Mobius Assembly for Plant Systems highlights promoter-coding sequences-terminator interaction in gene regulation

Mobius Assembly for Plant Systems highlights promoter-coding sequences-terminator interaction in gene regulation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Mobius Assembly for Plant Systems highlights promoter-coding sequences-terminator interaction in gene regulation Naomi Nakayama, Elif Gediz Kocaoglan, Andreas Andreou, Jessica Nirkko, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5118685/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Plants are the primary biological platforms for producing food, energy, and materials in agriculture; however, they remain a minor player in the recent synthetic biology-driven transformation in bioproduction. Molecular tools and technologies for complex, multigene engineering in plants are as yet limited, with the challenge to enhance their stability and predictivity. Here, we present a new standardized and streamlined toolkit for plant synthetic biology, Mobius Assembly for Plant Systems (MAPS). It is based on small plant binary vectors pMAPs, which contain a fusion origin of replication that enhances plasmid yield in both Escherichia coli and Rhizobium radiobacter . MAPS includes a new library of promoters and terminators with different activity levels; part sizes were minimized to improve construct stability and transformation efficiency. These promoters and terminators were characterized using a high-throughput protoplast expression assay. We observed a significant influence of terminators on gene expression, as the strength of a single promoter can change more than seven-folds in combination with different terminators. Changing the coding sequence changed the relative strength of promoter and terminator pairs, thus uncovering combinatorial gene regulation among all parts of a transcriptional unit. We further gained insights into the mechanisms of such interactions by analyzing RNA folding, with which we suggest a design principle for more predictive and context-independent genetic parts in synthetic biology of plant systems and beyond. Biological sciences/Systems biology/Synthetic biology Biological sciences/Plant sciences/Plant molecular biology Biological sciences/Genetics/Gene regulation Biological sciences/Biotechnology/Molecular engineering Plant synthetic biology context dependency gene regulation promoters terminators protoplast interaction RNA folding Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION Throughout the history of civilization, plants have been the primary culturing platform for food, energy, materials, and drugs. However, in synthetic and engineering biology applications, plants have remained a minor vehicle for bioproduction with untapped potential. Consequently, the molecular tools and technologies for rational engineering of plant systems are still limited compared to their microbial and mammalian counterparts. With the increasing world population and ongoing climate crisis, considerately designed plant engineering holds the key to a sustainable future, creating transformative solutions for a wide range of applications from agriculture to environmental regeneration 1 – 4 . From enhancing crop yields, quality, and resilience to improving carbon fixation and biofuel efficiency, synthetic biology-based plant engineering is becoming a powerful catalyst to futureproof our society and planet 5 – 7 . Synthetic biology enables complex pathway engineering necessary for modifications of biological metabolisms, cellular and developmental pathways, or responses to pathogens and diseases. The foundation of synthetic biological genetic engineering is the engineering principles, such as standardization, modularity, and design-build-test cycles 8 . However, as the field matures past its first two decades, biology has often defied these fundamental assumptions. The concept of orthogonality – context-independent, universal functions of genetic parts – is routinely challenged, uncovering new insights and understanding of how biological systems interact 9 . As this can be the case for just a single gene, it poses a significant hurdle when working with the many genes necessary for pathway and network engineering. The development of synthetic biology tools and technologies for multigene engineering starts with creating a collection (library) of standardized genetic parts. Standardized parts (e.g., Phytobricks) 10 include promoters, coding sequences, terminators, and functional protein tags, which together form transcriptional units (TUs). Promoters, which contain the transcription start site (TSS) where RNA polymerase binds, are well established for their instructive roles in regulating gene activity. In comparison, terminators, typically found at the end of a gene, are often underestimated in their regulatory roles. However, they have been shown to influence transcription by controlling transcription arrest, mRNA stability, protecting genes against silencing and tuning other transcription functions 11 . The most commonly used promoters and terminators in plant genetic engineering originally stemmed from Cauliflower Mosaic Virus ( 35S ), Rhizobium opine genes ( NOS, MAS , and OCS ), and more recently, plant genomes (e.g., UBQ10 promoter and HSP terminator from Arabidopsis thaliana or hereafter Arabidopsis). The shortage of well-characterized regulatory parts results in repeated use of the same sequences in multigene construction, increasing the possibility of homologous sequence-dependent recombination and gene silencing 12 , 13 . In addition, there is a need for shorter standard parts, especially when multiple genes are delivered in a single vector, to reduce the size of the construct. Large constructs tend to make plasmids structurally unstable 14 and decrease the efficiency of Rhizobium -mediated transformation 15 . Therefore, creating novel and shorter promoter and terminator parts will enrich the plant genetic engineering toolkit available to the community and increase our capacity to create genetic modifications. Once standard genetic parts are made, their activity level should be assessed. This typically involves combining a promoter and terminator pair with a reporter protein coding sequence and quantifying the reporter expression as output. While promoters are generally more well characterized, recently, attention has also been directed towards terminators; diverse arrays of terminators have been characterized in bacteria 16 and yeast 17 . For plant systems, the number of available terminator parts was limited for a long time, with their characterization typically performed using the Nicotiana benthamiana leaf infiltration system 18 , 19 . It is worth noting that different species, or even different expression systems within the same species, may exhibit varying levels of expression for the same TU 18 , 19 . Therefore, to achieve higher stability and predictability, new standard parts should be characterized in different combinations and chassis environments to account for potential context dependency. As the number of constructs to build and characterize increases, molecular construction needs to become more efficient. Golden Gate is a widely used technology for DNA assembly that utilizes Type IIS restriction endonucleases that cut DNA outside their recognition sequences, allowing the introduction of short ‘sticky end’ sequences (syntaxis) that help orient and combine multiple DNA fragments in an intended order. Many Golden Gate frameworks have been developed for diverse species 20 – 23 ; recent developments in the plant field include Loop Assembly 24 , an extension of the MoClo toolkit for plants 25 , and Joint Modular Cloning 18 . We have previously developed a new Golden Gate framework 26 , named Mobius Assembly, for its iterative cloning strategy between two vector sets; it allows theoretically infinite assembly of up to four DNA fragments each time. It is designed to be simple, versatile, and efficient (e.g., less need for domestication), complying with the universal and popularly employed the syntaxis design Phytobrick 27 . In plant transgenic engineering, binary vectors house transgenes and facilitate their delivery and incorporation into the recipient genomes. Although functional, these vectors could be improved to enhance transformation efficiency and stability. A typical Rhizobium -mediated transformation vector includes T-DNA borders 28 , replication functions for E. coli and Rhizobium , and selection markers. Plasmid instability is a significant challenge, especially for plant systems, as vectors must be stable in both E. coli and Rhizobium . Factors such as size 14 , copy number 29 , direct repeats12 and inverted repeats 30 affect plasmid stability. Traditional vectors are bulky (> 6 kb), but smaller backbones like pGreen (2.5 kb 31 ), pLSU (4.6 kb 32 ), and pLX (3.3 kb 33 ) have been developed to reduce instability. The pLX vectors use a modular design with minimal functional parts and stability features, improving binary vector construction. Here, we report the adaptation of Mobius Assembly for plant species engineering: Mobius Assembly for Plant Systems (MAPS). The MAPS toolkit includes new, compact plant binary vectors (pMAPs). These vectors are 3.6 kb in size, structurally stable through molecular construction, and suitable for transformation methods requiring high plasmid DNA amounts. MAPS also features bioluminescence and fluorescent protein reporters, along with new standardized promoter and terminator parts for a range of gene expression strengths. In high-throughput transient expression tests with Arabidopsis thaliana protoplasts, MAPS promoters effectively drove gene activation gradients. We also demonstrated that terminators can be used to control gene expression levels. The strength of the promoter and terminator parts depended on their combinations, and such interactions extended to the coding sequence selection. These findings facilitate a bottom-up understanding of gene regulation and the design of context-independent molecular constructs with more predictable outputs. RESULTS Development of Mobius Assembly for Plant Systems (MAPS) MAPS is an extension of our molecular cloning framework Mobius Assembly 26 to enable transformation and expression of transgenes in plant systems. MAPS has a universal acceptor vector (mUAV) at Level 0 to house a standard part in the Phytobrick format. Level 0 parts are combined at Level 1, and up to four Level 1 constructs are then combined to make Level 2 constructs. MAPS follows a linear cloning strategy until Level 2 and then iterates between two cloning levels (Level 1 and Level 2) for quadruple augmentation of cloning units each time (Fig. 1 a). Using the rare cutter AarI (PaqCI) , as opposed to frequently used restriction enzymes that recognize shorter sequences (e.g., BsmBI or BpiI ), reduces the need for removing internal restriction sites ( i.e. , domestication). Initially, we developed pGreen-based vectors but encountered issues with large constructs consistent with reported instability issues 34 , 35 . To address this, we created a new small plant binary vector called pMAP, based on the pLX architecture (Fig. 1 b). This vector is suitable for transient expression in protoplasts, cell culture, tissues/organs, and whole-plant stable transformation. We devised a new origin of replication by fusing pWKS1 and pUC19 Ori. The pWKS1 Ori, derived from Paracoccus pantotrophus DSM 11072 36 , is functional in Rhizobium but not in E. coli , so we fused it with the minimal stable pUC Ori from pUC19. pMAPS also has two Left Border (LB) sequences instead of one. Rhizobium gene transfer is from the Right Border (RB) to the LB, and having two LBs suppresses backbone transfer to the plant genome 37 . Another feature of pMAPS is that terminators flank LB and RB sequences to isolate transgene activity from the plasmid backbone. Colourific markers, antibiotics selections, and restriction enzymes used for Mobius Assembly at different levels are summarized in Fig. 1 c. MAPS vector toolkit consists of a core set of pMAP cloning/destination vectors (Level 1 and Level 2 Acceptor Vectors, four variations Α-Δ for each level), which have a fusion origin of replication to replicate in E. coli and Rhizobium . The mUAV and the seven Auxiliary plasmids are also included, as described in the original Mobius Assembly kit 26 . The MAPS toolkit also contains a selection of plant promoters, terminators, antibiotic resistance genes, and visible reporter genes (bioluminescence and fluorescent proteins). All MAPS plasmids are listed in Supplementary Fig. S1 ; Supplemental Tables S1, S2 and available through AddGene ( https://www.addgene.org/browse/article/28211394/ ). Reflecting on user feedback, two specific improvements were made to improve the Mobius Assembly vectors. A few users of the original Mobius Assembly kit indicated that the chromoprotein selection had been lost in their clones. Upon investigation, we observed independent events of transposon insertion in the promoter of the chromoprotein genes, which we hypothesized is a response to the stress imposed by chromoprotein production ( Supplementary Fig. S2). To solve the problem, we replaced the marker chromogenic protein (spisPINK) with a red fluorescent protein (mScarlet-I). ( Supplementary Fig. S2). We also noticed some Mobius Assembly clones showed growth retardation in the selection media and identified the instability was caused by plasmid dimerization. Therefore, we introduced a 240-bp cer domain, which recognizes dimers and triggers recombination help keep the plasmids in the monomeric state ( Supplementary Fig. S3 ) 38 . Combinatorial DNA libraries are crucial for part characterization, as well as in applications such as biosynthetic pathway optimization 39 , but their manual construction is time-consuming and resource-intensive. To aid combinatorial library construction, we developed the 'MethylAble' feature in Mobius Assembly, allowing standard part variants (Level 0) to be introduced to specific sites in single or multi-gene constructs ( Supplementary Fig. S4) . MethylAble utilizes the DNA methylation sensitivity of BsaI to mask its recognition sites by cytosine methylation during Level 1 cloning. We designed an amilCP expression cassette with divergent and convergent BsaI recognition sites, where CpG methylation blocks BsaI digestion only at divergent sites, allowing insertion of Level 0 parts into premade Level 1 constructs. Correct constructs show a purple color from amilCP until the Level 0 parts replace the cassette. As a proof of concept, the MethylAble protocol was used to build the library of the three inducible promoters (see below), each of which was combined with the 14 terminator coparts, making 42 constructs in total ( Supplemental Table S3 ). MethylAble presents a novel strategy to create construct libraries and can be implemented in all Golden Gate frameworks in which BsaI enzyme is used, not only in Mobius Assembly. Designing, building, and testing MAPS promoter/terminator standard parts To select new ‘constitutive’ promoter and terminator parts, we chose ubiquitously expressed genes that are likely to have strong expression in different tissue types (Supplementary Table S2). The promoters and terminators were characterized with a transient gene expression assay based on Arabidopsis mesophyll protoplasts and PEG transformation. We optimized the parameters throughout the protocol based on 40 to improve the transformation efficiency and reproducibility. We were able to reach up to 70% transformation efficiency consistently ( Supplementary Fig. S5 ), which was high enough to adapt to plate reader measurements in a 96-well format. To evaluate the promoter/terminator activity levels, we used a dual luciferase system with highly sensitive nano luciferase (NLuc) as the reporter and firefly luciferase (FLuc) for normalization 41 . To account for possible batch-to-batch differences in overall protoplast transformation rates/efficiency, we included FLuc gene ( UBQ10-FLuc:UBQ5 ) in each construct and calculated the NLuc/FLuc ratio as RLU (Relative Light Unit). For the promoter testing, seventeen promoters drove NLuc expression, with termination by either the NDUFA8 or HSP terminator ( Promoter:NLuc:HSP/NDUFA8 ). The UBQ10 promoter exhibited by far the highest expression activity among the promoters, followed by MAS (Fig. 2 a,b). The HSP terminator increased gene expression for all promoters except TUB9 . Two of the newly isolated promoters, UBQ11 and UBQ4 , matched or exceeded the activity of the 35S and OCS promoters. Furthermore, the newly isolated promoters ACT7 , TUB2 , TUB9 , APT1 , ACT2 and LEC2 outperformed the commonly used the NOS promoter. The FAD2 and NDUFA8 promoters had the lowest expression. For the terminator evaluation, the NLuc expression was driven by a strong ( UBQ10 ) or weak ( NDUFA8 ) promoter and one of the 14 terminators ( UBQ10/NDUFA8:NLuc:Terminators ). The luciferase expression varied by 5.3–6.3 fold for the UBQ10 and NDUFA8 promoters, respectively, depending on the terminators they were paired with (Fig. 2 d,e). For the strong UBQ10 promoter, the FAD2 terminator had the highest activity (547.9 RLU), while the NOS terminator had the lowest (103.9 RLU). For the weak NDUFA8 promoter, the HSP terminator led to the highest expression (0.327 RLU) and APT1 to the lowest (0.052 RLU).Since it was surprising to see a wide range of gene expression levels led by different terminators under the same promoter, we extended terminator characterization with the three chemically inducible systems popularly used in plant sciences: dexamethasone (Dex), estradiol, and ethanol inducible systems 42 – 44 . They are all based on two-component mechanisms involving at least two transcriptional units. The exogenously applied chemical activates the transcription factor that further transactivates the downstream target genes. The target genes are activated by the specific promoters that contain binding sites for the transactivator ( pOp6 , lexA , and alcSynth ), and hence promoters cannot be changed, while terminators can be. Interestingly, the different terminators resulted in more uniform NLuc expression, with a 2.2- and 2.9-fold range in expression levels for the Dex and estradiol inducible promoters ( pOp6-35S and lexA-35S ), respectively (Fig. 2 h,i). For the Dex system, RLU was spread between 37.9 and 111.6 in combination with the UBQ5 and 35S terminators. For the estradiol promoter ( lexA ), the E9-RbcS and 35S terminators had RLU counts of 11.6 and 25.1, respectively. In contrast, the ethanol inducible promoter ( alcSynth ), showed a much wider range, with the HSP terminator driving sevenfold higher expression than the LEC2 terminator (Fig. 2 j). Both the Dex and estradiol systems showed a basal expression of around 10 RLU; with Dex inducing a ~ 11-fold and estradiol a ~ 3-fold activation. The ethanol system had high basal expression (i.e., it was leaky), leading to only 30% increase in luminescence upon chemical induction. Promoter-coding sequence-terminator interactions in gene regulation Reflecting on the observed promoter-terminator interactions, we investigated whether changing the coding sequence also influences promoter/terminator activity. Fluorescent proteins are an alternative visible reporting system to luciferases. However, using fluorescent proteins in a plant chassis can be challenging as plants emit red and green-range autofluorescence from their chloroplasts, and stress-induced blue-range autofluorescence from their cytoplasm 45 . Over the years, fluorescent proteins with improved brightness and expression dynamics have been developed, but their quantitative efficacy was not comprehensively characterized in protoplasts. Therefore, we screened fluorescent proteins from four spectrums (green, red, yellow, and blue) to examine their compatibility with a protoplast system using a plate reader or microscopy ( Supplementary Table S4 ). Generally, expression of fluorescent proteins was detected 6 hours after transformation and plateaued around 15 hours ( Supplementary Fig. S6 ). Informed by this screening, we selected the brightest two fluorescent proteins from different spectra: sfGFP and mScarlet-I. sfGFP was the main reporter, while mScarlet-I was used as the normalizing gene (similar to FLuc above) and expressed using the UBQ10 promoter and UBQ5 terminator. Initially promoters were evaluated with the HSP terminator for sfGFP expression. Unlike luciferase reporters, not all promoters drove strong enough expression that could be detected with a plate reader (Fig. 2 c). The highest expression was driven by the UBQ10 and MAS promoters, followed by the 35S promoter. Readouts from the rest of the promoters could not be distinguished from the background autofluorescence. Therefore, the UBQ10 and MAS promoters were analyzed in combination with all the MAPS terminators ( Fig. 2 f,g ). The HSP terminator was the strongest with both promoters (9.0 and 12.1 RFU for UBQ10 and MAS , respectively ). The FAD2 terminator was on the high-expression side for both promoters, along with the UBQ10 promoter-35S terminator and MAS promoter-NOS terminator combinations. The Rbsc2b terminator resulted in the lowest expression with both promoters. Overall, a 3-fold expression difference was observed by using different terminators with the UBQ10 promoter, and the difference was 6-fold with the MAS promoter. Our part characterization revealed that although promoter choice could dominantly determine gene expression strength in some cases ( e.g., pOp6 and NDUFA8 ) (Fig. 3 a), TU activation by a promoter, terminator or coding sequence is not independent or additive, but combinatorial and even synergistic (Fig. 3 b). A clear example of such all-part interactions is seen with the NOS terminator, which in combination with the UBQ10 resulted in weak, NDUFA8 strong, lexA medium, pOp6 weak and alcSynth mid-level expression in the luciferase system. When we switched to a fluorescence-based reporter, the combination of the NOS terminator with the UBQ10 and MAS promoters drove medium and strong expression, respectively. Among the 14 terminators characterized, only four exhibited stable behaviours with both coding sequences: FAD2 and HSP were consistently on the strong side, while LEC2 and RbcS2b tended towards the weak side. Dissecting the mechanism of the combinatorial gene regulation Next, we sought for insights into how the promoters, coding sequences, and terminators interacted in gene regulation. The interactions may regulate gene expression by changing the transcript abundance or with post-transcriptional modifications affecting translation. To distinguish these two possibilities, we performed qPCR to examine how the transcript (mRNA) level correlates with the reporter readout. Because transforming enough protoplasts for RNA extraction is laborious, we selected key constructs to test. The HSP and FAD2 terminators were chosen for consistently strong expression regardless of different promoters and reporter protein sequences. For consistently weak expression, the NOS and Rbcs2b terminators were selected for NLuc , whereas the Rbcs2b, APT1 and E9-RbcS terminators were chosen for sfGFP . The NOS terminator was chosen because its relative strength varies the most depending on the partnering promoters or coding sequences. Similarly, the 35S terminator was selected for its variability in strength, although it tends to be on the strong side. The qPCR assay revealed that the consistently strong FAD2 and HSP terminators have significantly higher mRNA levels; similarly, the weak E9-Rbcs2b terminator had significantly lower mRNA levels than the other terminators (Fig. 4 a). Generally, NLuc displayed a linear correlation between the measured reporter expression levels and mRNA levels (Fig. 4 b), while sfGFP exhibited a looser correlation (Fig. 4 c). Surprisingly, the 35S terminator yielded higher reporter expression in both reporter combinations compared to its mRNA levels (Fig. 4 b,c). Taken together, the transcript level analysis suggests that the combinatorial gene regulation is partially explained by transcript abundance and likely to involve post-transcriptional (post-mRNA formation) processes in some constructs. The 35S terminator for both NLuc and sfGFP suggested a post-transcriptional enhancer effect, while NLuc:NOS , together with sfGFP:RbcS2b and sfGFP:APT1 constructs indicated post-transcriptional repression. Transcriptional regulation is mediated by specific DNA sequences that recruit functional proteins to activate or repress processes from transcriptional initiation to stable mRNA formation. Surprised by how effective terminators are in controlling gene expression, we investigated the nucleotide sequence features possibly influencing terminator strength, such as the GC content and presence of likely functional sequences ( e.g ., the canonical poly-A signal AAUAAA and UGUA motifs) (Fig. 5 , Supplementary Fig. S7 ). A GC content of approximately 30% has been shown to be optimal for synthetic terminator functions, and the average GC content in natural Arabidopsis terminators is about 32.5 46 .The GC content in the MAPS terminators had no clear correlations between the strength and GC content of the terminators; consistently or predominantly strong terminators [ HSP (29.1%), FAD2 (36.5%), and 35S (32%)] had a similar range of GC content as weak or variable terminators [ RbcS2b (30%), E9-RbcS (32.8%), APT1 (35%), and NOS (38.7%)] (Fig. 5 a). One apparent feature in the strong terminators is that the GC content is apparently lower at a 50 bp window, which is located around their dominant Poly-A signal, and then the GC content goes back up. The presence of the canonical AAUAAA poly-A signal tends to enhance terminator strength 46 . We also searched for sequence motifs that may link to the consistently high strength of HSP and FAD2 terminators. The presence of the UGUA motif around 30–40 bp upstream of the RNA cleavage site is thought to enhance the cleavage and thus increase terminator strength in the 35S terminator 46 . The HSP terminator has two UGUA motifs at locations 119 bp and 206 bp, while FAD2 lacks the motif. Interestingly, the XSTREME motif discovery tool identified four motifs that putatively have functional roles a piriori (Fig. 5 b,c,e). One of them is the 15-bp motif (CAAAUGUUUUGUGUC) found around 145 bp in both the HSP and FAD2 terminators, which correspond to the transcript cleavage site. Three other motifs - CUCAUUAUGUUA, UUGUUGUGUUAUGAC, and UUUUUCUAAUAUUA - were found at similar locations in both terminators but around 10–20 bp apart. Only the CUCAUUAUGUUA motif is present in the UBQ5 terminator at 167 bp (Fig. 5 b). The effects of the UUUUU motif are complicated; it decreases terminator strength in maize protoplasts, especially if they surround a UGUA motif, while U-rich sequences increase terminator strength in tobacco leaves 46 . Many of these AT/U-rich motifs reside inside the 50 bp low GC domains described above. We examined similar potentially functional features in the ‘outlier terminators’ – the terminators performing stronger or weaker than expected for their transcript levels ( i.e ., likely post-transcriptionally regulated) (Fig. 4 a,b). The GC content of outlier terminators is moderate and varies between 30.5–35.0% (Fig. 5 a). RbcS2b is the only outlier terminator that does not possess the canonical poly-A signal. The 35S terminator has three repeats of the UGUA motif (x3 starting from 96 bp separated by UU). The UUUUU motif is present in the NOS , RbcS2b and APT1 terminators (Supplementary Fig. S6). Therefore, no distinct sequence signatures were identified to differentiate the terminators that are primarily regulated transcriptionally from the outliers. We then wanted to experimentally probe how the above-identified sequence features influence the terminator activity by generating a deletion series of the HSP and FAD2 terminators. Five terminator variations were made to sequentially delete possible regulatory elements, such as Poly-A signals, putative destabilization signals, and Musashi binding elements (Fig. 5 c-f). Statistical analysis of the results showed only a significant difference between the full-length terminator (240 bp) and FAD2 Sequence 5, which is the shortest variation (70 bp) with no Poly-A site, as well as the deletion series Sequence 1 (200 bp). The rest of the HSP series, as well as the FAD2 series, showed no statistically significant difference in reporter expression (Fig. 5 c-f). This result suggests short sequences (30–50 bp) at the 5’ end of terminators might determine the gene expression strength, and terminator functional elements remain unresolved, especially in plant contexts. We also investigated a sequence feature likely enhancing promoter activity. UBQ10 is by far the strongest promoter in the MAPS toolkit (Fig. 2 , 3 ), and its TSS structure is predicted to be unstructured and single-stranded (Fig. 6 ). In the survey of the Arabidopsis genome, translation efficiency was found higher for transcripts with unstructured 5’UTR 47 . To test if the strength of the UBQ10 promoter is dependent on the open loop structure of its 3’ UTR, which consists of mostly TSS, three mutated versions were created. The first version, Mutated Control (MUTC), involves point mutations that preserve the predicted loop structure; therefore, it is expected to behave similar to the original UBQ10 TSS sequence. Additionally, two other versions, a tight stem-loop structure Mutated 1 (MUT1) and a slightly more branched but more loosely stemmed Mutated 2 (MUT2), were designed with point mutations (Fig. 6 a,b). TSSPlant and Softberry software were used to confirm the mutations do not interfere with the identified motifs or introduce new motifs compared to the WT. When the TSS variants were used to drive sfGFP with five different terminators ( HSP, FAD2, 35S, NOS , and RbcS2b ), the results revealed no statistically significant difference in expression between WT and MUTC, as expected (Fig. 6 c). However, MUT2 exhibited a reduction in expression by approximately 20% for the HSP and FAD2 terminators. Additionally, MUT1 resulted in a 60% decrease in expression with 35S , while resulting in a 30% increase with the NOS terminator. There was no difference between the three mutated versions and the WT for the RbcS2b samples, where the WT expression is already very weak (Fig. 6 c). With the strong terminators, reporter expression was reduced in MUT1 and MUT2 compared to WT and MUTC variants, suggesting that conversion from open loop to stem-loop decreases gene expression. To gain insights into how promoters, coding sequences, and terminators interact post-transcriptionally, we studied RNA folding. RNA is single-stranded and extensively forms secondary and tertiary structures via hydrogen bonds bringing together (nearly) complementary sequences. Such 2D and 3D structures ( e.g ., G-quadruplex) can strongly influence translation and protein expression 48 , 49 . The combination-dependent regulatory function among the three TU parts may be explained by direct physical interactions through RNA folding. Using the RNAFold software 50 , the folding energy of the whole mRNA sequence was calculated for the transcript species we selected for the qPCR analysis above. The transcription start site (TSS) was identified based on the TSSPlant software 51 , and the downstream promoter sequence was incorporated, along with the protein-coding and terminator sequences. The lower the holding energy, the tighter the transcript folds, and the less likely for translation to occur. No direct correlation was found between the predicted transcript folding energy and mRNA expression levels or between the folding energies and reporter gene expression (Fig. 4 d,e). We therefore proceeded to examine the RNA folding structure and local interactions among the nucleotide sequences. We then visually examined the RNA folding structures in 2D. RNA secondary structure formation was predicted using the ViennaFold2.0 software 50 , in which the whole transcript (identified as described above) was used as the input. Within the transcript, the strong UBQ10 and MAS promoters are likely to have little interaction with the other parts: no interaction with the HSP and RbcS2b terminators and minimal to medium interactions with the FAD2 terminator were predicted by the software (Fig. 7 ). On the contrary, NOS , a highly variable terminator, may have strong interactions with the other parts ( UBQ10:NLuc ) when it drives weak reporter expression. Interestingly, there was no apparent correlation between inducible promoter-terminator structures and reporter output, except for pOp6:NLuc:NOS ( Supplementary Fig. S8 ). In strong and consistent terminators like HSP , we tended to find loops bigger than 20 bp ( Fig. 7 ). When the variable strength-terminator NOS is paired with strong promoters ( UBQ10 and MAS) , it also may form a large loop structure, though not when combined with other promoters ( e.g ., NDUFA8 ) (Fig. 6 , Supplementary Fig. S9 ). RNA sequence-mediated cross-part interactions among the promoters, coding sequences, and terminators could explain the combinatorial gene regulation. DISCUSSION MAPS: A new synthetic biology toolkit for engineering with plant systems In the present work, we established the Mobius Assembly for Plant Systems (MAPS), which is an adaptation of the Mobius Assembly 26 for plant genetic programming ( Supplemental Figure S1 , Supplemental Table S1 ). MAPS is a stand-alone yet highly versatile and functional DNA assembly platform and genetic toolkit collection. It is based on small binary vectors for all levels of cloning and comes with a well-characterized, Phytobrick-compatible standard part library for gene expression control in plant species, including a suite of reporter protein cassettes. We have also introduced a new feature for Mobius Assembly to facilitate the generation of a series of constructs with part variations; MethyAble exploits in vitro DNA methylation to directly feed Phytobricks in single- or multi-TU constructs. For plant genetic engineering, small binary vectors are instrumental in enhancing efficiency in cloning and transformation. As we wanted a reliable core vector in our kit for both cloning and transformation applications, we developed a new binary vector pMAP. This new vector has a unique dual-mode origin of replications for E. coli and Rhizobium backgrounds, which enables amplification in high copy numbers for experiments demanding high DNA yields, such as the protoplast assay (4 ug DNA per 75 ul transformation) used in this study. The plasmid dimerization phenomenon we have observed is not unique to Mobius Assembly vectors; in fact, with the increasing availability of whole-plasmid sequencing technologies, more dimers are being detected. Therefore, including a cer site in the backbone provides an effective method for preventing dimerization and growth defects ( Supplementary Fig. S3 ) 38 . MAPS delivers a collection of short promoters (17 constitutive and three inducible) and terminator (14 constitutive) standard parts for plant expression. Ten of the promoters and six of the terminators were newly isolated, and they are short in length: 300–600 bp (promoters) and 200bp (terminators). We confirmed the short size of the promoters did not compromise their strength. The Actin 2 promoter characterized in MoClo and GB2.0 toolboxes had comparable activity to the NOS promoter 52 , 53 ; the shorter version in the MAPS toolkit, ACT2 (340bp) and NOS promoter, exhibited similar expression activity (Fig. 3 ). In general, the size of the available standard promoter or terminator parts for plant engineering ( e.g ., MoClo plants, GB plants, and GreenGate) can be as long as 4 kb. Shorter parts are desirable as large constructs can reduce plasmid stability in bacteria, while also lowering transformation efficiency and causing incomplete/truncated transformation in planta 14 , 15 . Here, we reported the first characterization of the MAPS toolkit, which was done exclusively in Arabidopsis leaf mesophyll protoplasts with a plate reader-based high-throughput assay. Most plant standard part characterization to date has been conducted in either Arabidopsis protoplasts or Nicotiana benthamiana (leaf expression system), which are both dicot plants. The standard parts can behave differently in different backgrounds. In monocots, for example, the 35S and NOS promoters drive low activity in monocot Setaria viridis 18 , 54 . Even within dicots, the same part can have different levels of expression 52 , 53 . The OCS promoter had strong activity in our study but low expression in N. benthamiana 52 . Genetic parts can behave differently in different chassis, and we should not generalize characterization results among different plant species or expression systems ( e.g. , whole plants, leaves, protoplasts, or cell cultures). We recommend not making assumptions and testing the activity levels of standard parts in the specific context in which users want to apply them. Terminators influence gene expression, in part through interaction with promoters Our characterization of the MAPS terminators underscored the strong influence of the terminators cast on gene expression. It is a common practice to rely on promoters to control gene expression level and overlook the impact of terminators. For example, when the domestication of the synthetic plant promoter G10-90 promoter resulted in loss of expression, researchers replaced it with the AtRPL37aC promoter to rectify 44 . They were using the psE9-RbcS terminator, which in our assay had low activity; the gene expression could potentially have been restored with a stronger terminator instead. Even though promoters generally have a strong influence on gene expression (Fig. 3 a), terminators could alter the gene expression up to 8-fold in this study (Fig. 2 , 3 ). Alternative to constitutive expression, chemically inducible systems are powerful tools to control the timing, localization, and amplitude of transgene expression. Dex-, estradiol-, and ethanol-inducible systems were developed 20 years ago and have been used in numerous studies since then 42 . The extent of target gene induction can be modulated by exchanging the terminators (Fig. 3 ). Terminators influence gene expression through multiple mechanisms 55 , including post-transcriptional regulation via the 3' UTR, which can affect mRNA stability through specific sequence motifs. For instance, certain motifs at the 3' UTR in Arabidopsis can stabilize (e.g., TTGCTT) or destabilize (e.g., AATTTT) mRNA 56 . Poly(A) tails also play a role, with short tails often linked to strong gene expression 57 . In Arabidopsis, the poly(A) tail can prevent RDR6 from converting aberrant mRNAs into degradation substrates 57 . Unpolyadenylated transcripts derived from terminator-less constructs or readthrough mRNAs from transgenes with strong promoters are subjected to RDR6-mediated silencing 59 , which might explain the increased gene expression observed with double terminators 60 – 62 . However, the synergistic effects of terminators, particularly when combined with weak promoters, suggest interactions beyond these mechanisms. Direct interactions between terminators and promoters, potentially involving gene looping mediated by DNA-binding proteins, can influence gene expression 63 . These interactions might affect transcriptional memory 64 , intron-mediated modulation of transcription 65 , transcription directionality 66 , reinitiation of transcription 67 , and transcription termination 68 , 69 . Additionally, terminators may impact epigenetic regulation, as the absence of a terminator can lead to increased DNA methylation on promoter regions, silencing transgene expression 70 . Promoter-coding sequence-terminator interactions in gene regulation In addition to the interactions between terminators and promoters, we observed that protein-coding sequences can also modify the strength of promoter-terminator function. The coding sequence may impact gene expression through factors such as codon usage bias, gene length, GC content, correct 5' cap, and mRNA folding 71 . Both the NLuc (516 bp, GC 52.7%) and sfGFP (714 bp, GC 61.3%) have been optimized for plant expression in terms of codon usage. In an orthogonal system where part performance does not depend on the context, any factors related to codon usage, gene length, GC content, or correct 5' cap may affect the magnitudes of the reporter readout, but the comparative profiles of gene expression across the promoter-terminator pairs should remain the same. However, the relative strengths of promoters and terminators changed when the reporter protein was replaced (Fig. 3 , 4 ), indicating that the coding sequence interacted with the promoter and the terminator presumably directly and physically. We wanted to understand how the promoter-coding sequence-terminator interactions occur. There are many possible mechanisms (some examples outlined above), which likely to influence gene activity in combinations. However, to simplify, such interactions can be explained either at the DNA or RNA level – or transcriptional or post-transcriptional mechanisms. The qPCR assay indicated that the reporter genes activity was regulated transcriptionally by controlling the stable transcript abundance, but also post-transcriptionally as the transcript abundance alone did not always correlate with the reporter activity (Fig. 4 ). To explore the mechanisms behind the transcriptional regulation, we surveyed DNA sequence features potentially involved in determining terminator or promoter activity. The terminators with consistently strong reporter protein readout ( HSP and FAD2 ) also had high mRNA levels (Fig. 5 a). These two terminators contain a local drop (50 bp) in the GC content, that was absent in most other terminators. It is likely that this low GC region holds other structural features that aid the recognition of transcription termination and cleavage. When other possible motifs were searched for a priori in HSP and FAD2 but not in other MAPS terminators , the XSTREME motif discovery tool identified four motifs. Among the four, CAAAUGUUUGUGUC motif at location 145 bp in HSP and 146 bp in FAD2 , may aid with cleavage site recognition and cleavage efficiency, enhancing overall terminator strength (Fig. 7 b,c). With our deletion series, we have seen that only the shortest FAD2 terminator (70 bp) has significantly reduced strength compared to the full-length version (240 bp). The first 35 bp of the HSP terminator has a strength statistically nondifferent compared to the full-length (Fig. 5 c,d); this is consistent with Felippes et al . 72 , which showed that the first 32 bp of the HSP terminator is a transferable element that enhances the strength of other terminators. A very short (< 50 bp), minimal terminator domain may govern much of the terminator activity, especially in transient expression systems, whereas the specific features identified (Fig. 5 ) may play clearer roles in longer timescales or certain physiological or developmental contexts. Recent studies revealed the roles of RNA structures in gene expression control. Lower mRNA folding energies, typically associated with greater structural stability, impact ribosome function and translation in organisms such as E. coli and S. cerevisiae 73 , 74 . Beyond global folding, local secondary structures can also play a pivotal role in shaping gene expression. Pseudoknots, hairpins, loops, and the presence of hydrogen bonding patterns upstream of the transcription start site have been shown to reduce ribosome sequestration and translation rates 75 , 76 . A study with 224,000 synthetic sequences in E. coli has highlighted the importance of secondary structures at the 30 bp region upstream and downstream of the start codon (referred to as STR -30:+30 ) 77 . While comprehensive studies in plants are still pending, in vivo genome-wide profiling of Arabidopsis RNA secondary structures revealed that unstructured regions upstream of the start codon are enriched in high translation efficiency mRNAs 47 . In the strong promoter UBQ10 , we found an unstructured TSS (loop) where altering its structure affected reporter output (Fig. 6 ). RNA secondary structures also point to a similar regulatory influence in terminator-dependent gene regulation. When the consistently strong terminators HSP and FAD2 were combined with various promoters, loops of > 20 bp were predicted (Fig. 7 ). This might be a structural feature of strong-acting terminators, since a similar > 20 bp loop was predicted when the variable NOS terminator was combined with strong promoters ( MAS, UBQ10); yet the loop is thought to re-configure to a stem-loop structure when combined with weaker promoters. Expanding on these structural themes, we examined the folding structure of the terminators from Felippes et al . 71 that showed increased strength when combined with the first 32 bp of the HSP terminator. The predicted folding of the transcript showed that the addition of the 32 bp led to an increase in the size of the loops > 20 bp for the NOS and ACS2 terminators, as well as higher base-pairing probability structures for Rbcs1A ( Supplementary Fig. S9 ). RNA folding patterns predicted the consistent activity promoter and terminator parts tend to be isolated in terms of secondary structure, displaying a low level of physical interactions from the other parts, regardless of their sequences (Fig. 7 , Supplementary Fig. S8 ). Additionally, in the case of the NOS terminator, cross-part interactions could explain the high variability. It is plausible that certain promoters and terminators possess currently unknown regulatory elements that enhance their activity while physically restricting their interactions with other parts. Meanwhile, there may be TU parts that may require physical interactions with other parts to function effectively. For example, in mammalian systems, terminator elements require specific secondary structures to interact between the polyadenylation complex and mRNA 78 . Synthetic parts with known elements, like the pOp6 and LexA inducible promoters, likely hold key to context-independency. Even though their transcripts were predicted to fold, generating cross-fold interactions, such interaction did not seem to have a strong effect on the reporter output ( Supplementary Fig. S8 ). CONCLUSION We developed a new, simple and versatile synthetic biology toolkit for plant genetic engineering, Mobius Assembly for Plant Systems. In so doing, we unravelled new insights into gene regulation using a bottom-up approach and have shown that gene expression is regulated by all parts of a transcriptional unit combinatorically. Uncovering the relationship between the structure and function of RNA (and DNA) will help establish the principles for designing consistent and predictive standard parts that are independent of the nucleotide context-independent and more orthogonal. MATERIALS AND METHODS Bacterial strains and growth conditions E. coli strains JM109, and NEB stable were used. E. coli chemically competent cells were prepared in-house using the TSS preparation described by Chung & Miller 79 . E. coli cells were incubated at 37°C (30°C for NEB stable), 200 min-1 either in 5 ml (for a high copy) or 10 ml (for a low copy), or 100 ml (for midi prep) in LB growth medium supplemented with antibiotics. Cells bearing the LhGR-pOp6 and sXVE-lexA inducible systems were grown for 24 h instead due to their slower growth rate. Bacterial transformations and plasmid isolation For E. coli transformation, 5 µl of the plasmid DNA was incubated with 100 µl of the competent cells on ice for 30 min, followed by a heat shock at 42°C for 90 sec (30 sec for TOP10) and re-cooled on ice for 5 min. S.O.C medium (400 µl) was added, and after 1 h incubation at 37°C, 100 µl of the cell suspension was plated on LB agar plates with antibiotic selection. Plasmids were isolated using Monarch (NEB) Plasmid Miniprep Kits. The GeneJET Plasmid Midiprep Kit (ThermoFisher) were used for higher yields. MAPS vector construction MAPS Level 1 and Level 2 Acceptor Vectors were built using Gibson Assembly. The Mobius Assembly cassettes were amplified from the corresponding E. coli plasmids in the Mobius Assembly Vector toolkit 26 and fused to the plant binary vectors. The pGreen-based Level 1 vectors were constructed using pGreen0029 31 . The NptI gene was replaced with the spectinomycin resistance gene amplified from the pCR8 vector (ThermoFisher) to generate Level 2 vectors. The pLX-based Level 1 and Level 2 vectors were built using pLX-B3 and pLX-B2 33 , respectively. WKS1 Ori was synthesized (Twist Bioscience) into two parts due to repetitive sequences, and pUC Ori was amplified from pUC19, both flanked by BsaI recognition sites. The minimum sequence requirement of pUC for stable replication in E. coli was found to include RNA I/RNA II transcripts on the 5′-side and dnaA/dnaA′ boxes on the 3′-side, while co-directional transcription of two different replicons in the same plasmid was shown to increase transformation efficiency and DNA yield 32 . A pLX Level 1 A vector with the construct NOS:BglR:NOS-UBQ10:nluc:HSP was amplified outside the BBR1 Ori with primers harbouring BsaI recognition sites and fused with pUC and WKS1 Ori. The resulting plasmid was used as the template to amplify the pUC-WKS1 fused Ori, which then replaced BBR1 Ori from the pLX-based Level 1A and Level 2A vectors with Gibson Assembly, resulting in the pMAP Level 1A and Level 2A vectors. The rest of the pMAP vectors were constructed again using isothermal assembly and as a template for the Mobius cloning cassettes, the plasmids from the original Mobius Assembly kit 26 . MethylAble modules were devised with the Gibson Assembly using mUAV as a template. Overlapping primers bearing two outward-facing BsaI sites prone to CpG methylation and suitable standard overhangs were used to amplify mUAV into two parts. The resulting parts were purified and digested with DpnI (ThermoFisher) to eliminate the template DNA, and they were fused in an isothermal reaction. Mobius Assembly and standard part library construction A detailed protocol on Mobius Assembly can be found in 80 . Briefly, the assembly was performed in a one-tube reaction with a total volume of 10 µl, with ~ 50 ng Acceptor Vectors and double amounts of inserts. Reagents added were 1 µl of 1 mg/ml BSA (diluted from 20 mg/ml - NEB), 1 µl T4 DNA ligase buffer (ThermoFisher/NEB), 0.5 µl AarI/PaqCI (ThermoFisher/NEB) and 0.2 µl 50x oligos of the AarI recognition site for Level 0 and Level 2 cloning or Eco31I/BsaI-HFv2 (ThermoFisher/NEB) for Level 1 cloning, and 0.5 µl T4 DNA ligase (ThermoFisher/NEB). The reactions were incubated in a thermocycler for 5–10 cycles of 5 min at 37°C and 10 min at 16°C, followed by 5 min digestion at 37°C and 5 min deactivation at 80°C (5 cycles for Level 0 and the first round of Level 1 cloning – 10 cycles for Level 2 and large constructs > 10 kb). PCR amplification All PCR amplifications for plasmid construction and cloning were performed using Q5® High- Fidelity DNA Polymerase (NEB), followed by purification with Monarch® PCR & DNA Cleanup Kit (NEB). Successful DNA assembly was verified first by colony PCR using GoTaq® Green Master Mix (Promega) and then with double restriction digestion with EcoRI-HF (NEB) and PstI- HF (NEB). The constructs were further verified by Sanger sequencing (GATC Biotech-Eurofins, Edinburgh Genomics and Source BioScience) and whole plasmid sequencing (Full Circle). Promoter and terminator part design and generation Likely constitutive promoters and terminators were selected from the literature, especially the genes commonly used as positive controls in qRT-PCR 81 . Their sequences were retrieved from the TAIR webpage 82 ( http://www.arabidopsis.org/index.jsp ) and blasted in NCBI to find the untranslated regions flanking the genes. A 1.5 kb sequence upstream of the start codon was run through the online prediction software and TSSPlant 51 ( http://www.softberry.com ) to identify TATA and TATA-less promoters or enhancer sites. In the promoter selection, it was also considered, when possible, to include the initiator (INR) elements (YYA(+ 1)NWYY- TYA(+ 1)YYN-TYA(+ 1)GGG)) and downstream promoter (DPE) element (RGWYV). Potential promoter elements linked to increased gene expression were identified using PlantCare 83 and PLACE 84 software. Terminators were selected with PASPA, a web server for poly(A) site prediction in plants and algae 85 ( http://bmi.xmu.edu.cn/paspa/interface/run_PASPA.php ). A sequence 300bp downstream of the stop codon was input into PASPA, and the end of the terminator was set at 10bp after the second polyadenylation site, resulting in a sequence of around 200bp. They were then analyzed in RegRNA2.0 to identify other RNA functional motifs 86 ( http://regrna2.mbc.nctu.edu.tw ). The parts designed in the first round were short promoters (~ 300bp) and short terminators (~ 200bp) from the genes ACT2, FAD2, TUB9, APT1, NDUFA8 and LEC2. As the ~ 300 bp promoters were very low in activity (Data not shown), we designed a new set of promoters derived from the genes TUB2, UBQ11, UBQ4, ACT7 , and the longer versions of the previous promoters (~ 500 kb). Appropriate primers compiling to the Phytobrick standard overhangs were designed for both promoters and terminators for cloning into mUAV. Protoplast isolation and transformation Arabidopsis (Wildtype, Col-0) seeds were sown on the soil. After a 2-day pre-treatment at 4°C in darkness, they were sawn and grown under long-day conditions (21°C; 16h light / 8h dark cycles; light intensity ~ 100µmol/m2s-1; humidity 40–65%) until harvest. The protoplast isolation and transformation protocol was developed based on Chupeau et al ., (2013) 40 and Faraco et al ., (2011) 87 . The optimized protocol is descripted in Supplementary Information, with representative images of consistently high transformation ( Supplementary Fig. S4 ). Luciferase assay The 96-well plates containing the transformed protoplasts were let to sediment and 60 µl of supernatant was discarded. The protoplasts were resuspended, and 40 µl was transferred to white optical plates in a grid pattern with empty spaces between wells to reduce luminescence bleed-through. Luciferase activity was assayed in an Omega luminescence plate-reader (Fluostar) with four different gains following the instructions of the Nano Dual-Luciferase® Reporter kit (Promega, N1620). A further correction for luminescence bleed-through and background signal was applied using the software developed by Mauri et al. 88 Then NLuc signal was divided by the FLuc signal to normalize for the transformation efficiency. Fluorescence assay For fluorescent protein assays, TECAN Spark plate reader was used with either 96-well plates (Greiner) or black Thermo Scientific Nunc F96 MicroWell. Excitation and emission for the super-folder green fluorescent protein (sfGFP) was 485 nm and 520 nm, respectively. For mScarlet-I, excitation and emission wavelengths were 560nm and 620nm. Measurements were taken at two different gains to ensure signals don’t overshoot beyond saturation. The sfGFP values were divided by the mScarlet-I values to normalize for the transformation efficiency. Since fluorescence was not always active, unlike luminescence and also because black-walled plates were used to prevent signal bleed-through, deconvolution correction was not applied to the fluorescent samples. 88 RNA isolation and real-time PCR (qRT-PCR) Isolation and transformation were performed as described above, with reaction and solution volumes upscaled by 26 times. 24 h after transformation, the supernatant was discarded, and sedimented protoplasts were harvested for RNA extraction with GeneJET Plant RNA Purification Kit (ThermoFisher). Extracted RNA was treated with DNA-free DNA Removal Kit (Invitrogen). Then, reverse transcription was performed with UltraScript 2.0 cDNA Synthesis Kit (PCR Biosystems). This cDNA synthesis kit uses optimum amounts of oligo (dT) and random hexamers for unbiased amplification of RNA variants. It is advised to use 2 µM of both oligo (dT) and random hexamers for cDNA synthesis when using a different kit. qPCR reaction was set up with primers and probe sequences listed in Supplementary Table S4. Sso Advanced Universal Probes Supermix (Bio-Rad) was used following the manufacturer’s instructions. with the reactions were prepared in MicroAmp Endura 96-well plate and sealed with MicroAmp Optical Adhesive films, and the optical output was measured with StepOnePlus qPCR machine (all Applied Biosystems). A standard curve was included on the plate for each gene target tested; this standard curve was used to calculate cDNA copy number of the samples on the same plate. The FLuc or mScarlet-I cDNA copy number was used for normalization for the transformation efficiency. The resulting values from all the constructs were then normalized to the NLuc cDNA copy number from the UBQ10:NLUC:NOS in the same experiment, to account for any batch-to-batch variations. Functional dissection of the HSP and FAD2 terminators Five length deletion series were constructed for HSP and FAD2 terminators. The deletions removed putative motifs that could affect their function to influence the gene expression level. Putative Stabilization/Destabilization Motifs 8 while the rest were identified using RegRNA2.0 1 and PASPA online tools 2 . Primers were designed to gradually remove functional sequence elements from the 3' end of each terminator through PCR and subsequently cloned to mUAV. Site-directed mutagenesis by Gibson Assembly was employed to mutate the poly-A signal of the HSP Part3, while the same method was used to build HSP Part 5. Transformation of the construct was performed with PEG-transformation of Arabidopsis leaf mesophyll protoplasts in 96-well plates. The expression activity of the constructs was assayed using a plate reader, measuring the Nano luciferase activity normalized by Firefly luciferase. The construct we used was UBQ10:Nluc:Terminator Part:UBQ10:Fluc:UBQ5 , housed in a pMAP vector. RNA folding prediction For mRNA folding predictions, the transcription start site was predicted for each promoter by using TSSPlant 51 . The nucleotide sequence downstream of the TSS to the end of the terminator was used as the mRNA sequence. mRNA folding was predicted by using RNAFold software that uses Vienna RNA package. 50 Thermodynamic ensemble prediction was used for folding energy predictions which accounts for all possible RNA secondary structures weighted by their Boltzmann probabilities, allowing the calculation of base pairing probabilities and ensemble free energy. 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Science 338:671–675 Al Husini N, Kudla P, Ansari A (2013) A Role for CF1A 3′ End Processing Complex in Promoter-Associated Transcription. PLoS Genet 9:e1003722 Mukundan B, Ansari A (2013) Srb5/Med18-mediated Termination of Transcription Is Dependent on Gene Looping. J Biol Chem 288:11384–11394 Medler S, Ansari A (2015) Gene looping facilitates TFIIH kinase-mediated termination of transcription. Sci Rep 5:12586 Pérez-González A, Caro E (2018) Effect of transcription terminator usage on the establishment of transgene transcriptional gene silencing. BMC Res Notes 11:511 Indian Institute of Integrative Medicine, Canal Road CSIR (2020) Jammu-180001 & Singh, R. A report on DNA sequence determinants in gene expression. Bioinformation 16:422 De Felippes FF, Shand K, Waterhouse PM (2022) Identification of a Transferrable Terminator Element That Inhibits Small RNA Production and Improves Transgene Expression Levels. Front Plant Sci 13:877793 Faure G, Ogurtsov AY, Shabalina SA, Koonin E (2017) Adaptation of mRNA structure to control protein folding. RNA Biol 14:1649–1654 Tuller T, Waldman Y, Kupiec M, Ruppin E (2010) Translation efficiency is determined by both codon bias and folding energy. PNAS 107:3645–3650 Kudla G, Murray AW, Tollervey D, Plotkin JB (2009) Coding-Sequence Determinants of Gene Expression in Escherichia coli . Science 324:255–258 Gu W, Zhou T, Wilke CA (2010) Universal Trend of Reduced mRNA Stability near the Translation-Initiation Site in Prokaryotes and Eukaryotes. PLoS Comput Biol 6 Cambray G, Guimaraes JC, Arkin AP (2018) Evaluation of 244,000 synthetic sequences reveals design principles to optimize translation in Escherichia coli. Nat Biotechnol 36:1005–1015 Zarudnaya MI (2003) Downstream elements of mammalian pre-mRNA polyadenylation signals: primary, secondary and higher-order structures. Nucleic Acids Res 31:1375–1386 Chung C, Miller R (1993) Preparation and storage of competent Escherichia coli cells. Methods Enzymol 218:621–627 Andreou A, Nakayama N Mobius Assembly. in DNA Cloning and Assembly: Methods and Protocols vol. 1116 Guenin S et al (2009) Normalization of qRT-PCR data: the necessity of adopting a systematic, experimental conditions-specific, validation of references. J J Experimental Bot 60:487–493 Huala E (2001) The Arabidopsis Information Resource (TAIR): a comprehensive database and web-based information retrieval, analysis, and visualization system for a model plant. Nucleic Acids Res 29:102–105 Lescot M (2002) PlantCARE, a database of plant cis-acting regulatory elements and a portal to tools for in silico analysis of promoter sequences. Nucleic Acids Res 30:325–327 Higo K, Ugawa Y, Iwamoto M, Korenaga T (1999) Plant cis-acting regulatory DNA elements (PLACE) database: 1999. Nucleic Acids Res 27:297–300 Ji G et al (2015) PASPA: a web server for mRNA poly(A) site predictions in plants and algae. Bioinformatics 31:1671–1673 Chang T-H et al (2013) An enhanced computational platform for investigating the roles of regulatory RNA and for identifying functional RNA motifs. BMC Bioinformatics 14:S4 Faraco M, Di Sansebastiano GP, Spelt K, Koes RE, Quattrocchio FM (2011) One Protoplast Is Not the Other! Plant Physiol 156:474–478 Mauri M, Vecchione S, Fritz G (2019) Deconvolution of Luminescence Cross-Talk in High-Throughput Gene Expression Profiling. ACS Synth Biol 8:1361–1370 Additional Declarations There is NO Competing Interest. Supplementary Files MAPSpaperSIfinal.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5118685","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":359028737,"identity":"d94175ed-f9b8-4443-8e35-28e3d3281c08","order_by":0,"name":"Naomi Nakayama","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYJACxgYwxXzgAIhiY2CG8CUIa2FLgGphJFoLjwEqH5cWcwbegx9n1ByW120/8/HQzR02+XzsBxsYftQwJM5swK7FsoEvWXLDscOG287kbjiceybNso0nsYGx5xhD4mwcthgc4DGQfMB2mHHbAZCWtsMGbAyJDQy8DQyJ83BrMf754N9h+23n3zwAavlvwMb/sIHxL34tZpIb2w4nbruRwwDUcsCATSKxgRlkC06HHeYxs5zZl5687cYzA6BfkoFaHjYcljkmYYzL+wbHe4xv9nyztt12Pvnx59wddgby/ckHH76psZGdcQCHNcxgshnCgcYIwwH8EQkGdahaRsEoGAWjYBQgAwAV9mTIUF18UgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-9390-3545","institution":"Imperial College London","correspondingAuthor":true,"prefix":"","firstName":"Naomi","middleName":"","lastName":"Nakayama","suffix":""},{"id":359028738,"identity":"24ecfcb0-8871-4f23-900c-eb09b863a3b6","order_by":1,"name":"Elif Gediz Kocaoglan","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Elif","middleName":"Gediz","lastName":"Kocaoglan","suffix":""},{"id":359028739,"identity":"32c3ba43-ed35-41a8-bd4a-1b1cdadd8886","order_by":2,"name":"Andreas Andreou","email":"","orcid":"","institution":"University of Edinburgh","correspondingAuthor":false,"prefix":"","firstName":"Andreas","middleName":"","lastName":"Andreou","suffix":""},{"id":359028740,"identity":"70a1ffdb-b70e-4cdb-a9a5-d53b7e12c350","order_by":3,"name":"Jessica Nirkko","email":"","orcid":"","institution":"University of Edinburgh","correspondingAuthor":false,"prefix":"","firstName":"Jessica","middleName":"","lastName":"Nirkko","suffix":""},{"id":359028741,"identity":"890260d6-c12c-4f7c-98fa-c341f431c06a","order_by":4,"name":"Marisol Villarreal","email":"","orcid":"","institution":"University of Edinburgh","correspondingAuthor":false,"prefix":"","firstName":"Marisol","middleName":"","lastName":"Villarreal","suffix":""},{"id":359028742,"identity":"99c26bac-aa29-441d-92ea-e302fc5d34de","order_by":5,"name":"Gary Loake","email":"","orcid":"","institution":"University of Edinburgh","correspondingAuthor":false,"prefix":"","firstName":"Gary","middleName":"","lastName":"Loake","suffix":""}],"badges":[],"createdAt":"2024-09-19 17:15:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5118685/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5118685/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65404298,"identity":"9ec9d009-35a6-44a8-b11f-ad05ec91ffe0","added_by":"auto","created_at":"2024-09-27 04:18:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5417892,"visible":true,"origin":"","legend":"\u003ch3\u003eMobius Assembly for Plant Systems (MAPS) vector structure and workflow.\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003eSchematics of how Mobius Assembly operates. MAPS is an adaptation of the Mobius Assembly for plant systems based on small binary vectors intended for plant transformation. The core vector toolkit is comprised of one part storage vector in Level 0 (mUAV), four Level 1 Acceptor Vectors, four Level 2 Acceptor Vectors, and seven Auxiliary plasmids providing linkers. The vectors release the insert with \u003cem\u003eBsaI \u003c/em\u003ein Level 1 cloning and the rare cutter \u003cem\u003eAarI (PaqCI) \u003c/em\u003ein Level 0 and Level 2 cloning. \u003cstrong\u003eb\u003c/strong\u003e Plasmid design of MAPS vectors, showing a Level 1 plasmid as an example. At Level 2, \u003cem\u003eBsaI \u003c/em\u003eand\u003cem\u003eAaRI\u003c/em\u003e (\u003cem\u003ePaqCI\u003c/em\u003e) cut sites swap places. \u003cstrong\u003ec. \u003c/strong\u003eSummary of the markers, antibiotic resistance and RE enzymes used for Mobius Assembly at different Levels. \u003cem\u003eCmR\u003c/em\u003e is a gene conferring the resistance for chloramphenicol, \u003cem\u003eKanR\u003c/em\u003e for kanamycin and \u003cem\u003eSpecR\u003c/em\u003efor spectinomycin. For simplicity, the overhangs carried from Level 0 are not shown in the Level 2 construct.\u003c/p\u003e","description":"","filename":"MAPSFIG1.png","url":"https://assets-eu.researchsquare.com/files/rs-5118685/v1/82fe73725f990f3bdfa78882.png"},{"id":65405188,"identity":"a99e2afb-4f40-408f-80f9-877855c7873a","added_by":"auto","created_at":"2024-09-27 04:34:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":264900,"visible":true,"origin":"","legend":"\u003cp\u003ePromoter/terminator part characterization underscores terminator-mediated gene regulation.\u003c/p\u003e\n\u003cp\u003ePromoter characterization is shown on the top panels (a-c),the terminator characterization on the middle panels (d-g) and inducible systems on the bottom panels (h-j). The promoter library (a-c) was characterized either with the \u003cem\u003eHSP \u003c/em\u003e(a,c) or \u003cem\u003eNDUFA8 \u003c/em\u003e(b) terminator. The terminator library (d-g) was examined either with the \u003cem\u003eUBQ10 \u003c/em\u003e(d,f), \u003cem\u003eNDUFA8 \u003c/em\u003e(e) or \u003cem\u003eMAS\u003c/em\u003e (g) promoters. Inducible systems were induced using (h) 2.5μM Dex (\u003cem\u003epOp6-35S\u003c/em\u003e), (i) 5μM estradiol (\u003cem\u003elexA-35S\u003c/em\u003e), (j) or 0.1% EtOH (\u003cem\u003ealcSynth\u003c/em\u003e). Arabidopsis mesophyll protoplasts were transformed with PEG using an optimized transient expression protocol. Relative Light Unit (RLU) refers to the NLuc readout divided by the FLuc readout for a,b,d,e,h-j. RFU (Relative Fluorescence Unit) is the sfGFP readout divided by the mScarlet-I readout for c,f,g. The bar graphs show RLU or RFU in mean ±SE. The promoter and terminator parts are colour-coded according to the activity strengths in the first characterization series (a for promoters; d for terminators). The negative control (NC) consisted of untransformed protoplasts (no DNA added). For luciferase systems, NC values were subtracted from measured sample reading during deconvolution, making the NC values 0. For fluorescence samples, deconvolution was not performed, and NC values are displayed. For inducible systems, a first deconvolution round was performed to account for background autofluorescence, and then uninduced \u003cem\u003eHSP\u003c/em\u003eterminator constructs were used as a control for tightness of gene expression. Data points are from three biological replicates, each with three technical replicates.\u003c/p\u003e\n\u003ch3\u003e\u003cbr\u003e\u003c/h3\u003e","description":"","filename":"MAPSFIG2.png","url":"https://assets-eu.researchsquare.com/files/rs-5118685/v1/9749255a7535f06acaf8e66f.png"},{"id":65405030,"identity":"f8fd7919-ae3a-4181-b499-f6838853ca5b","added_by":"auto","created_at":"2024-09-27 04:26:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":681577,"visible":true,"origin":"","legend":"\u003cp\u003eCombinatorial gene regulation by promoters, coding sequences, and terminators.\u003c/p\u003e\n\u003cp\u003eSummary of the reporter expression levels measured for the promoter-coding sequence-terminator combinations.\u003cstrong\u003e a.\u003c/strong\u003e Heatmap showing all sample comparisons. The reporter gene expression levels were compared across the luciferase samples and the fluorescent protein samples separately. This highlighted that some promoters are dominant and consistent in gene expression strength (\u003cem\u003ee.g\u003c/em\u003e., \u003cem\u003eNDUFA8\u003c/em\u003e,\u003cem\u003e pOp6\u003c/em\u003e). \u003cstrong\u003eb.\u003c/strong\u003e Heatmap showing the relative strength among the terminators. The color gradient is set independently in each promoter series; dark blue, light blue, and red denote low, mid-level, and high expression, respectively. Dark blue denotes the lowest expression, light blue mid-level, and dark red the highest.\u003c/p\u003e","description":"","filename":"MAPSFIG3.png","url":"https://assets-eu.researchsquare.com/files/rs-5118685/v1/e2053e594f1258906d3a5e53.png"},{"id":65405031,"identity":"4d0c6906-b90c-47d1-aaad-fdae35b82672","added_by":"auto","created_at":"2024-09-27 04:26:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":109633,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptional or post-transcriptional regulation underlie combinatorial gene expression control\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mRNA and protein expression levels were compared to differentiate transcriptional or post-translational regulation in different promoter-coding sequence-terminator combinations. \u003cstrong\u003ea \u003c/strong\u003eTranscriptional regulation: mRNA level was quantitated by qPCR for \u003cem\u003eNLuc\u003c/em\u003e or \u003cem\u003esfGFP\u003c/em\u003e gene expression, under \u003cem\u003eUBQ10\u003c/em\u003e promoter and different terminators. \u003cem\u003eUBQ10:FLuc:UBQ5\u003c/em\u003ewas used as the normalizing construct for \u003cem\u003eNLuc\u003c/em\u003e, and \u003cem\u003eUBQ10:mScarlet-I:UBQ5 \u003c/em\u003efor \u003cem\u003esfGFP\u003c/em\u003e. One-way ANOVA was applied for the statistical analysis\u003cstrong\u003e. \u003c/strong\u003eAll the data points from three biological replicates are shown on the graph as dots.\u003cstrong\u003e b,c\u003c/strong\u003e Correlation of the reporter protein activity to the mRNA levels of different terminator constructs:\u003cstrong\u003e b \u003c/strong\u003eNLuc and \u003cstrong\u003ec\u003c/strong\u003e sfGFP. Crossing lines indicate the mean ± SE. \u003cstrong\u003ed,c\u003c/strong\u003e Correlation of predicted RNA folding energy to \u003cstrong\u003ed\u003c/strong\u003e mRNA or \u003cstrong\u003ee \u003c/strong\u003ereporter protein activity level. Blue circlels represent \u003cem\u003eNLuc\u003c/em\u003e constructs, and green circles \u003cem\u003esfGFP\u003c/em\u003e constructs. The folding energy prediction values are available in \u003cstrong\u003eSupplementary Table S5.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"MAPSFIG4.png","url":"https://assets-eu.researchsquare.com/files/rs-5118685/v1/c266eb4d2599e5627fadf551.png"},{"id":65404301,"identity":"94c2bd14-a293-4356-b4d9-f99d53340aee","added_by":"auto","created_at":"2024-09-27 04:18:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":279768,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTerminator features that may affect gene expression.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003eGC content of the terminators in the toolkit with 25 bp windows. The shade of the blue indicates the GC content (the darker, the higher). Consistently strong terminators \u003cem\u003eHSP \u003c/em\u003eand\u003cem\u003e FAD2\u003c/em\u003e, have a local GC drop of 50-bp that correlates with their dominant Poly-A site (red asterisks). \u003cstrong\u003eb \u003c/strong\u003e\u003cem\u003eA priori\u003c/em\u003e identification of motifs in consistently strong terminators. The XSTREME software identified four motifs that are present only in the \u003cem\u003eHSP-t\u003c/em\u003e and \u003cem\u003eFAD2-t\u003c/em\u003e terminators. The first identified motif, CAAAUGUUUGUGUC, possibly correlates with high cleave efficiency. \u003cstrong\u003ec\u003c/strong\u003e Structure, GC content, and functional motifs in the \u003cem\u003eHSP\u003c/em\u003e and \u003cem\u003eFAD2\u003c/em\u003eterminators. \u003cstrong\u003ec,d.\u003c/strong\u003e Deletion series of the \u003cem\u003eHSP \u003c/em\u003eterminator. \u003cstrong\u003ee,f.\u003c/strong\u003eDeletion series of the \u003cem\u003eFAD2 \u003c/em\u003eterminator. Schematics of terminator features (\u003cstrong\u003ec,e\u003c/strong\u003e) and the reporter gene activity (\u003cstrong\u003ed,f\u003c/strong\u003e) are shown. Relative light unit (RLU) is obtained by normalizing Nluc reporter values by Fluc values. Bar graphs show luciferase activity values in mean ± SE. p \u0026lt; 0.05, one-way ANOVA and Tukey's HSD test. Data points are from three biological replicates, each with three technical replicates.\u003c/p\u003e","description":"","filename":"MAPSFIG5.png","url":"https://assets-eu.researchsquare.com/files/rs-5118685/v1/e351e0067192e5997dd1afc0.png"},{"id":65404296,"identity":"0172bca1-bdf6-45df-86d3-2672c59441b6","added_by":"auto","created_at":"2024-09-27 04:18:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":451888,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOpen structure at \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eUBQ10\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003epromoter TSS affects gene expression.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStrong promoters, such as \u003cem\u003eUBQ10\u003c/em\u003e, tend to have an open loop structure at the TSS. To test the significance of this structural signature, point mutations were introduced to form closed stem-loop structures instead. \u003cstrong\u003ea\u003c/strong\u003e Four TSS variants were tested: Wild Type (WT), Mutated control (MUTC), Mutated 1 (MUT1), and Mutated 2 (MUT2), and \u003cstrong\u003eb\u003c/strong\u003e their predicted RNA folding structures. \u003cstrong\u003ec \u003c/strong\u003eOpen loop structure at TSS enhances gene expression. \u003cem\u003esfGFP\u003c/em\u003e was expressed under \u003cem\u003eUBQ10 \u003c/em\u003econtaining WT, MUTC, MUT1, or MUT2 variant of TSS. Five different terminators were chosen to assess the effect: \u003cem\u003eHSP, FAD2, 35S, NOS, \u003c/em\u003eand\u003cem\u003e RbcS2b\u003c/em\u003e. The normalization construct was \u003cem\u003eUBQ10:mScarlet-I:UBQ5\u003c/em\u003e for all samples. Bar graphs show mean reporter activity values ± SE. Negative control (NC) was untransformed protoplasts. One-way ANOVA with Tukey’s HSD was done individually for each terminator category.\u003c/p\u003e","description":"","filename":"MAPSFIG6.png","url":"https://assets-eu.researchsquare.com/files/rs-5118685/v1/06dcb50af462baf0887615f7.png"},{"id":65404294,"identity":"8861aa40-769c-4047-9b33-7904cade1ba0","added_by":"auto","created_at":"2024-09-27 04:18:13","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":662961,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural interaction patterns in the transcript RNA sequences\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA folding was visualized to show the likelihood of intra-transcript nucleotide interactions. The promoter part corresponds to the TSS of the \u003cem\u003eMAS, UBQ10\u003c/em\u003e and \u003cem\u003eNDUFA8 \u003c/em\u003epromoters. Terminator strength is represented as a blue gradient, where dark blue represents strong and light blue weak; terminators whose strength shifts depending on the promoters are marked with mixed shades. Promoter strength is represented similarly, where dark to light red corresponds to strong to weak. Interaction levels are categorized as none (empty squares), weak (one square full), medium (two squares full), and strong (three squares full). For none, there is no base pairing predicted between promoter and terminator parts; for weak, there is only base pairing with blue and green colours, which means it is \u0026lt;0.5 probable; for medium, there is base pairing in yellow and orange colours, which means 0.75 probability, or 5 bp with red base pairing, indicating ~1.0 probability. Strong interactions are defined as \u0026gt;5 red base pairs.\u003c/p\u003e","description":"","filename":"MAPSFIG7.png","url":"https://assets-eu.researchsquare.com/files/rs-5118685/v1/3b003f74f57c542564ab900c.png"},{"id":66716220,"identity":"08d13b6b-6dfa-4493-998d-f8925eba8cf4","added_by":"auto","created_at":"2024-10-15 20:08:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8842229,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5118685/v1/76eeeaa9-f421-4354-9ddd-94cf06c5c0a3.pdf"},{"id":65404300,"identity":"04663b7f-3a7a-4be9-92c1-afe698970281","added_by":"auto","created_at":"2024-09-27 04:18:13","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":2984222,"visible":true,"origin":"","legend":"","description":"","filename":"MAPSpaperSIfinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-5118685/v1/3c74368185377efbf1171ed5.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Mobius Assembly for Plant Systems highlights promoter-coding sequences-terminator interaction in gene regulation","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThroughout the history of civilization, plants have been the primary culturing platform for food, energy, materials, and drugs. However, in synthetic and engineering biology applications, plants have remained a minor vehicle for bioproduction with untapped potential. Consequently, the molecular tools and technologies for rational engineering of plant systems are still limited compared to their microbial and mammalian counterparts. With the increasing world population and ongoing climate crisis, considerately designed plant engineering holds the key to a sustainable future, creating transformative solutions for a wide range of applications from agriculture to environmental regeneration\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. From enhancing crop yields, quality, and resilience to improving carbon fixation and biofuel efficiency, synthetic biology-based plant engineering is becoming a powerful catalyst to futureproof our society and planet\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSynthetic biology enables complex pathway engineering necessary for modifications of biological metabolisms, cellular and developmental pathways, or responses to pathogens and diseases. The foundation of synthetic biological genetic engineering is the engineering principles, such as standardization, modularity, and design-build-test cycles\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. However, as the field matures past its first two decades, biology has often defied these fundamental assumptions. The concept of orthogonality \u0026ndash; context-independent, universal functions of genetic parts \u0026ndash; is routinely challenged, uncovering new insights and understanding of how biological systems interact\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. As this can be the case for just a single gene, it poses a significant hurdle when working with the many genes necessary for pathway and network engineering.\u003c/p\u003e \u003cp\u003eThe development of synthetic biology tools and technologies for multigene engineering starts with creating a collection (library) of standardized genetic parts. Standardized parts (e.g., Phytobricks)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e include promoters, coding sequences, terminators, and functional protein tags, which together form transcriptional units (TUs). Promoters, which contain the transcription start site (TSS) where RNA polymerase binds, are well established for their instructive roles in regulating gene activity. In comparison, terminators, typically found at the end of a gene, are often underestimated in their regulatory roles. However, they have been shown to influence transcription by controlling transcription arrest, mRNA stability, protecting genes against silencing and tuning other transcription functions\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe most commonly used promoters and terminators in plant genetic engineering originally stemmed from Cauliflower Mosaic Virus (\u003cem\u003e35S\u003c/em\u003e), \u003cem\u003eRhizobium\u003c/em\u003e opine genes (\u003cem\u003eNOS, MAS\u003c/em\u003e, and \u003cem\u003eOCS\u003c/em\u003e), and more recently, plant genomes (e.g., \u003cem\u003eUBQ10\u003c/em\u003e promoter and \u003cem\u003eHSP\u003c/em\u003e terminator from \u003cem\u003eArabidopsis thaliana\u003c/em\u003e or hereafter Arabidopsis). The shortage of well-characterized regulatory parts results in repeated use of the same sequences in multigene construction, increasing the possibility of homologous sequence-dependent recombination and gene silencing\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In addition, there is a need for shorter standard parts, especially when multiple genes are delivered in a single vector, to reduce the size of the construct. Large constructs tend to make plasmids structurally unstable\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e and decrease the efficiency of \u003cem\u003eRhizobium\u003c/em\u003e-mediated transformation\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Therefore, creating novel and shorter promoter and terminator parts will enrich the plant genetic engineering toolkit available to the community and increase our capacity to create genetic modifications.\u003c/p\u003e \u003cp\u003eOnce standard genetic parts are made, their activity level should be assessed. This typically involves combining a promoter and terminator pair with a reporter protein coding sequence and quantifying the reporter expression as output. While promoters are generally more well characterized, recently, attention has also been directed towards terminators; diverse arrays of terminators have been characterized in bacteria\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e and yeast\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. For plant systems, the number of available terminator parts was limited for a long time, with their characterization typically performed using the \u003cem\u003eNicotiana benthamiana\u003c/em\u003e leaf infiltration system\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. It is worth noting that different species, or even different expression systems within the same species, may exhibit varying levels of expression for the same TU\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Therefore, to achieve higher stability and predictability, new standard parts should be characterized in different combinations and chassis environments to account for potential context dependency.\u003c/p\u003e \u003cp\u003eAs the number of constructs to build and characterize increases, molecular construction needs to become more efficient. Golden Gate is a widely used technology for DNA assembly that utilizes Type IIS restriction endonucleases that cut DNA outside their recognition sequences, allowing the introduction of short \u0026lsquo;sticky end\u0026rsquo; sequences (syntaxis) that help orient and combine multiple DNA fragments in an intended order. Many Golden Gate frameworks have been developed for diverse species\u003csup\u003e\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e; recent developments in the plant field include Loop Assembly\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, an extension of the MoClo toolkit for plants\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, and Joint Modular Cloning\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. We have previously developed a new Golden Gate framework\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, named Mobius Assembly, for its iterative cloning strategy between two vector sets; it allows theoretically infinite assembly of up to four DNA fragments each time. It is designed to be simple, versatile, and efficient (e.g., less need for domestication), complying with the universal and popularly employed the syntaxis design Phytobrick\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn plant transgenic engineering, binary vectors house transgenes and facilitate their delivery and incorporation into the recipient genomes. Although functional, these vectors could be improved to enhance transformation efficiency and stability. A typical \u003cem\u003eRhizobium\u003c/em\u003e-mediated transformation vector includes T-DNA borders\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, replication functions for \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eRhizobium\u003c/em\u003e, and selection markers. Plasmid instability is a significant challenge, especially for plant systems, as vectors must be stable in both \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eRhizobium\u003c/em\u003e. Factors such as size\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, copy number\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, direct repeats12 and inverted repeats\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e affect plasmid stability. Traditional vectors are bulky (\u0026gt;\u0026thinsp;6 kb), but smaller backbones like pGreen (2.5 kb\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e), pLSU (4.6 kb\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e), and pLX (3.3 kb\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e) have been developed to reduce instability. The pLX vectors use a modular design with minimal functional parts and stability features, improving binary vector construction.\u003c/p\u003e \u003cp\u003eHere, we report the adaptation of Mobius Assembly for plant species engineering: Mobius Assembly for Plant Systems (MAPS). The MAPS toolkit includes new, compact plant binary vectors (pMAPs). These vectors are 3.6 kb in size, structurally stable through molecular construction, and suitable for transformation methods requiring high plasmid DNA amounts. MAPS also features bioluminescence and fluorescent protein reporters, along with new standardized promoter and terminator parts for a range of gene expression strengths. In high-throughput transient expression tests with \u003cem\u003eArabidopsis thaliana\u003c/em\u003e protoplasts, MAPS promoters effectively drove gene activation gradients. We also demonstrated that terminators can be used to control gene expression levels. The strength of the promoter and terminator parts depended on their combinations, and such interactions extended to the coding sequence selection. These findings facilitate a bottom-up understanding of gene regulation and the design of context-independent molecular constructs with more predictable outputs.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eDevelopment of Mobius Assembly for Plant Systems (MAPS)\u003c/h2\u003e\n\u003cp\u003eMAPS is an extension of our molecular cloning framework Mobius Assembly\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e to enable transformation and expression of transgenes in plant systems. MAPS has a universal acceptor vector (mUAV) at Level 0 to house a standard part in the Phytobrick format. Level 0 parts are combined at Level 1, and up to four Level 1 constructs are then combined to make Level 2 constructs. MAPS follows a linear cloning strategy until Level 2 and then iterates between two cloning levels (Level 1 and Level 2) for quadruple augmentation of cloning units each time (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea). Using the rare cutter \u003cem\u003eAarI (PaqCI)\u003c/em\u003e, as opposed to frequently used restriction enzymes that recognize shorter sequences (e.g., \u003cem\u003eBsmBI\u003c/em\u003e or \u003cem\u003eBpiI\u003c/em\u003e), reduces the need for removing internal restriction sites (\u003cem\u003ei.e.\u003c/em\u003e, domestication).\u003c/p\u003e\n\u003cp\u003eInitially, we developed pGreen-based vectors but encountered issues with large constructs consistent with reported instability issues\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. To address this, we created a new small plant binary vector called pMAP, based on the pLX architecture (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb). This vector is suitable for transient expression in protoplasts, cell culture, tissues/organs, and whole-plant stable transformation. We devised a new origin of replication by fusing pWKS1 and pUC19 Ori. The pWKS1 Ori, derived from \u003cem\u003eParacoccus pantotrophus\u003c/em\u003e DSM 11072\u003csup\u003e36\u003c/sup\u003e, is functional in \u003cem\u003eRhizobium\u003c/em\u003e but not in \u003cem\u003eE. coli\u003c/em\u003e, so we fused it with the minimal stable pUC Ori from pUC19. pMAPS also has two Left Border (LB) sequences instead of one. \u003cem\u003eRhizobium\u003c/em\u003e gene transfer is from the Right Border (RB) to the LB, and having two LBs suppresses backbone transfer to the plant genome\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Another feature of pMAPS is that terminators flank LB and RB sequences to isolate transgene activity from the plasmid backbone. Colourific markers, antibiotics selections, and restriction enzymes used for Mobius Assembly at different levels are summarized in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec.\u003c/p\u003e\n\u003cp\u003eMAPS vector toolkit consists of a core set of pMAP cloning/destination vectors (Level 1 and Level 2 Acceptor Vectors, four variations \u0026Alpha;-\u0026Delta; for each level), which have a fusion origin of replication to replicate in \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eRhizobium\u003c/em\u003e. The mUAV and the seven Auxiliary plasmids are also included, as described in the original Mobius Assembly kit\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The MAPS toolkit also contains a selection of plant promoters, terminators, antibiotic resistance genes, and visible reporter genes (bioluminescence and fluorescent proteins). All MAPS plasmids are listed in \u003cstrong\u003eSupplementary Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplemental Tables S1, S2\u003c/strong\u003e and available through AddGene (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.addgene.org/browse/article/28211394/\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eReflecting on user feedback, two specific improvements were made to improve the Mobius Assembly vectors. A few users of the original Mobius Assembly kit indicated that the chromoprotein selection had been lost in their clones. Upon investigation, we observed independent events of transposon insertion in the promoter of the chromoprotein genes, which we hypothesized is a response to the stress imposed by chromoprotein production (\u003cstrong\u003eSupplementary Fig. S2).\u003c/strong\u003e To solve the problem, we replaced the marker chromogenic protein (spisPINK) with a red fluorescent protein (mScarlet-I). (\u003cstrong\u003eSupplementary Fig. S2).\u003c/strong\u003e We also noticed some Mobius Assembly clones showed growth retardation in the selection media and identified the instability was caused by plasmid dimerization. Therefore, we introduced a 240-bp \u003cem\u003ecer\u003c/em\u003e domain, which recognizes dimers and triggers recombination help keep the plasmids in the monomeric state (\u003cstrong\u003eSupplementary Fig. S3\u003c/strong\u003e)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eCombinatorial DNA libraries are crucial for part characterization, as well as in applications such as biosynthetic pathway optimization\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, but their manual construction is time-consuming and resource-intensive. To aid combinatorial library construction, we developed the 'MethylAble' feature in Mobius Assembly, allowing standard part variants (Level 0) to be introduced to specific sites in single or multi-gene constructs (\u003cstrong\u003eSupplementary Fig. S4)\u003c/strong\u003e. MethylAble utilizes the DNA methylation sensitivity of \u003cem\u003eBsaI\u003c/em\u003e to mask its recognition sites by cytosine methylation during Level 1 cloning. We designed an \u003cem\u003eamilCP\u003c/em\u003e expression cassette with divergent and convergent \u003cem\u003eBsaI\u003c/em\u003e recognition sites, where CpG methylation blocks \u003cem\u003eBsaI\u003c/em\u003e digestion only at divergent sites, allowing insertion of Level 0 parts into premade Level 1 constructs. Correct constructs show a purple color from \u003cem\u003eamilCP\u003c/em\u003e until the Level 0 parts replace the cassette. As a proof of concept, the MethylAble protocol was used to build the library of the three inducible promoters (see below), each of which was combined with the 14 terminator coparts, making 42 constructs in total (\u003cstrong\u003eSupplemental Table S3\u003c/strong\u003e). MethylAble presents a novel strategy to create construct libraries and can be implemented in all Golden Gate frameworks in which \u003cem\u003eBsaI\u003c/em\u003e enzyme is used, not only in Mobius Assembly.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eDesigning, building, and testing MAPS promoter/terminator standard parts\u003c/h3\u003e\n\u003cp\u003eTo select new \u0026lsquo;constitutive\u0026rsquo; promoter and terminator parts, we chose ubiquitously expressed genes that are likely to have strong expression in different tissue types \u003cstrong\u003e(Supplementary Table S2).\u003c/strong\u003e The promoters and terminators were characterized with a transient gene expression assay based on Arabidopsis mesophyll protoplasts and PEG transformation. We optimized the parameters throughout the protocol based on\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e to improve the transformation efficiency and reproducibility. We were able to reach up to 70% transformation efficiency consistently (\u003cstrong\u003eSupplementary Fig. S5\u003c/strong\u003e), which was high enough to adapt to plate reader measurements in a 96-well format.\u003c/p\u003e\n\u003cp\u003eTo evaluate the promoter/terminator activity levels, we used a dual luciferase system with highly sensitive nano luciferase (NLuc) as the reporter and firefly luciferase (FLuc) for normalization\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. To account for possible batch-to-batch differences in overall protoplast transformation rates/efficiency, we included \u003cem\u003eFLuc\u003c/em\u003e gene (\u003cem\u003eUBQ10-FLuc:UBQ5\u003c/em\u003e) in each construct and calculated the NLuc/FLuc ratio as RLU (Relative Light Unit).\u003c/p\u003e\n\u003cp\u003eFor the promoter testing, seventeen promoters drove \u003cem\u003eNLuc\u003c/em\u003e expression, with termination by either the \u003cem\u003eNDUFA8\u003c/em\u003e or \u003cem\u003eHSP\u003c/em\u003e terminator (\u003cem\u003ePromoter:NLuc:HSP/NDUFA8\u003c/em\u003e). The \u003cem\u003eUBQ10\u003c/em\u003e promoter exhibited by far the highest expression activity among the promoters, followed by \u003cem\u003eMAS\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea,b). The \u003cem\u003eHSP\u003c/em\u003e terminator increased gene expression for all promoters except \u003cem\u003eTUB9\u003c/em\u003e. Two of the newly isolated promoters, \u003cem\u003eUBQ11\u003c/em\u003e and \u003cem\u003eUBQ4\u003c/em\u003e, matched or exceeded the activity of the \u003cem\u003e35S\u003c/em\u003e and \u003cem\u003eOCS\u003c/em\u003e promoters. Furthermore, the newly isolated promoters \u003cem\u003eACT7\u003c/em\u003e, \u003cem\u003eTUB2\u003c/em\u003e, \u003cem\u003eTUB9\u003c/em\u003e, \u003cem\u003eAPT1\u003c/em\u003e, \u003cem\u003eACT2\u003c/em\u003e and \u003cem\u003eLEC2\u003c/em\u003e outperformed the commonly used the \u003cem\u003eNOS\u003c/em\u003e promoter. The \u003cem\u003eFAD2\u003c/em\u003e and \u003cem\u003eNDUFA8\u003c/em\u003e promoters had the lowest expression.\u003c/p\u003e\n\u003cp\u003eFor the terminator evaluation, the \u003cem\u003eNLuc\u003c/em\u003e expression was driven by a strong (\u003cem\u003eUBQ10\u003c/em\u003e) or weak (\u003cem\u003eNDUFA8\u003c/em\u003e) promoter and one of the 14 terminators (\u003cem\u003eUBQ10/NDUFA8:NLuc:Terminators\u003c/em\u003e). The luciferase expression varied by 5.3\u0026ndash;6.3 fold for the \u003cem\u003eUBQ10\u003c/em\u003e and \u003cem\u003eNDUFA8\u003c/em\u003e promoters, respectively, depending on the terminators they were paired with (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed,e). For the strong \u003cem\u003eUBQ10\u003c/em\u003e promoter, the \u003cem\u003eFAD2\u003c/em\u003e terminator had the highest activity (547.9 RLU), while the \u003cem\u003eNOS\u003c/em\u003e terminator had the lowest (103.9 RLU). For the weak \u003cem\u003eNDUFA8\u003c/em\u003e promoter, the \u003cem\u003eHSP\u003c/em\u003e terminator led to the highest expression (0.327 RLU) and \u003cem\u003eAPT1\u003c/em\u003e to the lowest (0.052 RLU).Since it was surprising to see a wide range of gene expression levels led by different terminators under the same promoter, we extended terminator characterization with the three chemically inducible systems popularly used in plant sciences: dexamethasone (Dex), estradiol, and ethanol inducible systems\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. They are all based on two-component mechanisms involving at least two transcriptional units. The exogenously applied chemical activates the transcription factor that further transactivates the downstream target genes. The target genes are activated by the specific promoters that contain binding sites for the transactivator (\u003cem\u003epOp6\u003c/em\u003e, \u003cem\u003elexA\u003c/em\u003e, and \u003cem\u003ealcSynth\u003c/em\u003e), and hence promoters cannot be changed, while terminators can be.\u003c/p\u003e\n\u003cp\u003eInterestingly, the different terminators resulted in more uniform \u003cem\u003eNLuc\u003c/em\u003e expression, with a 2.2- and 2.9-fold range in expression levels for the Dex and estradiol inducible promoters (\u003cem\u003epOp6-35S\u003c/em\u003e and \u003cem\u003elexA-35S\u003c/em\u003e), respectively (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eh,i). For the Dex system, RLU was spread between 37.9 and 111.6 in combination with the \u003cem\u003eUBQ5\u003c/em\u003e and \u003cem\u003e35S terminators.\u003c/em\u003e For the estradiol promoter (\u003cem\u003elexA\u003c/em\u003e), the \u003cem\u003eE9-RbcS\u003c/em\u003e and \u003cem\u003e35S\u003c/em\u003e terminators had RLU counts of 11.6 and 25.1, respectively. In contrast, the ethanol inducible promoter (\u003cem\u003ealcSynth\u003c/em\u003e), showed a much wider range, with the \u003cem\u003eHSP\u003c/em\u003e terminator driving sevenfold higher expression than the \u003cem\u003eLEC2\u003c/em\u003e terminator (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ej). Both the Dex and estradiol systems showed a basal expression of around 10 RLU; with Dex inducing a\u0026thinsp;~\u0026thinsp;11-fold and estradiol a\u0026thinsp;~\u0026thinsp;3-fold activation. The ethanol system had high basal expression (i.e., it was leaky), leading to only 30% increase in luminescence upon chemical induction.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003ePromoter-coding sequence-terminator interactions in gene regulation\u003c/h3\u003e\n\u003cp\u003eReflecting on the observed promoter-terminator interactions, we investigated whether changing the coding sequence also influences promoter/terminator activity. Fluorescent proteins are an alternative visible reporting system to luciferases. However, using fluorescent proteins in a plant chassis can be challenging as plants emit red and green-range autofluorescence from their chloroplasts, and stress-induced blue-range autofluorescence from their cytoplasm\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Over the years, fluorescent proteins with improved brightness and expression dynamics have been developed, but their quantitative efficacy was not comprehensively characterized in protoplasts. Therefore, we screened fluorescent proteins from four spectrums (green, red, yellow, and blue) to examine their compatibility with a protoplast system using a plate reader or microscopy (\u003cstrong\u003eSupplementary Table S4\u003c/strong\u003e). Generally, expression of fluorescent proteins was detected 6 hours after transformation and plateaued around 15 hours (\u003cstrong\u003eSupplementary Fig. S6\u003c/strong\u003e). Informed by this screening, we selected the brightest two fluorescent proteins from different spectra: sfGFP and mScarlet-I. \u003cem\u003esfGFP\u003c/em\u003e was the main reporter, while \u003cem\u003emScarlet-I\u003c/em\u003e was used as the normalizing gene (similar to \u003cem\u003eFLuc\u003c/em\u003e above) and expressed using the \u003cem\u003eUBQ10\u003c/em\u003e promoter and \u003cem\u003eUBQ5\u003c/em\u003e terminator.\u003c/p\u003e\n\u003cp\u003eInitially promoters were evaluated with the \u003cem\u003eHSP\u003c/em\u003e terminator for \u003cem\u003esfGFP\u003c/em\u003e expression. Unlike luciferase reporters, not all promoters drove strong enough expression that could be detected with a plate reader (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec). The highest expression was driven by the \u003cem\u003eUBQ10\u003c/em\u003e and \u003cem\u003eMAS\u003c/em\u003e promoters, followed by the \u003cem\u003e35S\u003c/em\u003e promoter. Readouts from the rest of the promoters could not be distinguished from the background autofluorescence. Therefore, the \u003cem\u003eUBQ10\u003c/em\u003e and \u003cem\u003eMAS\u003c/em\u003e promoters were analyzed in combination with all the MAPS terminators \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ef,g\u003cstrong\u003e).\u003c/strong\u003e The \u003cem\u003eHSP\u003c/em\u003e terminator was the strongest with both promoters (9.0 and 12.1 RFU for \u003cem\u003eUBQ10\u003c/em\u003e and \u003cem\u003eMAS\u003c/em\u003e, respectively\u003cem\u003e).\u003c/em\u003e The \u003cem\u003eFAD2\u003c/em\u003e terminator was on the high-expression side for both promoters, along with the \u003cem\u003eUBQ10 promoter-35S terminator\u003c/em\u003e and \u003cem\u003eMAS promoter-NOS terminator\u003c/em\u003e combinations. The \u003cem\u003eRbsc2b\u003c/em\u003e terminator resulted in the lowest expression with both promoters. Overall, a 3-fold expression difference was observed by using different terminators with the \u003cem\u003eUBQ10\u003c/em\u003e promoter, and the difference was 6-fold with the \u003cem\u003eMAS\u003c/em\u003e promoter.\u003c/p\u003e\n\u003cp\u003eOur part characterization revealed that although promoter choice could dominantly determine gene expression strength in some cases (\u003cem\u003ee.g., pOp6\u003c/em\u003e and \u003cem\u003eNDUFA8\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea), TU activation by a promoter, terminator or coding sequence is not independent or additive, but combinatorial and even synergistic (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb). A clear example of such all-part interactions is seen with the \u003cem\u003eNOS\u003c/em\u003e terminator, which in combination with the \u003cem\u003eUBQ10\u003c/em\u003e resulted in weak, \u003cem\u003eNDUFA8\u003c/em\u003e strong, \u003cem\u003elexA\u003c/em\u003e medium, \u003cem\u003epOp6\u003c/em\u003e weak and \u003cem\u003ealcSynth\u003c/em\u003e mid-level expression in the luciferase system. When we switched to a fluorescence-based reporter, the combination of the \u003cem\u003eNOS\u003c/em\u003e terminator with the \u003cem\u003eUBQ10\u003c/em\u003e and \u003cem\u003eMAS\u003c/em\u003e promoters drove medium and strong expression, respectively. Among the 14 terminators characterized, only four exhibited stable behaviours with both coding sequences: \u003cem\u003eFAD2\u003c/em\u003e and \u003cem\u003eHSP\u003c/em\u003e were consistently on the strong side, while \u003cem\u003eLEC2\u003c/em\u003e and \u003cem\u003eRbcS2b\u003c/em\u003e tended towards the weak side.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eDissecting the mechanism of the combinatorial gene regulation\u003c/h3\u003e\n\u003cp\u003eNext, we sought for insights into how the promoters, coding sequences, and terminators interacted in gene regulation. The interactions may regulate gene expression by changing the transcript abundance or with post-transcriptional modifications affecting translation. To distinguish these two possibilities, we performed qPCR to examine how the transcript (mRNA) level correlates with the reporter readout. Because transforming enough protoplasts for RNA extraction is laborious, we selected key constructs to test. The \u003cem\u003eHSP\u003c/em\u003e and \u003cem\u003eFAD2\u003c/em\u003e terminators were chosen for consistently strong expression regardless of different promoters and reporter protein sequences. For consistently weak expression, the \u003cem\u003eNOS\u003c/em\u003e and \u003cem\u003eRbcs2b\u003c/em\u003e terminators were selected for \u003cem\u003eNLuc\u003c/em\u003e, whereas the \u003cem\u003eRbcs2b, APT1\u003c/em\u003e and \u003cem\u003eE9-RbcS\u003c/em\u003e terminators were chosen for \u003cem\u003esfGFP\u003c/em\u003e. The \u003cem\u003eNOS\u003c/em\u003e terminator was chosen because its relative strength varies the most depending on the partnering promoters or coding sequences. Similarly, the \u003cem\u003e35S\u003c/em\u003e terminator was selected for its variability in strength, although it tends to be on the strong side.\u003c/p\u003e\n\u003cp\u003eThe qPCR assay revealed that the consistently strong \u003cem\u003eFAD2\u003c/em\u003e and \u003cem\u003eHSP\u003c/em\u003e terminators have significantly higher mRNA levels; similarly, the weak \u003cem\u003eE9-Rbcs2b\u003c/em\u003e terminator had significantly lower mRNA levels than the other terminators (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea). Generally, \u003cem\u003eNLuc\u003c/em\u003e displayed a linear correlation between the measured reporter expression levels and mRNA levels (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb), while \u003cem\u003esfGFP\u003c/em\u003e exhibited a looser correlation (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec). Surprisingly, the \u003cem\u003e35S\u003c/em\u003e terminator yielded higher reporter expression in both reporter combinations compared to its mRNA levels (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb,c). Taken together, the transcript level analysis suggests that the combinatorial gene regulation is partially explained by transcript abundance and likely to involve post-transcriptional (post-mRNA formation) processes in some constructs. The \u003cem\u003e35S\u003c/em\u003e terminator for both \u003cem\u003eNLuc\u003c/em\u003e and \u003cem\u003esfGFP\u003c/em\u003e suggested a post-transcriptional enhancer effect, while \u003cem\u003eNLuc:NOS\u003c/em\u003e, together with \u003cem\u003esfGFP:RbcS2b\u003c/em\u003e and \u003cem\u003esfGFP:APT1\u003c/em\u003e constructs indicated post-transcriptional repression.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTranscriptional regulation is mediated by specific DNA sequences that recruit functional proteins to activate or repress processes from transcriptional initiation to stable mRNA formation. Surprised by how effective terminators are in controlling gene expression, we investigated the nucleotide sequence features possibly influencing terminator strength, such as the GC content and presence of likely functional sequences (\u003cem\u003ee.g\u003c/em\u003e., the canonical poly-A signal AAUAAA and UGUA motifs) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cstrong\u003eSupplementary Fig. S7\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eA GC content of approximately 30% has been shown to be optimal for synthetic terminator functions, and the average GC content in natural Arabidopsis terminators is about 32.5 \u003csup\u003e46\u003c/sup\u003e.The GC content in the MAPS terminators had no clear correlations between the strength and GC content of the terminators; consistently or predominantly strong terminators [\u003cem\u003eHSP\u003c/em\u003e (29.1%), \u003cem\u003eFAD2\u003c/em\u003e (36.5%), and \u003cem\u003e35S\u003c/em\u003e (32%)] had a similar range of GC content as weak or variable terminators [\u003cem\u003eRbcS2b\u003c/em\u003e (30%), \u003cem\u003eE9-RbcS\u003c/em\u003e (32.8%), \u003cem\u003eAPT1\u003c/em\u003e (35%), and \u003cem\u003eNOS\u003c/em\u003e (38.7%)] (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea). One apparent feature in the strong terminators is that the GC content is apparently lower at a 50 bp window, which is located around their dominant Poly-A signal, and then the GC content goes back up. The presence of the canonical AAUAAA poly-A signal tends to enhance terminator strength\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWe also searched for sequence motifs that may link to the consistently high strength of \u003cem\u003eHSP\u003c/em\u003e and \u003cem\u003eFAD2\u003c/em\u003e terminators. The presence of the UGUA motif around 30\u0026ndash;40 bp upstream of the RNA cleavage site is thought to enhance the cleavage and thus increase terminator strength in the \u003cem\u003e35S\u003c/em\u003e terminator \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. The \u003cem\u003eHSP\u003c/em\u003e terminator has two UGUA motifs at locations 119 bp and 206 bp, while \u003cem\u003eFAD2\u003c/em\u003e lacks the motif. Interestingly, the XSTREME motif discovery tool identified four motifs that putatively have functional roles \u003cem\u003ea piriori\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb,c,e). One of them is the 15-bp motif (CAAAUGUUUUGUGUC) found around 145 bp in both the \u003cem\u003eHSP\u003c/em\u003e and \u003cem\u003eFAD2\u003c/em\u003e terminators, which correspond to the transcript cleavage site. Three other motifs - CUCAUUAUGUUA, UUGUUGUGUUAUGAC, and UUUUUCUAAUAUUA - were found at similar locations in both terminators but around 10\u0026ndash;20 bp apart. Only the CUCAUUAUGUUA motif is present in the \u003cem\u003eUBQ5\u003c/em\u003e terminator at 167 bp (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb). The effects of the UUUUU motif are complicated; it decreases terminator strength in maize protoplasts, especially if they surround a UGUA motif, while U-rich sequences increase terminator strength in tobacco leaves\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Many of these AT/U-rich motifs reside inside the 50 bp low GC domains described above.\u003c/p\u003e\n\u003cp\u003eWe examined similar potentially functional features in the \u0026lsquo;outlier terminators\u0026rsquo; \u0026ndash; the terminators performing stronger or weaker than expected for their transcript levels (\u003cem\u003ei.e\u003c/em\u003e., likely post-transcriptionally regulated) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea,b). The GC content of outlier terminators is moderate and varies between 30.5\u0026ndash;35.0% (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea). \u003cem\u003eRbcS2b\u003c/em\u003e is the only outlier terminator that does not possess the canonical poly-A signal. The \u003cem\u003e35S\u003c/em\u003e terminator has three repeats of the UGUA motif (x3 starting from 96 bp separated by UU). The UUUUU motif is present in the \u003cem\u003eNOS\u003c/em\u003e, \u003cem\u003eRbcS2b\u003c/em\u003e and \u003cem\u003eAPT1\u003c/em\u003e terminators \u003cstrong\u003e(Supplementary Fig. S6).\u003c/strong\u003e Therefore, no distinct sequence signatures were identified to differentiate the terminators that are primarily regulated transcriptionally from the outliers.\u003c/p\u003e\n\u003cp\u003eWe then wanted to experimentally probe how the above-identified sequence features influence the terminator activity by generating a deletion series of the \u003cem\u003eHSP\u003c/em\u003e and \u003cem\u003eFAD2\u003c/em\u003e terminators. Five terminator variations were made to sequentially delete possible regulatory elements, such as Poly-A signals, putative destabilization signals, and Musashi binding elements (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec-f). Statistical analysis of the results showed only a significant difference between the full-length terminator (240 bp) and \u003cem\u003eFAD2\u003c/em\u003e Sequence 5, which is the shortest variation (70 bp) with no Poly-A site, as well as the deletion series Sequence 1 (200 bp). The rest of the \u003cem\u003eHSP\u003c/em\u003e series, as well as the \u003cem\u003eFAD2\u003c/em\u003e series, showed no statistically significant difference in reporter expression (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec-f). This result suggests short sequences (30\u0026ndash;50 bp) at the 5\u0026rsquo; end of terminators might determine the gene expression strength, and terminator functional elements remain unresolved, especially in plant contexts.\u003c/p\u003e\n\u003cp\u003eWe also investigated a sequence feature likely enhancing promoter activity. \u003cem\u003eUBQ10\u003c/em\u003e is by far the strongest promoter in the MAPS toolkit (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e,\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), and its TSS structure is predicted to be unstructured and single-stranded (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). In the survey of the Arabidopsis genome, translation efficiency was found higher for transcripts with unstructured 5\u0026rsquo;UTR\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. To test if the strength of the \u003cem\u003eUBQ10\u003c/em\u003e promoter is dependent on the open loop structure of its 3\u0026rsquo; UTR, which consists of mostly TSS, three mutated versions were created. The first version, Mutated Control (MUTC), involves point mutations that preserve the predicted loop structure; therefore, it is expected to behave similar to the original \u003cem\u003eUBQ10\u003c/em\u003e TSS sequence. Additionally, two other versions, a tight stem-loop structure Mutated 1 (MUT1) and a slightly more branched but more loosely stemmed Mutated 2 (MUT2), were designed with point mutations (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea,b). TSSPlant and Softberry software were used to confirm the mutations do not interfere with the identified motifs or introduce new motifs compared to the WT.\u003c/p\u003e\n\u003cp\u003eWhen the TSS variants were used to drive \u003cem\u003esfGFP\u003c/em\u003e with five different terminators (\u003cem\u003eHSP, FAD2, 35S, NOS\u003c/em\u003e, and \u003cem\u003eRbcS2b\u003c/em\u003e), the results revealed no statistically significant difference in expression between WT and MUTC, as expected (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ec). However, MUT2 exhibited a reduction in expression by approximately 20% for the \u003cem\u003eHSP\u003c/em\u003e and \u003cem\u003eFAD2\u003c/em\u003e terminators. Additionally, MUT1 resulted in a 60% decrease in expression with \u003cem\u003e35S\u003c/em\u003e, while resulting in a 30% increase with the \u003cem\u003eNOS\u003c/em\u003e terminator. There was no difference between the three mutated versions and the WT for the \u003cem\u003eRbcS2b\u003c/em\u003e samples, where the WT expression is already very weak (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ec). With the strong terminators, reporter expression was reduced in MUT1 and MUT2 compared to WT and MUTC variants, suggesting that conversion from open loop to stem-loop decreases gene expression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo gain insights into how promoters, coding sequences, and terminators interact post-transcriptionally, we studied RNA folding. RNA is single-stranded and extensively forms secondary and tertiary structures via hydrogen bonds bringing together (nearly) complementary sequences. Such 2D and 3D structures (\u003cem\u003ee.g\u003c/em\u003e., G-quadruplex) can strongly influence translation and protein expression\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. The combination-dependent regulatory function among the three TU parts may be explained by direct physical interactions through RNA folding.\u003c/p\u003e\n\u003cp\u003eUsing the RNAFold software\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, the folding energy of the whole mRNA sequence was calculated for the transcript species we selected for the qPCR analysis above. The transcription start site (TSS) was identified based on the TSSPlant software\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, and the downstream promoter sequence was incorporated, along with the protein-coding and terminator sequences. The lower the holding energy, the tighter the transcript folds, and the less likely for translation to occur. No direct correlation was found between the predicted transcript folding energy and mRNA expression levels or between the folding energies and reporter gene expression (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ed,e). We therefore proceeded to examine the RNA folding structure and local interactions among the nucleotide sequences.\u003c/p\u003e\n\u003cp\u003eWe then visually examined the RNA folding structures in 2D. RNA secondary structure formation was predicted using the ViennaFold2.0 software\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, in which the whole transcript (identified as described above) was used as the input. Within the transcript, the strong \u003cem\u003eUBQ10\u003c/em\u003e and \u003cem\u003eMAS\u003c/em\u003e promoters are likely to have little interaction with the other parts: no interaction with the \u003cem\u003eHSP\u003c/em\u003e and \u003cem\u003eRbcS2b\u003c/em\u003e terminators and minimal to medium interactions with the \u003cem\u003eFAD2\u003c/em\u003e terminator were predicted by the software (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). On the contrary, \u003cem\u003eNOS\u003c/em\u003e, a highly variable terminator, may have strong interactions with the other parts (\u003cem\u003eUBQ10:NLuc\u003c/em\u003e) when it drives weak reporter expression. Interestingly, there was no apparent correlation between inducible promoter-terminator structures and reporter output, except for \u003cem\u003epOp6:NLuc:NOS\u003c/em\u003e (\u003cstrong\u003eSupplementary Fig. S8\u003c/strong\u003e). In strong and consistent terminators like \u003cem\u003eHSP\u003c/em\u003e, we tended to find loops bigger than 20 bp \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e\u003cstrong\u003e).\u003c/strong\u003e When the variable strength-terminator \u003cem\u003eNOS\u003c/em\u003e is paired with strong promoters (\u003cem\u003eUBQ10\u003c/em\u003e and \u003cem\u003eMAS)\u003c/em\u003e, it also may form a large loop structure, though not when combined with other promoters (\u003cem\u003ee.g\u003c/em\u003e., \u003cem\u003eNDUFA8\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, \u003cstrong\u003eSupplementary Fig. S9\u003c/strong\u003e). RNA sequence-mediated cross-part interactions among the promoters, coding sequences, and terminators could explain the combinatorial gene regulation.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMAPS: A new synthetic biology toolkit for engineering with plant systems\u003c/h2\u003e \u003cp\u003eIn the present work, we established the Mobius Assembly for Plant Systems (MAPS), which is an adaptation of the Mobius Assembly\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e for plant genetic programming (\u003cb\u003eSupplemental Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Supplemental Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). MAPS is a stand-alone yet highly versatile and functional DNA assembly platform and genetic toolkit collection. It is based on small binary vectors for all levels of cloning and comes with a well-characterized, Phytobrick-compatible standard part library for gene expression control in plant species, including a suite of reporter protein cassettes. We have also introduced a new feature for Mobius Assembly to facilitate the generation of a series of constructs with part variations; MethyAble exploits \u003cem\u003ein vitro\u003c/em\u003e DNA methylation to directly feed Phytobricks in single- or multi-TU constructs.\u003c/p\u003e \u003cp\u003eFor plant genetic engineering, small binary vectors are instrumental in enhancing efficiency in cloning and transformation. As we wanted a reliable core vector in our kit for both cloning and transformation applications, we developed a new binary vector pMAP. This new vector has a unique dual-mode origin of replications for \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eRhizobium\u003c/em\u003e backgrounds, which enables amplification in high copy numbers for experiments demanding high DNA yields, such as the protoplast assay (4 ug DNA per 75 ul transformation) used in this study. The plasmid dimerization phenomenon we have observed is not unique to Mobius Assembly vectors; in fact, with the increasing availability of whole-plasmid sequencing technologies, more dimers are being detected. Therefore, including a \u003cem\u003ecer\u003c/em\u003e site in the backbone provides an effective method for preventing dimerization and growth defects (\u003cb\u003eSupplementary Fig. S3\u003c/b\u003e)\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMAPS delivers a collection of short promoters (17 constitutive and three inducible) and terminator (14 constitutive) standard parts for plant expression. Ten of the promoters and six of the terminators were newly isolated, and they are short in length: 300\u0026ndash;600 bp (promoters) and 200bp (terminators). We confirmed the short size of the promoters did not compromise their strength. The \u003cem\u003eActin 2\u003c/em\u003e promoter characterized in MoClo and GB2.0 toolboxes had comparable activity to the NOS promoter\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e; the shorter version in the MAPS toolkit, \u003cem\u003eACT2\u003c/em\u003e (340bp) and \u003cem\u003eNOS\u003c/em\u003e promoter, exhibited similar expression activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In general, the size of the available standard promoter or terminator parts for plant engineering (\u003cem\u003ee.g\u003c/em\u003e., MoClo plants, GB plants, and GreenGate) can be as long as 4 kb. Shorter parts are desirable as large constructs can reduce plasmid stability in bacteria, while also lowering transformation efficiency and causing incomplete/truncated transformation in planta\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHere, we reported the first characterization of the MAPS toolkit, which was done exclusively in Arabidopsis leaf mesophyll protoplasts with a plate reader-based high-throughput assay. Most plant standard part characterization to date has been conducted in either Arabidopsis protoplasts or \u003cem\u003eNicotiana benthamiana\u003c/em\u003e (leaf expression system), which are both dicot plants. The standard parts can behave differently in different backgrounds. In monocots, for example, the \u003cem\u003e35S\u003c/em\u003e and \u003cem\u003eNOS\u003c/em\u003e promoters drive low activity in monocot \u003cem\u003eSetaria viridis\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Even within dicots, the same part can have different levels of expression\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. The \u003cem\u003eOCS\u003c/em\u003e promoter had strong activity in our study but low expression in \u003cem\u003eN. benthamiana\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Genetic parts can behave differently in different chassis, and we should not generalize characterization results among different plant species or expression systems (\u003cem\u003ee.g.\u003c/em\u003e, whole plants, leaves, protoplasts, or cell cultures). We recommend not making assumptions and testing the activity levels of standard parts in the specific context in which users want to apply them.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTerminators influence gene expression, in part through interaction with promoters\u003c/h3\u003e\n\u003cp\u003eOur characterization of the MAPS terminators underscored the strong influence of the terminators cast on gene expression. It is a common practice to rely on promoters to control gene expression level and overlook the impact of terminators. For example, when the domestication of the synthetic plant promoter \u003cem\u003eG10-90\u003c/em\u003e promoter resulted in loss of expression, researchers replaced it with the \u003cem\u003eAtRPL37aC\u003c/em\u003e promoter to rectify\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. They were using the \u003cem\u003epsE9-RbcS\u003c/em\u003e terminator, which in our assay had low activity; the gene expression could potentially have been restored with a stronger terminator instead. Even though promoters generally have a strong influence on gene expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), terminators could alter the gene expression up to 8-fold in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e,\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Alternative to constitutive expression, chemically inducible systems are powerful tools to control the timing, localization, and amplitude of transgene expression. Dex-, estradiol-, and ethanol-inducible systems were developed 20 years ago and have been used in numerous studies since then\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The extent of target gene induction can be modulated by exchanging the terminators (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTerminators influence gene expression through multiple mechanisms\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, including post-transcriptional regulation via the 3' UTR, which can affect mRNA stability through specific sequence motifs. For instance, certain motifs at the 3' UTR in Arabidopsis can stabilize (e.g., TTGCTT) or destabilize (e.g., AATTTT) mRNA\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Poly(A) tails also play a role, with short tails often linked to strong gene expression \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. In Arabidopsis, the poly(A) tail can prevent RDR6 from converting aberrant mRNAs into degradation substrates\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Unpolyadenylated transcripts derived from terminator-less constructs or readthrough mRNAs from transgenes with strong promoters are subjected to RDR6-mediated silencing\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, which might explain the increased gene expression observed with double terminators\u003csup\u003e\u003cspan additionalcitationids=\"CR61\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. However, the synergistic effects of terminators, particularly when combined with weak promoters, suggest interactions beyond these mechanisms. Direct interactions between terminators and promoters, potentially involving gene looping mediated by DNA-binding proteins, can influence gene expression\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. These interactions might affect transcriptional memory\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e, intron-mediated modulation of transcription\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e, transcription directionality\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e, reinitiation of transcription\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e, and transcription termination\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. Additionally, terminators may impact epigenetic regulation, as the absence of a terminator can lead to increased DNA methylation on promoter regions, silencing transgene expression\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003ePromoter-coding sequence-terminator interactions in gene regulation\u003c/h3\u003e\n\u003cp\u003eIn addition to the interactions between terminators and promoters, we observed that protein-coding sequences can also modify the strength of promoter-terminator function. The coding sequence may impact gene expression through factors such as codon usage bias, gene length, GC content, correct 5' cap, and mRNA folding\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Both the \u003cem\u003eNLuc\u003c/em\u003e (516 bp, GC 52.7%) and \u003cem\u003esfGFP\u003c/em\u003e (714 bp, GC 61.3%) have been optimized for plant expression in terms of codon usage. In an orthogonal system where part performance does not depend on the context, any factors related to codon usage, gene length, GC content, or correct 5' cap may affect the magnitudes of the reporter readout, but the comparative profiles of gene expression across the promoter-terminator pairs should remain the same. However, the relative strengths of promoters and terminators changed when the reporter protein was replaced (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e,\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), indicating that the coding sequence interacted with the promoter and the terminator presumably directly and physically.\u003c/p\u003e \u003cp\u003eWe wanted to understand how the promoter-coding sequence-terminator interactions occur. There are many possible mechanisms (some examples outlined above), which likely to influence gene activity in combinations. However, to simplify, such interactions can be explained either at the DNA or RNA level \u0026ndash; or transcriptional or post-transcriptional mechanisms. The qPCR assay indicated that the reporter genes activity was regulated transcriptionally by controlling the stable transcript abundance, but also post-transcriptionally as the transcript abundance alone did not always correlate with the reporter activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo explore the mechanisms behind the transcriptional regulation, we surveyed DNA sequence features potentially involved in determining terminator or promoter activity. The terminators with consistently strong reporter protein readout (\u003cem\u003eHSP\u003c/em\u003e and \u003cem\u003eFAD2\u003c/em\u003e) also had high mRNA levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). These two terminators contain a local drop (50 bp) in the GC content, that was absent in most other terminators. It is likely that this low GC region holds other structural features that aid the recognition of transcription termination and cleavage. When other possible motifs were searched for \u003cem\u003ea priori\u003c/em\u003e in \u003cem\u003eHSP\u003c/em\u003e and \u003cem\u003eFAD2 but not in other MAPS terminators\u003c/em\u003e, the XSTREME motif discovery tool identified four motifs. Among the four, CAAAUGUUUGUGUC motif at location 145 bp in \u003cem\u003eHSP\u003c/em\u003e and 146 bp in \u003cem\u003eFAD2\u003c/em\u003e, may aid with cleavage site recognition and cleavage efficiency, enhancing overall terminator strength (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb,c).\u003c/p\u003e \u003cp\u003eWith our deletion series, we have seen that only the shortest FAD2 terminator (70 bp) has significantly reduced strength compared to the full-length version (240 bp). The first 35 bp of the \u003cem\u003eHSP\u003c/em\u003e terminator has a strength statistically nondifferent compared to the full-length (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec,d); this is consistent with Felippes \u003cem\u003eet al\u003c/em\u003e.\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e, which showed that the first 32 bp of the \u003cem\u003eHSP\u003c/em\u003e terminator is a transferable element that enhances the strength of other terminators. A very short (\u0026lt;\u0026thinsp;50 bp), minimal terminator domain may govern much of the terminator activity, especially in transient expression systems, whereas the specific features identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) may play clearer roles in longer timescales or certain physiological or developmental contexts.\u003c/p\u003e \u003cp\u003eRecent studies revealed the roles of RNA structures in gene expression control. Lower mRNA folding energies, typically associated with greater structural stability, impact ribosome function and translation in organisms such as \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eS. cerevisiae\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e,\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Beyond global folding, local secondary structures can also play a pivotal role in shaping gene expression. Pseudoknots, hairpins, loops, and the presence of hydrogen bonding patterns upstream of the transcription start site have been shown to reduce ribosome sequestration and translation rates\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. A study with 224,000 synthetic sequences in \u003cem\u003eE. coli\u003c/em\u003e has highlighted the importance of secondary structures at the 30 bp region upstream and downstream of the start codon (referred to as STR\u003csub\u003e-30:+30\u003c/sub\u003e)\u003csup\u003e77\u003c/sup\u003e. While comprehensive studies in plants are still pending, \u003cem\u003ein vivo\u003c/em\u003e genome-wide profiling of Arabidopsis RNA secondary structures revealed that unstructured regions upstream of the start codon are enriched in high translation efficiency mRNAs\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. In the strong promoter \u003cem\u003eUBQ10\u003c/em\u003e, we found an unstructured TSS (loop) where altering its structure affected reporter output (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRNA secondary structures also point to a similar regulatory influence in terminator-dependent gene regulation. When the consistently strong terminators \u003cem\u003eHSP and FAD2\u003c/em\u003e were combined with various promoters, loops of \u0026gt;\u0026thinsp;20 bp were predicted (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). This might be a structural feature of strong-acting terminators, since a similar\u0026thinsp;\u0026gt;\u0026thinsp;20 bp loop was predicted when the variable \u003cem\u003eNOS\u003c/em\u003e terminator was combined with strong promoters (\u003cem\u003eMAS, UBQ10);\u003c/em\u003e yet the loop is thought to re-configure to a stem-loop structure when combined with weaker promoters. Expanding on these structural themes, we examined the folding structure of the terminators from Felippes \u003cem\u003eet al\u003c/em\u003e.\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e that showed increased strength when combined with the first 32 bp of the \u003cem\u003eHSP\u003c/em\u003e terminator. The predicted folding of the transcript showed that the addition of the 32 bp led to an increase in the size of the loops\u0026thinsp;\u0026gt;\u0026thinsp;20 bp for the \u003cem\u003eNOS\u003c/em\u003e and \u003cem\u003eACS2\u003c/em\u003e terminators, as well as higher base-pairing probability structures for \u003cem\u003eRbcs1A\u003c/em\u003e (\u003cb\u003eSupplementary Fig. S9\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eRNA folding patterns predicted the consistent activity promoter and terminator parts tend to be isolated in terms of secondary structure, displaying a low level of physical interactions from the other parts, regardless of their sequences (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, \u003cb\u003eSupplementary Fig. S8\u003c/b\u003e). Additionally, in the case of the \u003cem\u003eNOS\u003c/em\u003e terminator, cross-part interactions could explain the high variability. It is plausible that certain promoters and terminators possess currently unknown regulatory elements that enhance their activity while physically restricting their interactions with other parts. Meanwhile, there may be TU parts that may require physical interactions with other parts to function effectively. For example, in mammalian systems, terminator elements require specific secondary structures to interact between the polyadenylation complex and mRNA\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. Synthetic parts with known elements, like the \u003cem\u003epOp6\u003c/em\u003e and \u003cem\u003eLexA\u003c/em\u003e inducible promoters, likely hold key to context-independency. Even though their transcripts were predicted to fold, generating cross-fold interactions, such interaction did not seem to have a strong effect on the reporter output (\u003cb\u003eSupplementary Fig. S8\u003c/b\u003e).\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eWe developed a new, simple and versatile synthetic biology toolkit for plant genetic engineering, Mobius Assembly for Plant Systems. In so doing, we unravelled new insights into gene regulation using a bottom-up approach and have shown that gene expression is regulated by all parts of a transcriptional unit combinatorically. Uncovering the relationship between the structure and function of RNA (and DNA) will help establish the principles for designing consistent and predictive standard parts that are independent of the nucleotide context-independent and more orthogonal.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eBacterial strains and growth conditions\u003c/h2\u003e \u003cp\u003e \u003cem\u003eE. coli\u003c/em\u003e strains JM109, and NEB stable were used. \u003cem\u003eE. coli\u003c/em\u003e chemically competent cells were prepared in-house using the TSS preparation described by Chung \u0026amp; Miller\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eE. coli\u003c/em\u003e cells were incubated at 37\u0026deg;C (30\u0026deg;C for NEB stable), 200 min-1 either in 5 ml (for a high copy) or 10 ml (for a low copy), or 100 ml (for midi prep) in LB growth medium supplemented with antibiotics. Cells bearing the LhGR-pOp6 and sXVE-lexA inducible systems were grown for 24 h instead due to their slower growth rate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eBacterial transformations and plasmid isolation\u003c/h2\u003e \u003cp\u003eFor \u003cem\u003eE. coli\u003c/em\u003e transformation, 5 \u0026micro;l of the plasmid DNA was incubated with 100 \u0026micro;l of the competent cells on ice for 30 min, followed by a heat shock at 42\u0026deg;C for 90 sec (30 sec for TOP10) and re-cooled on ice for 5 min. S.O.C medium (400 \u0026micro;l) was added, and after 1 h incubation at 37\u0026deg;C, 100 \u0026micro;l of the cell suspension was plated on LB agar plates with antibiotic selection. Plasmids were isolated using Monarch (NEB) Plasmid Miniprep Kits. The GeneJET Plasmid Midiprep Kit (ThermoFisher) were used for higher yields.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMAPS vector construction\u003c/h2\u003e \u003cp\u003eMAPS Level 1 and Level 2 Acceptor Vectors were built using Gibson Assembly. The Mobius Assembly cassettes were amplified from the corresponding \u003cem\u003eE. coli\u003c/em\u003e plasmids in the Mobius Assembly Vector toolkit\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e and fused to the plant binary vectors. The pGreen-based Level 1 vectors were constructed using pGreen0029\u003csup\u003e31\u003c/sup\u003e. The \u003cem\u003eNptI\u003c/em\u003e gene was replaced with the spectinomycin resistance gene amplified from the pCR8 vector (ThermoFisher) to generate Level 2 vectors. The pLX-based Level 1 and Level 2 vectors were built using pLX-B3 and pLX-B2\u003csup\u003e33\u003c/sup\u003e, respectively.\u003c/p\u003e \u003cp\u003eWKS1 Ori was synthesized (Twist Bioscience) into two parts due to repetitive sequences, and pUC Ori was amplified from pUC19, both flanked by \u003cem\u003eBsaI\u003c/em\u003e recognition sites. The minimum sequence requirement of pUC for stable replication in E. coli was found to include RNA I/RNA II transcripts on the 5\u0026prime;-side and dnaA/dnaA\u0026prime; boxes on the 3\u0026prime;-side, while co-directional transcription of two different replicons in the same plasmid was shown to increase transformation efficiency and DNA yield\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. A pLX Level 1 A vector with the construct NOS:BglR:NOS-UBQ10:nluc:HSP was amplified outside the BBR1 Ori with primers harbouring \u003cem\u003eBsaI\u003c/em\u003e recognition sites and fused with pUC and WKS1 Ori. The resulting plasmid was used as the template to amplify the pUC-WKS1 fused Ori, which then replaced BBR1 Ori from the pLX-based Level 1A and Level 2A vectors with Gibson Assembly, resulting in the pMAP Level 1A and Level 2A vectors. The rest of the pMAP vectors were constructed again using isothermal assembly and as a template for the Mobius cloning cassettes, the plasmids from the original Mobius Assembly kit\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMethylAble modules were devised with the Gibson Assembly using mUAV as a template. Overlapping primers bearing two outward-facing \u003cem\u003eBsaI\u003c/em\u003e sites prone to CpG methylation and suitable standard overhangs were used to amplify mUAV into two parts. The resulting parts were purified and digested with \u003cem\u003eDpnI\u003c/em\u003e (ThermoFisher) to eliminate the template DNA, and they were fused in an isothermal reaction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMobius Assembly and standard part library construction\u003c/h2\u003e \u003cp\u003eA detailed protocol on Mobius Assembly can be found in\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. Briefly, the assembly was performed in a one-tube reaction with a total volume of 10 \u0026micro;l, with ~\u0026thinsp;50 ng Acceptor Vectors and double amounts of inserts. Reagents added were 1 \u0026micro;l of 1 mg/ml BSA (diluted from 20 mg/ml - NEB), 1 \u0026micro;l T4 DNA ligase buffer (ThermoFisher/NEB), 0.5 \u0026micro;l \u003cem\u003eAarI/PaqCI\u003c/em\u003e (ThermoFisher/NEB) and 0.2 \u0026micro;l 50x oligos of the \u003cem\u003eAarI\u003c/em\u003e recognition site for Level 0 and Level 2 cloning or Eco31I/BsaI-HFv2 (ThermoFisher/NEB) for Level 1 cloning, and 0.5 \u0026micro;l T4 DNA ligase (ThermoFisher/NEB). The reactions were incubated in a thermocycler for 5\u0026ndash;10 cycles of 5 min at 37\u0026deg;C and 10 min at 16\u0026deg;C, followed by 5 min digestion at 37\u0026deg;C and 5 min deactivation at 80\u0026deg;C (5 cycles for Level 0 and the first round of Level 1 cloning \u0026ndash; 10 cycles for Level 2 and large constructs\u0026thinsp;\u0026gt;\u0026thinsp;10 kb).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePCR amplification\u003c/h2\u003e \u003cp\u003eAll PCR amplifications for plasmid construction and cloning were performed using Q5\u0026reg; High- Fidelity DNA Polymerase (NEB), followed by purification with Monarch\u0026reg; PCR \u0026amp; DNA Cleanup Kit (NEB). Successful DNA assembly was verified first by colony PCR using GoTaq\u0026reg; Green Master Mix (Promega) and then with double restriction digestion with EcoRI-HF (NEB) and PstI- HF (NEB). The constructs were further verified by Sanger sequencing (GATC Biotech-Eurofins, Edinburgh Genomics and Source BioScience) and whole plasmid sequencing (Full Circle).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePromoter and terminator part design and generation\u003c/h2\u003e \u003cp\u003eLikely constitutive promoters and terminators were selected from the literature, especially the genes commonly used as positive controls in qRT-PCR\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. Their sequences were retrieved from the TAIR webpage\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.arabidopsis.org/index.jsp\u003c/span\u003e\u003cspan address=\"http://www.arabidopsis.org/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and blasted in NCBI to find the untranslated regions flanking the genes. A 1.5 kb sequence upstream of the start codon was run through the online prediction software and TSSPlant\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.softberry.com\u003c/span\u003e\u003cspan address=\"http://www.softberry.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to identify TATA and TATA-less promoters or enhancer sites. In the promoter selection, it was also considered, when possible, to include the initiator (INR) elements (YYA(+\u0026thinsp;1)NWYY- TYA(+\u0026thinsp;1)YYN-TYA(+\u0026thinsp;1)GGG)) and downstream promoter (DPE) element (RGWYV). Potential promoter elements linked to increased gene expression were identified using PlantCare\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e and PLACE\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e software. Terminators were selected with PASPA, a web server for poly(A) site prediction in plants and algae\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bmi.xmu.edu.cn/paspa/interface/run_PASPA.php\u003c/span\u003e\u003cspan address=\"http://bmi.xmu.edu.cn/paspa/interface/run_PASPA.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A sequence 300bp downstream of the stop codon was input into PASPA, and the end of the terminator was set at 10bp after the second polyadenylation site, resulting in a sequence of around 200bp. They were then analyzed in RegRNA2.0 to identify other RNA functional motifs\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://regrna2.mbc.nctu.edu.tw\u003c/span\u003e\u003cspan address=\"http://regrna2.mbc.nctu.edu.tw\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe parts designed in the first round were short promoters (~\u0026thinsp;300bp) and short terminators (~\u0026thinsp;200bp) from the genes \u003cem\u003eACT2, FAD2, TUB9, APT1, NDUFA8\u003c/em\u003e and \u003cem\u003eLEC2.\u003c/em\u003e As the ~\u0026thinsp;300 bp promoters were very low in activity (Data not shown), we designed a new set of promoters derived from the genes \u003cem\u003eTUB2, UBQ11, UBQ4, ACT7\u003c/em\u003e, and the longer versions of the previous promoters (~\u0026thinsp;500 kb). Appropriate primers compiling to the Phytobrick standard overhangs were designed for both promoters and terminators for cloning into mUAV.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eProtoplast isolation and transformation\u003c/h2\u003e \u003cp\u003eArabidopsis (Wildtype, Col-0) seeds were sown on the soil. After a 2-day pre-treatment at 4\u0026deg;C in darkness, they were sawn and grown under long-day conditions (21\u0026deg;C; 16h light / 8h dark cycles; light intensity\u0026thinsp;~\u0026thinsp;100\u0026micro;mol/m2s-1; humidity 40\u0026ndash;65%) until harvest. The protoplast isolation and transformation protocol was developed based on Chupeau \u003cem\u003eet al\u003c/em\u003e., (2013)\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e and Faraco \u003cem\u003eet al\u003c/em\u003e., (2011)\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e. The optimized protocol is descripted in Supplementary Information, with representative images of consistently high transformation (\u003cb\u003eSupplementary Fig. S4\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLuciferase assay\u003c/h2\u003e \u003cp\u003eThe 96-well plates containing the transformed protoplasts were let to sediment and 60 \u0026micro;l of supernatant was discarded. The protoplasts were resuspended, and 40 \u0026micro;l was transferred to white optical plates in a grid pattern with empty spaces between wells to reduce luminescence bleed-through. Luciferase activity was assayed in an Omega luminescence plate-reader (Fluostar) with four different gains following the instructions of the Nano Dual-Luciferase\u0026reg; Reporter kit (Promega, N1620). A further correction for luminescence bleed-through and background signal was applied using the software developed by Mauri et al.\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e Then NLuc signal was divided by the FLuc signal to normalize for the transformation efficiency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eFluorescence assay\u003c/h2\u003e \u003cp\u003eFor fluorescent protein assays, TECAN Spark plate reader was used with either 96-well plates (Greiner) or black Thermo Scientific Nunc F96 MicroWell. Excitation and emission for the super-folder green fluorescent protein (sfGFP) was 485 nm and 520 nm, respectively. For mScarlet-I, excitation and emission wavelengths were 560nm and 620nm. Measurements were taken at two different gains to ensure signals don\u0026rsquo;t overshoot beyond saturation. The sfGFP values were divided by the mScarlet-I values to normalize for the transformation efficiency. Since fluorescence was not always active, unlike luminescence and also because black-walled plates were used to prevent signal bleed-through, deconvolution correction was not applied to the fluorescent samples.\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eRNA isolation and real-time PCR (qRT-PCR)\u003c/h2\u003e \u003cp\u003eIsolation and transformation were performed as described above, with reaction and solution volumes upscaled by 26 times. 24 h after transformation, the supernatant was discarded, and sedimented protoplasts were harvested for RNA extraction with GeneJET Plant RNA Purification Kit (ThermoFisher). Extracted RNA was treated with DNA-free DNA Removal Kit (Invitrogen). Then, reverse transcription was performed with UltraScript 2.0 cDNA Synthesis Kit (PCR Biosystems). This cDNA synthesis kit uses optimum amounts of oligo (dT) and random hexamers for unbiased amplification of RNA variants. It is advised to use 2 \u0026micro;M of both oligo (dT) and random hexamers for cDNA synthesis when using a different kit.\u003c/p\u003e \u003cp\u003eqPCR reaction was set up with primers and probe sequences listed in Supplementary Table S4. Sso Advanced Universal Probes Supermix (Bio-Rad) was used following the manufacturer\u0026rsquo;s instructions. with the reactions were prepared in MicroAmp Endura 96-well plate and sealed with MicroAmp Optical Adhesive films, and the optical output was measured with StepOnePlus qPCR machine (all Applied Biosystems). A standard curve was included on the plate for each gene target tested; this standard curve was used to calculate cDNA copy number of the samples on the same plate. The FLuc or mScarlet-I cDNA copy number was used for normalization for the transformation efficiency. The resulting values from all the constructs were then normalized to the NLuc cDNA copy number from the \u003cem\u003eUBQ10:NLUC:NOS\u003c/em\u003e in the same experiment, to account for any batch-to-batch variations.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFunctional dissection of the\u003c/b\u003e \u003cb\u003eHSP\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eFAD2\u003c/b\u003e \u003cb\u003eterminators\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFive length deletion series were constructed for \u003cem\u003eHSP\u003c/em\u003e and \u003cem\u003eFAD2\u003c/em\u003e terminators. The deletions removed putative motifs that could affect their function to influence the gene expression level. Putative Stabilization/Destabilization Motifs\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e while the rest were identified using RegRNA2.0\u003csup\u003e1\u003c/sup\u003e and PASPA online tools\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Primers were designed to gradually remove functional sequence elements from the 3' end of each terminator through PCR and subsequently cloned to mUAV. Site-directed mutagenesis by Gibson Assembly was employed to mutate the poly-A signal of the \u003cem\u003eHSP\u003c/em\u003e Part3, while the same method was used to build \u003cem\u003eHSP\u003c/em\u003e Part 5. Transformation of the construct was performed with PEG-transformation of Arabidopsis leaf mesophyll protoplasts in 96-well plates. The expression activity of the constructs was assayed using a plate reader, measuring the Nano luciferase activity normalized by Firefly luciferase. The construct we used was \u003cem\u003eUBQ10:Nluc:Terminator Part:UBQ10:Fluc:UBQ5\u003c/em\u003e, housed in a pMAP vector.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eRNA folding prediction\u003c/h2\u003e \u003cp\u003eFor mRNA folding predictions, the transcription start site was predicted for each promoter by using TSSPlant\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. The nucleotide sequence downstream of the TSS to the end of the terminator was used as the mRNA sequence. mRNA folding was predicted by using RNAFold software that uses Vienna RNA package.\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e Thermodynamic ensemble prediction was used for folding energy predictions which accounts for all possible RNA secondary structures weighted by their Boltzmann probabilities, allowing the calculation of base pairing probabilities and ensemble free energy. The centroid algorithm was used for secondary structure prediction which is the structure with minimal base pair distance to all other secondary structures in the Boltzmann ensemble\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eACKNOWLEDGEMENTS\u003c/h2\u003e \u003cp\u003eThis project was funded by the Biotechnology and Biological Sciences Research Council (BBSRC) High-Value Compound from Plants (HVCfP) Network Proof-of-Concept Award (POC- NOV16-04), Royal Society University Research Fellowship (UF140640 and URF\\R\\201035), and Schmidt Science Polymath Award to NN, as well as the University of Edinburgh Principal's Career Development PhD Studentship to AIA, the IBioIC CTP PhD Studentship to JN, and Imperial College Bioengineering Studentship to EGK.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLiu W, Yuan J, Stewart N (2013) Advanced genetic tools for plant biotechnology. 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BMC Bioinformatics 14:S4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaraco M, Di Sansebastiano GP, Spelt K, Koes RE, Quattrocchio FM (2011) One Protoplast Is Not the Other! Plant Physiol 156:474\u0026ndash;478\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMauri M, Vecchione S, Fritz G (2019) Deconvolution of Luminescence Cross-Talk in High-Throughput Gene Expression Profiling. ACS Synth Biol 8:1361\u0026ndash;1370\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Plant synthetic biology, context dependency, gene regulation, promoters, terminators, protoplast, interaction, RNA folding","lastPublishedDoi":"10.21203/rs.3.rs-5118685/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5118685/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePlants are the primary biological platforms for producing food, energy, and materials in agriculture; however, they remain a minor player in the recent synthetic biology-driven transformation in bioproduction. Molecular tools and technologies for complex, multigene engineering in plants are as yet limited, with the challenge to enhance their stability and predictivity. Here, we present a new standardized and streamlined toolkit for plant synthetic biology, Mobius Assembly for Plant Systems (MAPS). It is based on small plant binary vectors pMAPs, which contain a fusion origin of replication that enhances plasmid yield in both \u003cem\u003eEscherichia coli\u003c/em\u003e and \u003cem\u003eRhizobium radiobacter\u003c/em\u003e. MAPS includes a new library of promoters and terminators with different activity levels; part sizes were minimized to improve construct stability and transformation efficiency. These promoters and terminators were characterized using a high-throughput protoplast expression assay. We observed a significant influence of terminators on gene expression, as the strength of a single promoter can change more than seven-folds in combination with different terminators. Changing the coding sequence changed the relative strength of promoter and terminator pairs, thus uncovering combinatorial gene regulation among all parts of a transcriptional unit. We further gained insights into the mechanisms of such interactions by analyzing RNA folding, with which we suggest a design principle for more predictive and context-independent genetic parts in synthetic biology of plant systems and beyond.\u003c/p\u003e","manuscriptTitle":"Mobius Assembly for Plant Systems highlights promoter-coding sequences-terminator interaction in gene regulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-27 04:18:08","doi":"10.21203/rs.3.rs-5118685/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"05c51a1e-123b-4ba8-8807-749da0b46de5","owner":[],"postedDate":"September 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":38202376,"name":"Biological sciences/Systems biology/Synthetic biology"},{"id":38202377,"name":"Biological sciences/Plant sciences/Plant molecular biology"},{"id":38202378,"name":"Biological sciences/Genetics/Gene regulation"},{"id":38202379,"name":"Biological sciences/Biotechnology/Molecular engineering"}],"tags":[],"updatedAt":"2024-10-15T20:00:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-27 04:18:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5118685","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5118685","identity":"rs-5118685","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}