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

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Rational design of heterogeneous acid catalysts possessing uniform dispersion of active sites plays a significant role in the catalytic performance. In the present work, a coordination polymer, [Zn(4,4´-bpy)(µ-Hbtc)(H2O)]∙2H2O (Zn-CP), was solvothermally synthesized using 4,4´-bpy (= 4,4´-bipyridine) and H3btc (= 1,3,5-benzenetricarboxylic acid) mixed linkers. Single crystal X-ray analysis of the polymer displayed that Zn-CP chains were decorated with 4,4´-bpy having unidentate coordination fashion. Then, the free N atom of the linker in Zn-CP was functionalized by -SO3H groups to afford Zn-CP-SO3H with enhanced acidity. The structures were characterized by FT-IR, thermogravimetric analysis, powder X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), temperature programmed desorption of NH3 (NH3-TPD), and field emission scanning electron microscopy (FE-SEM) analyses. The coordination polymer was employed as heterogeneous catalyst for alcoholysis of epoxides under room conditions. The Zn-CP-SO3H exhibited excellent catalytic activity and regioselectivity in the methanolysis of styrene oxide within short reaction time.

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Phenazine-based small molecules are nitrogen-containing heterocyclic compounds with diverse bioactivities and electron transfer properties that exhibit promising applications in pharmaceutical and electrochemical industries. However, the biosynthetic mechanism of highly substituted natural phenazines remains poorly understood. In this study, we report the direct cloning and heterologous expression of the lomofungin biosynthetic gene cluster (BGC) from Streptomyces lomondensis S015. Reconstruction and overexpression of the BGCs in Streptomyces coelicolor M1152 resulted in eight phenazine derivatives including two novel hybrid phenazine metabolites, and the biosynthetic pathway of lomofungin was proposed. Furthermore, gene deletion suggested that NAD(P)H-dependent oxidoreductase gene lomo14 is a nonessential gene in the biosynthesis of lomofungin. Cytotoxicity evaluation of the isolated phenazines and lomofungin was performed. Specifically, lomofungin shows substantial inhibition against two human cancer cells, HCT116 and 5637. These results provide insights into the biosynthetic mechanism of lomofungin, which will be useful for the directed biosynthesis of natural phenazine derivatives.

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In settings requiring synthetic data generation based on a clinical cohort, e.g., due to data protection regulations, heterogeneity across individuals might be a nuisance that we need to control or faithfully preserve. The sources of such heterogeneity might be known, e.g., as indicated by sub-groups labels, or might be unknown and thus reflected only in properties of distributions, such as bimodality or skewness. We investigate how such heterogeneity can be preserved and controlled when obtaining synthetic data from variational autoencoders (VAEs), i.e., a generative deep learning technique that utilizes a low-dimensional latent representation. To faithfully reproduce unknown heterogeneity reflected in marginal distributions, we propose to combine VAEs with pre-transformations. For dealing with known heterogeneity due to sub-groups, we complement VAEs with models for group membership, specifically from propensity score regression. The evaluation is performed with a realistic simulation design that features sub-groups and challenging marginal distributions. The proposed approach faithfully recovers the latter, compared to synthetic data approaches that focus purely on marginal distributions. Propensity scores add complementary information, e.g., when visualized in the latent space, and enable sampling of synthetic data with or without sub-group specific characteristics. We also illustrate the proposed approach with real data from an international stroke trial that exhibits considerable distribution differences between study sites, in addition to bimodality. These results indicate that describing heterogeneity by statistical approaches, such as propensity score regression, might be more generally useful for complementing generative deep learning for obtaining synthetic data that faithfully reflects structure from clinical cohorts.

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Medicinal plants produce specialized metabolites (SM) that are used as drugs. However, due to low yields of field cultivation and the increasing market demand, this production method often failed to meet supply needs. Biotechnological alternatives, such as in vitro plant cultures, offer promising solutions. Nonetheless, SM production in these systems remains too low for industrial exploitation, necessitating an elicitation step to induce the plant defense metabolism. Traditional elicitation methods mimic environmental conditions that trigger plant-specialized metabolism, often with an artificial signal that mimics microbial interaction. Recent insights into the essential role of the plant microbiota, provides new opportunities for elicitation strategies by microbial coculture in a controlled environment. The successful co-culture of in vitro medicinal plants with synthetic microbial communities could enable sustainable production of pharmaceutically important SM.

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A common malignant bone neoplasm in teenagers is Osteosarcoma. Chemotherapy, surgical therapy, and radiation therapy together comprise the usual clinical course of treatment for Osteosarcoma. While Osteosarcoma and other bone tumors are typically treated surgically, however, surgical resection frequently fails to completely eradicate tumors, and in turn becomes the primary reason for postoperative recurrence and metastasis, ultimately leading to a high rate of mortality. Patients still require radiation and/or chemotherapy after surgery to stop the spread of the tumor and its metastases, and both treatments have an adverse influence on the body's organ systems. In the postoperative management of osteosarcoma, bone scaffolds can load cargos (growth factors or drugs) and function as drug delivery systems (DDSs). This review describes the different kinds of bone scaffolds that are currently available and highlights key studies that use scaffolds as DDSs for the treatment of osteosarcomas. The discussion also includes difficulties and perspectives regarding the use of scaffold-based DDSs. The study may serve as a source for outlining efficient and secure postoperative osteosarcoma treatment plans.

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Fiber dimension, durability/dissolution, and biopersistence are critical factors for the risk of fibrogenesis and carcinogenesis. In the modern era, to reduce, refine, and replace animals in toxicology research, the application of in vitro test methods is paramount for hazard evaluation and designing synthetic vitreous fibers (SVFs) for safe use. The objectives of this review are to: (1) summarize the international frameworks and acceptability criteria for implementation of new approach methods (NAMs), (2) evaluate the adverse outcome pathways (AOPs), key events (KEs), and key event relationships (KERs) for fiber-induced fibrogenesis and carcinogenesis in accordance with Organization for Economic Co-operation and Development (OECD) guidelines, (3) consider existing and emerging technologies for in silico and in vitro toxicity testing for the respiratory system and the ability to predict effects in vivo, (4) outline a recommended testing strategy for evaluating the hazard and safety of novel SVFs, and (5) reflect on methods needs for in vitro in vivo correlation (IVIVC) and predictive approaches for safety assessment of new SVFs. AOP frameworks following the conceptual model of the OECD were developed through an evaluation of available molecular and cellular initiating events, which lead to KEs and KERs in the development of fiber-induced fibrogenesis and carcinogenesis. AOP framework development included consideration of fiber physicochemical properties, respiratory deposition and clearance patterns, biosolubility, and biopersistence, as well as cellular, organ, and organism responses. Available data support that fiber AOPs begin with fiber physicochemical characteristics which influence fiber exposure and biosolubility and subsequent key initiating events are dependent on fiber biopersistence and reactivity. Key cellular events of pathogenic fibers include oxidative stress, chronic inflammation, and epithelial/fibroblast proliferation and differentiation, which ultimately lead to hyperplasia, metaplasia, and fibrosis/tumor formation. Available in vitro models (e.g. single-, multi-cellular, organ system) provide promising NAMs tools to evaluate these intermediate KEs. However, data on SVFs demonstrate that in vitro biosolubility is a reasonable predictor for downstream events of in vivo biopersistence and biological effects. In vitro SVF fiber dissolution rates >100 ng/cm2/hr (glass fibers in pH 7 and stone fibers in pH 4.5) and in vivo SVF fiber clearance half-life less than 40 or 50 days were not associated with fibrosis or tumors in animals. Long (fiber lengths >20 µm) biodurable and biopersistent fibers exceeding these fiber dissolution and clearance thresholds may pose a risk of fibrosis and cancer. In vitro fiber dissolution assays provide a promising avenue and potentially powerful tool to predict in vivo SVF fiber biopersistence, hazard, and health risk. NAMs for fibers (including SVFs) may involve a multi-factor in vitro approach leveraging in vitro dissolution data in complement with cellular- and tissue- based in vitro assays to predict health risk.

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For mammalian synthetic biology research, multiple orthogonal and tunable gene expression systems have been developed, among which the tetracycline (Tet)-inducible system is a key tool for gain-of-function mutations. Precise and long-lasting regulation of genetic circuits is necessary for the effective use of these systems in genetically engineered stable cell lines. However, current cell line development strategies, which depend on either random or site-specific integration along with antibiotic selection, are unpredictable and unsustainable, limiting their widespread use. To overcome these issues, we aimed to establish a Robust Overexpression via Site-specific integration of Effector (ROSE) system, a clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9-mediated streamlined Tet-On3G-inducible master cell line (MCL) development platform. ROSE MCLs equipped with a landing pad facilitated the transcriptional regulation of various effector genes via recombinase-mediated cassette exchange. Long-term investigation revealed that the modular design of genetic payloads and integration sites significantly affected the induction capacity and stability, with ROSE MCLs exhibiting exceptional induction performance. To demonstrate the versatility of our platform, we explored its efficiency for the precise regulation of selection stringency, manufacturing of therapeutic antibodies with tunable expression levels and timing, and transcription factor engineering. Overall, this study demonstrated the effectiveness and reliability of the ROSE platform, highlighting its potential for various biological and biotechnological applications.

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Synthetic promoters are particularly relevant for application not only in yeast expression systems designed for high-level heterologous protein production but also in other applications such as metabolic engineering, cell biological research, and stage-specific gene expression control. By designing synthetic promoters, researcher can create customized expression systems tailored to specific needs, whether it is maximizing protein production or precisely controlling gene expression at different stages of a process. While recognizing the limitations of endogenous promoters, they also provide important information needed to design synthetic promoters. In this review, emphasis will be placed on some key approaches to identify endogenous, and to generate synthetic promoters in yeast expression systems. It shows the connection between endogenous and synthetic promoters, highlighting how their interplay contributes to promoter development. Furthermore, this review illustrates recent developments in biotechnological advancements and discusses how this field will evolve in order to develop custom-made promoters for diverse applications. This review offers detailed information, explores the transition from endogenous to synthetic promoters, and presents valuable perspectives on the next generation of promoter design strategies.

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Precise measurement of fiber diameter in animal and synthetic textiles is crucial for quality assessment and pricing; however, traditional methods often struggle with accuracy, particularly when fibers are densely packed or overlapping. Current computer vision techniques, while useful, have limitations in addressing these challenges. This paper introduces a novel deep-learning-based method to automatically generate distance maps of fiber micrographs, enabling more accurate fiber segmentation and diameter calculation. Our approach utilizes a modified U-Net architecture, trained on both real and simulated micrographs, to regress distance maps. This allows for the effective separation of individual fibers, even in complex scenarios. The model achieves a mean absolute error (MAE) of 0.1094 and a mean square error (MSE) of 0.0711, demonstrating its effectiveness in accurately measuring fiber diameters. This research highlights the potential of deep learning to revolutionize fiber analysis in the textile industry, offering a more precise and automated solution for quality control and pricing.

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Atmospheric phase error is the main factor affecting the accuracy of ground-based synthetic aperture radar (GB-SAR). The atmospheric phase screen (APS) may be very complicated, so the atmospheric phase correction (APC) model is very important; in particular, the parameters to be estimated in the model are the key to improving the accuracy of APC. However, the conventional APC method first performs phase unwrapping and then removes the APS based on the least-squares method (LSM), and the general phase unwrapping method is prone to introducing unwrapping error. In particular, the LSM is difficult to apply directly due to the phase wrapping of permanent scatterers (PSs). Therefore, a novel methodology for estimating parameters of the APC model based on the maximum likelihood estimation (MLE) and the Gauss-Newton algorithm is proposed in this paper, which first introduces the MLE method to provide a suitable objective function for the parameter estimation of nonlinear far-end and near-end correction models. Then, based on the Gauss-Newton algorithm, the parameters of the objective function are iteratively estimated with suitable initial values, and the Matthews and Davies algorithm is used to optimize the Gauss-Newton algorithm to improve the accuracy of parameter estimation. Finally, the parameter estimation performance is evaluated based on Monte Carlo simulation experiments. The method proposed in this paper experimentally verifies the feasibility and superiority, which avoids phase unwrapping processing unlike the conventional method.

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We developed a method for making immune responses to bacterial glycans T cell-dependent, which involves attachment of short, synthetic glycans to a virus-like nanoparticle (VLP). This strategy enhances immune responses to glycans by facilitating cognate T cell help of B cells, leading to antibody class switching and affinity maturation yielding high-affinity, anti-glycan antibodies. This method requires synthesis of bacterial glycans as propargyl glycosides for covalent attachment to VLPs, and the resulting short linker between the VLP and glycan is important for optimal T cell receptor recognition. In this work, glycans that are part of the capsular polysaccharides (CPS) produced by Streptococcus pneumoniae serotypes Sp6A and Sp6B were synthesized as disaccharides and trisaccharides. The optimal glycan epitope for antibody binding to the CPS from these serotypes is unknown, and differing "frames" of disaccharides and trisaccharides were prepared to elucidate the optimal antigen for antibody binding.

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This study explores the dosimetric benefits of cone-beam computed tomography (CBCT)-based online adaptive radiation therapy (oART) for a non-small-cell lung cancer (NSCLC) patient exhibiting significant tumor shrinkage during ChemoRT. The patient was prescribed 60 Gray (Gy) in 30 fractions and was initially treated with conventional RT. After the delivery of the first four treatment fractions, the patient's treatment course was converted to oART due to tumor shrinkage seen on CBCT. Current oART dose calculations use a synthetic CT (sCT) image derived from deformable image registration (DIR) of the planning CT to the daily CBCT, and, as the tumor regressed, the discrepancy between the CBCT and the sCT increased, leading to a re-simulation after the delivery of the ninth fraction. In this case report, we first investigated dosimetric differences leveraged by converting this patient from conventional RT to oART. With oART using sCT, the patient's target coverage remained consistent with the reference plan while simultaneously changing lung V20 by 7.8 ± 1.4% and heart mean by 3.4 ± 1.5 Gy. Then, using this new simulation CT and comparing it with iterative CBCT (iCBCT) images acquired with the new HyperSight™ (HS) (Varian Medical Systems, Inc., Palo Alto, CA, USA) imaging system on the Ethos, we investigated the impact of direct dose calculation on HS-iCBCT as compared to sCT. The HS-iCBCT generated a dose distribution similar to the CT reference, achieving a 96.01% gamma passing rate using Task Group-218 (TG-218) criteria. Results indicate that HS-iCBCT has the potential to better reflect daily anatomical changes, resulting in improved dosimetric accuracy. This study highlights the advantages of oART in the presence of tumor response to therapy and underscores HS-iCBCT's potential to provide CT-level dose calculation accuracy in oART for NSCLC patients.

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BACKGROUND: Corynebacterium glutamicum is an attractive host for secretory production of recombinant proteins, including high-value industrial enzymes and therapeutic proteins. The choice of an appropriate signaling peptide is crucial for efficient protein secretion. However, due to the limited availability of signal peptides in C. glutamicum, establishing an optimal secretion system is challenging. RESULT: We constructed a signal peptide library for the isolation of target-specific signal peptides and developed a highly efficient secretory production system in C. glutamicum. Based on the sequence information of the signal peptides of the general secretion-dependent pathway in C. glutamicum, a synthetic signal peptide library was designed, and validated with three protein models. First, we examined endoxylanase (XynA) and one potential signal peptide (C1) was successfully isolated by library screening on xylan-containing agar plates. With this C1 signal peptide, secretory production of XynA as high as 3.2 g/L could be achieved with high purity (> 80%). Next, the signal peptide for ⍺-amylase (AmyA) was screened on a starch-containing agar plate. The production titer of the isolated signal peptide (HS06) reached 1.48 g/L which was 2-fold higher than that of the well-known Cg1514 signal peptide. Finally, we isolated the signal peptide for the M18 single-chain variable fragment (scFv). As an enzyme-independent screening tool, we developed a fluorescence-dependent screening tool using Fluorescence-Activating and Absorption-Shifting Tag (FAST) fusion, and successfully isolated the optimal signal peptide (18F11) for M18 scFv. With 18F11, secretory production as high as 228 mg/L was achieved, which was 3.4-fold higher than previous results. CONCLUSIONS: By screening a fully synthetic signal peptide library, we achieved improved production of target proteins compared to previous results using well-known signal peptides. Our synthetic library provides a useful resource for the development of an optimal secretion system for various recombinant proteins in C. glutamicum, and we believe this bacterium to be a more promising workhorse for the bioindustry.

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Improving crop traits requires genetic diversity, which allows breeders to select advantageous alleles of key genes. In species or loci that lack sufficient genetic diversity, synthetic directed evolution (SDE) can supplement natural variation, thus expanding the possibilities for trait engineering. In this review, we explore recent advances and applications of SDE for crop improvement, highlighting potential targets (coding sequences and cis-regulatory elements) and computational tools to enhance crop resilience and performance across diverse environments. Recent advancements in SDE approaches have streamlined the generation of variants and the selection processes; by leveraging these advanced technologies and principles, we can minimize concerns about host fitness and unintended effects, thus opening promising avenues for effectively enhancing crop traits.

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Escherichia coli depletion of chaperone trigger factor and DnaK/J were not viable at 37°C, but viable below 30°C. Among the engineered E. coli depleted of trigger factor and DnaK/J, one strain Z625, exhibited survival at 37°C, while another strain Z629 only survived below 30°C. Comparative analysis of fatty acid profiles of Z625 and Z629 revealed absence of numerous saturated fatty acids in Z625 as compared to the wild-type E. coli BW25113. In addition, increased unsaturated fatty acids were present in Z625, whereas the fatty acids profile of Z629 closely resembled that of BW25113. Whole genome sequencing revealed a 9-bp insertion in rpoB of Z625. Combined structural analysis of simulated RpoB protein bearing the amino acid sequence L451G452N453 insertion and susceptibility analysis to rifampicin suggested that the insertion did not disturb the individual RpoB structure as beta subunit of RNA polymerase. Comparative transcriptomic analysis of Z625 and Z629 suggested that this insertion impacted transcription of the overall RNA polymerase in Z625, leading to potential repression of some genes whose overexpression was toxic to E. coli. Additionally, Z625 exhibited distinctive metabolic adaptations, likely contributing to its survival at 37°C. In summary, our study elucidated one LGN insertion in rpoB that impacts transcriptional regulation in E. coli, thereby explaining the survival of E. coli depletion of trigger factor and DnaK/J at 37°C, and these founding suggested that some simple mutations in critical genes like rpoB might play an important role in driving adaptive evolution.

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While research on the sourdough microbiome has primarily focused on lactic acid bacteria (LAB) and yeast, recent studies have found that acetic acid bacteria (AAB) are also common members. However, the ecology, genomic diversity, and functional contributions of AAB in sourdough remain unknown. To address this gap, we sequenced 29 AAB genomes, including three that represent putatively novel species, from a collection of over 500 sourdough starters surveyed globally from community scientists. We found variations in metabolic traits related to carbohydrate utilization, nitrogen metabolism, and alcohol production, as well as in genes related to mobile elements and defense mechanisms. Sourdough AAB genomes did not cluster when compared to AAB isolated from other environments, although a subset of gene functions was enriched in sourdough isolates. The lack of a sourdough-specific genomic cluster may reflect the nomadic lifestyle of AAB. To assess the consequences of AAB on the emergent function of sourdough starter microbiomes, we constructed synthetic starter microbiomes, varying only the AAB strain included. All AAB strains increased the acidification of synthetic sourdough starters relative to yeast and LAB by 18.5% on average. Different strains of AAB had distinct effects on the profile of synthetic starter volatiles. Taken together, our results begin to define the ways in which AAB shape emergent properties of sourdough and suggest that differences in gene content resulting from intraspecies diversification can have community-wide consequences on emergent function. IMPORTANCE: This study is a comprehensive genomic and ecological survey of acetic acid bacteria (AAB) isolated from sourdough starters. By combining comparative genomics with manipulative experiments using synthetic microbiomes, we demonstrate that even strains with >97% average nucleotide identity can shift important microbiome functions, underscoring the importance of species and strain diversity in microbial systems. We also demonstrate the utility of sourdough starters as a model system to understand the consequences of genomic diversity at the strain and species level on multispecies communities. These results are also relevant to industrial and home-bakers as we uncover the importance of AAB in shaping properties of sourdough starters that have direct impacts on sensory notes and the quality of sourdough bread.

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The malachite green aptamer (MGapt) is known for its utility in RNA measurement in vivo and in lysate-based cell-free protein systems. However, MGapt fluorescence dynamics do not accurately reflect RNA concentration. Our study finds that MGapt fluorescence is unstable in commercial PURE systems. We discovered that the chemical composition of the cell-free reaction strongly influences MGapt fluorescence, which leads to inaccurate RNA calculations. Specific to the commercial system, we posit that MGapt fluorescence is significantly affected by the system's chemical properties, governed notably by the presence of dithiothreitol (DTT). We propose a model that, on average, accurately predicts MGapt measurement within a 10% margin, leveraging DTT concentration as a critical factor. This model sheds light on the complex dynamics of MGapt in cell-free systems and underscores the importance of considering environmental factors in RNA measurements using aptamers.

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The formation of well-designed synthetic compartments or membraneless organelles for applications in synthetic biology and cellular engineering has aroused enormous interest. However, establishing stable and robust intracellular compartments in bacteria remains a challenge. Here, we use the structured DIX domains derived from Wnt signaling pathway components, more specifically, Dvl2 and Axin1, as building blocks to generate intracellular synthetic compartments in Escherichia coli. Moreover, the aggregation behaviors and physical properties of the DIX-based compartments can be tailored by genetically embedding a specific dimeric domain into the DIX domains. Then, a pair of interacting motifs, consisting of the aforementioned dimeric domain and its corresponding binding ligand, was incorporated to modify the client recruitment pattern of the synthetic compartments. As a proof of concept, the human milk oligosaccharide lacto-N-tetraose (LNT) biosynthesis pathway was selected as a model metabolic pathway. The fermentation results demonstrated that the co-compartmentalization of sequential pathway enzymes into intracellular compartments created by DIX domain, or by the DIX domain in conjunction with interacting motifs, prominently enhanced the metabolic flux and increased LNT production. These synthetic protein compartments may provide a feasible and effective tool to develop versatile organelle-like compartments in bacteria for applications in cellular engineering and synthetic biology.

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Artificial turf, a consumer product growing in usage in the United States, contains diverse chemicals, some of which are endocrine disruptive. Endocrine effects from turf material extracts have been primarily limited to one component, crumb rubber, of these multi-material products. We present in vitro bioactivities from non-weathered and weathered turf sample extracts, including multiple turf components. All weathered samples were collected from real-world turf fields. Non-weathered versus weathered differentially affected the androgen (AR), estrogen (ER), glucocorticoid (GR), and thyroid receptors (TR) in reporter bioassays. While weathered extracts more efficaciously activated peroxisome proliferator activated receptor γ (PPARγ), this did not translate to greater in vitro adipogenic potential. All turf extracts activated the aryl hydrocarbon receptor (AhR). High AhR-efficacy extracts induced modest rat cardiomyoblast toxicity in an AhR-dependent manner. Our data demonstrate potential endocrine and cardiometabolic effects from artificial turf material extracts, warranting further investigation into potential exposures and human health effects.