainability and reduce recurring costs of biological research. However, throughput and variable cleaning quality make manual decontamination impractical in most instances. Automating single-use plastic cleaning with liquid handling robots makes decontamination more practical by offering higher throughput and consistent cleaning quality. However, open-source, validated protocols using low-cost lab robotics for effective decontamination of plasticware--facilitating safe reuse--have not yet been developed. Here we introduce and validate TidyTron: a library of protocols for cleaning micropipette tips and microtiter plates that are contaminated with DNA, E. coli, and S. cerevisiae. We tested a variety of cleaning solutions, contact times, and agitation methods with the aim of minimizing time and cost, while maximizing cleaning stringency and sustainability. We tested and validated these cleaning procedures by comparing fresh versus cleaned tips and plates for contamination with cells, DNA, or cleaning solutions. We assessed contamination by measuring colony forming units by plating, PCR efficiency and DNA concentration by qPCR, and event counts and debris by flow cytometry. Open source cleaning protocols are available at https://github.com/PlantSynBioLab/TidyTron and hosted on a graphical user interface at https://jbryantvt.github.io/TidyTron/.

molecular biology↗

bioRxiv2023

A novel platform for metabolomics using barcoded structure-switching aptamers

Small organic molecules like metabolites and drugs are critical for diagnostics, treatment, and synthetic biology. Measuring them presents two key challenges however: they are biochemically highly diverse and there is no method to amplify them. Mass spectrometry has been the workhorse of metabolomics for decades but is costly and slow and single-cell metabolomics remains very challenging. Here we describe an alternative platform for metabolomics based on structure-switching aptamers (SSAs). SSAs are short nucleic acid molecules that each recognise a specific target ligand and undergo a major conformational change on ligand binding. This conformational change can drive detection such as fluorescence allowing SSAs to be used as sensors. We adapted conventional SSAs to a novel readout: barcode release. Each SSA recognises a unique ligand and each SSA releases a unique barcode allowing many ligands to be detected in parallel. We show that these barcode SSAs (bSSAs) can be multiplexed and act as independent sensors and that barcode release can be massively amplified to allow high sensitivity. Finally, we establish methods for the generation of large collections of bSSAs where barcode-SSA matching is completely directed. We believe that this novel platform which converts metabolite detection into barcode sequencing will allow the deep multiplexed detection of metabolites and drugs down to the scale of single cells.

genomics↗