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Results for “Synthetic Biology”

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bioRxiv2023

A comparative analysis of stably expressed genes across diverse angiosperms exposes flexibility in underlying promoter architecture

Promoters regulate both the amplitude and pattern of gene expression--key factors needed for optimization of many synthetic biology applications. Previous work in Arabidopsis found that promoters that contain a TATA-box element tend to be expressed only under specific conditions or in particular tissues, while promoters which lack any known promoter elements, thus designated as Coreless, tend to be expressed more ubiquitously. To test whether this trend represents a conserved promoter design rule, we identified stably expressed genes across multiple angiosperm species using publicly available RNA-seq data. Comparisons between core promoter architectures and gene expression stability revealed differences in core promoter usage in monocots and eudicots. Furthermore, when tracing the evolution of a given promoter across species, we found that core promoter type was not a strong predictor of expression stability. Our analysis suggests that core promoter types are correlative rather than causative in promoter expression patterns and highlights the challenges in finding or building constitutive promoters that will work across diverse plant species.

plant biology↗

bioRxiv2023

ORBIT for E. coli: Kilobase-scale oligonucleotide recombineering at high throughput and high efficiency

Microbiology and synthetic biology depend on reverse genetic approaches to manipulate bacterial genomes; however, existing methods require molecular biology to generate genomic homology, suffer from low efficiency, and are not easily scaled to high throughput applications. To overcome these limitations, we developed a system for creating kilobase-scale genomic modifications that uses DNA oligonucleotides to direct the integration of a non-replicating plasmid. This method, Oligonucleotide Recombineering followed by Bxb-1 Integrase Targeting (ORBIT) was pioneered in Mycobacteria, and here we adapt and expand it for E. coli. Our redesigned plasmid toolkit achieved nearly 1000x higher efficiency than {lambda} Red recombination and enabled precise, stable knockouts (<134 kb) and integrations (<11 kb) of various sizes. Additionally, we constructed multi-mutants (double and triple) in a single transformation, using orthogonal attachment sites. At high throughput, we used pools of targeting oligonucleotides to knock out nearly all known transcription factor and small RNA genes, yielding accurate, genome-wide, single mutant libraries. By counting genomic barcodes, we also show ORBIT libraries can scale to thousands of unique members (>30k). This work demonstrates that ORBIT for E. coli is a flexible reverse genetic system that facilitates rapid construction of complex strains and readily scales to create sophisticated mutant libraries.

microbiology↗

bioRxiv2023

The Hidden Biocatalytic Potential of the Old Yellow Enzyme Family

The rapid advancement of sequencing technology has created an immense reservoir of protein sequence-function information that has yet to be fully utilized for fundamental or biocatalytic applications. For example, ene reductases from the old yellow enzyme (OYE) family catalyze the asymmetric hydrogenation of activated alkenes with enhanced stereoselectivity - key transformations for sustainable production of pharmaceutical and industrial synthons. Despite the proven biocatalytic application, the OYE family remains relatively underexplored with only 0.1% of identified members having any experimental characterization. Here, a platform of integrated bioinformatics and synthetic biology techniques was employed to systematically organize and screen the natural diversity of the OYE family. Using protein similarity networks, the known and unknown regions of the >115,000 members of the OYE family were broadly explored to identify phylogenetic and sequence-based trends. From this analysis, 118 novel enzymes were characterized across the family to broadly explore and expand the biocatalytic performance and substrate scope of known OYEs. Over a dozen novel enzymes were identified exhibiting enhanced catalytic activity or altered stereospecificity. Beyond well-established ene reduction, we detected widespread occurrence of oxidative chemistry amongst OYE family members at ambient conditions. Crystallography studies of selected OYEs yielded structures for two enzymes, contributing to a better understanding of their unique performance. Their structures revealed an unusual loop conformation within a novel OYE subclass. Overall, our study significantly expands the known functional and chemical diversity of OYEs while identifying superior biocatalysts for asymmetric reduction and oxidation.

biochemistry↗

bioRxiv2023

Distinct phases of cellular signaling revealed by time-resolved protein synthesis

The post-translational regulation of protein function is involved in most cellular processes. As such, synthetic biology tools that operate at this level provide opportunities for manipulating cellular states. Here, we deploy a proximity-triggered protein trans-splicing technology to enable the time-resolved synthesis of target proteins from pre-made parts. The modularity of the strategy allows for the addition or removal of various control elements as a function of the splicing reaction, in the process permitting the cellular location and/or activity state of starting materials and products to be differentiated. The approach is applied to a diverse set of proteins, including the kinase oncofusions BCR/ABL and DNAJB1/PRKACA where dynamic cellular phosphorylation events are dissected, revealing distinct phases of signaling and identifying molecular players connecting the oncofusion to cancer transformation as novel therapeutic targets of cancer cells. We envision that the tools and control strategies developed herein will allow the activity of both naturally occurring and designer proteins to be harnessed for basic and applied research.

biochemistry↗

bioRxiv2023

Interchromosomal contacts between regulatory regions trigger stable transgenerational epigenetic inheritance in Drosophila

Non-genetic information can be inherited across generations by the process of Transgenerational Epigenetic Inheritance (TEI). TEI can be established by various triggering events, including transient genetic perturbations. In Drosophila, hemizygosity of the Fab-7 regulatory element triggers inheritance of the histone mark H3K27me3 at a homologous locus on another chromosome, resulting in heritable epigenetic differences in eye colour. By mutating transcription factor binding sites within the Fab-7 element, we demonstrate the importance of two proteins in the establishment and maintenance of TEI: GAGA-factor and Pleiohomeotic. We show that these proteins function by recruiting the Polycomb Repressive Complex 2 and by mediating interchromosomal chromatin contacts between Fab-7 and its homologous locus. Finally, using an in vivo synthetic biology system to induce them, we show that chromatin contacts alone can establish TEI, providing a mechanism by which hemizygosity of one locus can establish epigenetic memory at another in trans through long-distance chromatin contacts.

genetics↗

bioRxiv2023

Direct, quantitative, and comprehensive analysis of tRNA acylation using intact tRNA liquid-chromatography mass-spectrometry

Aminoacyl-tRNA synthetases (aaRSs) provide the functional and essential link between the sequence of an mRNA and the protein it encodes. aaRS enzymes catalyze a two-step chemical reaction that acylates specific tRNAs with a cognate -amino acid. In addition to their role in translation, acylated tRNAs contribute to non-ribosomal natural product biosynthesis and are implicated in multiple human diseases. From the standpoint of synthetic biology, the acylation of tRNAs with a non-canonical -amino acid (ncAA) or more recently, a non--amino acid monomer (nAA) is a critical first step in the incorporation of these monomers into proteins, where they can be used for fundamental and applied science. These endeavors all demand an understanding of aaRS activity and specificity. Although a number of methods to monitor aaRS function in vitro or in vivo have been developed, many evaluate only the first step of the two-step reaction, require the use of radioactivity, or are slow, difficult to generalize, or both. Here we describe an LC-MS assay that rapidly, quantitatively, and directly monitors aaRS activity by detecting the intact acyl-tRNA product. After a simple tRNA acylation reaction workup, acyl- and non-acyl-tRNA molecules are resolved using ion-pairing reverse phase chromatography and their exact masses are determined using high-resolution time-of-flight mass spectrometry. The intact tRNA assay we describe is fast, simple, and quantifies reaction yields as low as 0.23%. The assay can also be employed on tRNAs acylated with flexizyme to detect products that are undetectable using standard techniques. The protocol requires basic expertise in molecular biology, mass spectrometry, and RNAse-free techniques.

biochemistry↗

bioRxiv2023

USING SMALL MOLECULES TO TURN AN RNA APTAMER INTO SENSORS FOR INORGANIC TARGETS WITHOUT IN VITRO SELECTION

Fluorescent light-up aptamer (FLAP) systems are promising biosensing platforms that can be genetically encoded. Here, we describe how a single FLAP that works with specific organic ligands can detect multiple, structurally unique, non-fluorogenic, and reactive inorganic targets. We developed 4-O-functionalized benzylidene imidazolinones as pre-ligands with suppressed fluorescent binding interactions with the RNA aptamer Baby Spinach. Inorganic targets, hydrogen sulfide (H2S) or hydrogen peroxide (H2O2), can specifically convert these pre-ligands into the native benzylidene imidazolinones, and thus be detected with Baby Spinach. Adaptation of this approach to live cells opened a new opportunity for top-down construction of whole-cell sensors: Escherichia coli transformed with a Baby Spinach-encoding plasmid and incubated with pre-ligands generated fluorescence in response to exogenous H2S or H2O2. Our approach eliminates the requirement of in vitro selection of a new aptamer sequence for molecular target detection, allows for the detection of short-lived targets, thereby advancing FLAP systems beyond their current capabilities. Leveraging the functional group reactivity of small molecules can lead to cell-based sensors for inorganic molecular targets, exploiting a new synergism between synthetic organic chemistry and synthetic biology.

biochemistry↗

bioRxiv2023

Reverse engineering morphogenesis through Bayesian optimization of physics-based models

Morphogenetic programs direct the cell signaling and nonlinear mechanical interactions between multiple cell types and tissue layers to define organ shape and size. A key challenge for systems and synthetic biology is determining optimal combinations of intra- and inter-cellular interactions to predict an organs shape, size, and function. Physics-based mechanistic models that define the subcellular force distribution facilitate this, but it is extremely challenging to calibrate parameters in these models from data. To solve this inverse problem, we created a Bayesian optimization framework to determine the optimal cellular force distribution such that the predicted organ shapes match the desired organ shapes observed within the experimental imaging data. This integrative framework employs Gaussian Process Regression (GPR), a non-parametric kernel-based probabilistic machine learning modeling paradigm, to learn the mapping functions relating to the morphogenetic programs that generate and maintain the final organ shape. We calibrated and tested the method on cross-sections of Drosophila wing imaginal discs, a highly informative model organ system, to study mechanisms that regulate epithelial processes that range from development to cancer. As a specific test case, the parameter estimation framework successfully infers the underlying changes in core parameters needed to match simulation data with time series imaging data of wing discs perturbed with collagenase. Unexpectedly, the framework also identifies multiple distinct parameter sets that generate shapes similar to wild-type organ shapes. This platform enables an efficient, global sensitivity analysis to support the necessity of both actomyosin contractility and basal ECM stiffness to generate and maintain the curved shape of the wing imaginal disc. The optimization framework, combined with fixed tissue imaging, identified that Piezo, a mechanosensitive ion channel, impacts fold formation by regulating the apical-basal balance of actomyosin contractility and elasticity of ECM. This framework is extensible toward reverse-engineering the morphogenesis of any organ system and can be utilized in real-time control of complex multicellular systems.

systems biology↗

bioRxiv2023

SUPREM: an engineered non-site-specific m6A RNA methyltransferase with highly improved efficiency

m6A RNA methylation plays a key role in RNA processing and translational regulation, influencing both normal physiological and pathological processes. Yet, current techniques for studying RNA methylation struggle to isolate the effects of individual m6A modifications. Engineering of RNA methyltransferases (RNA MTases) could enable development of improved synthetic biology tools to manipulate RNA methylation, but it is challenging due to limited understanding of structure-function relationships in RNA MTases. Herein, using ancestral sequence reconstruction we explore the sequence space of the bacterial DNA methyltransferase EcoGII (M.EcoGII), a promising target for protein engineering due to its lack of sequence specificity and its residual activity on RNA. We thereby created an efficient non-specific RNA MTase termed SUPREM, which exhibits 8-fold higher expression levels, 7 {degrees}C higher thermostability, and 12-fold greater m6A RNA methylation activity compared with M.EcoGII. Immunofluorescent staining and quantitative LC/MS-MS analysis confirmed SUPREMs higher RNA methylation activity compared with M.EcoGII in mammalian cells. Additionally, Nanopore direct RNA sequencing highlighted that SUPREM is capable of methylating a larger number of RNA methylation sites than M.EcoGII. Through phylogenetic and mutational analysis, we identified a critical residue for the enhanced RNA methylation activity of SUPREM. Collectively, our findings indicate that SUPREM holds promise as a versatile tool for in vivo RNA methylation and labeling.

biochemistry↗

bioRxiv2023

Advancing Protein-DNA Binding Site Prediction: Integrating Sequence Models and Machine Learning Classifiers

Predicting protein-DNA binding sites is a challenging computational problem in the field of bioinformatics. Identifying the specific residues where proteins bind to DNA is of paramount importance, as it enables the modeling of their interactions and facilitates downstream studies. Nevertheless, the development of accurate and efficient computational methods for this task remains a persistent challenge. Accurate prediction of protein-DNA binding sites has far-reaching implications for understanding molecular mechanisms, disease processes, drug discovery, and synthetic biology applications. It helps bridge the gap between genomics and functional biology, enabling researchers to uncover the intricacies of cellular processes and advance our knowledge of the biological world. The method used to predict DNA binding residues in this study is a potent combination of conventional bioinformatics tools, protein language models, and cutting-edge machine learning and deep learning classifiers. On a dataset of protein-DNA binding sites, our model is meticulously trained, and it is then rigorously examined using several experiments. As indicated by higher predictive behavior with AUC values on two benchmark datasets, the results show superior performance when compared to existing models. The suggested model has a strong capacity for generalization and shows specificity for DNA-binding sites. We further demonstrated the adaptability of our model as a universal framework for binding site prediction by training it on a variety of protein-ligand binding site datasets. In conclusion, our innovative approach for predicting protein-DNA binding residues holds great promise in advancing our understanding of molecular interactions, thus paving the way for several groundbreaking applications in the field of molecular biology and genetics. Our approach demonstrated efficacy and versatility underscore its potential for driving transformative discoveries in biomolecular research.

bioinformatics↗

bioRxiv2023

DeepGEEP: Data-Driven Prediction of Bacterial Biofilm Gene Expression Profiles

Predicting the gene expression profile (GEEP) of bacterial biofilms in response to spatial, temporal, and concentration profiles of stimulus molecules holds significant potential across microbiology, biotechnology, and synthetic biology domains. However, the resource and time-intensive nature of experiments within Petri dishes presents significant challenges. Data-driven methods offer a promising avenue to replace or reduce such experiments given sufficient data. Through wellcrafted data generation techniques, the data scarcity issue can be effectively addressed. In this paper, an innovative methodology is presented for generating GEEP data over a Petri dish that results from a specific chemical stimulus release profile. A twodimensional convolutional neural network (2D-CNN) architecture is subsequently introduced to leverage the synthesized dataset to predict GEEP variations across bacterial biofilms within the Petri dish. The approach, coined DeepGEEP, is applied to data generated by a particle-based simulator (PBS) to enable a flexible evaluation of its efficacy. The proposed method attains a significant level of accuracy in comparison to established benchmark models such as Linear SVM, Radial Basis Function SVM, Decision Tree, and Random Forest.

bioinformatics↗

bioRxiv2023

Comparative analysis of metabolic models of microbial communities reconstructed from automated tools and consensus approaches

Genome-scale metabolic models (GEMs) of microbial communities offer valuable insights into the functional capabilities of their members and facilitate the exploration of microbial interactions. These models are generated using different automated reconstruction tools, each relying on different biochemical databases that may affect the conclusions drawn from the in silico analysis. One way to address this problem is to employ a consensus reconstruction method that combines the outcomes of different reconstruction tools. Here, we conducted a comparative analysis of community models reconstructed from three automated tools, i.e. CarveMe, gapseq, and KBase, alongside a consensus approach, utilizing data from two marine bacterial communities. Our analysis revealed that these reconstruction approaches, while based on the same genomes, resulted in GEMs with varying numbers of genes and reactions as well as metabolic functionalities, attributed to the different databases employed. Further, our results indicated that the set of exchanged metabolites was more influenced by the reconstruction approach rather than the specific bacterial community investigated. This observation suggests a potential bias in predicting metabolite interactions using community GEMs. We also showed that consensus models encompassed a larger number of reactions and metabolites while concurrently reducing the presence of dead-end metabolites. Therefore, the usage of consensus models allows making full and unbiased use from aggregating genes from the different reconstructions in assessing the functional potential of metabolic communities. ImportanceOur study contributes significantly to the field of microbial community modeling through a comprehensive comparison of genome-scale metabolic models (GEMs) generated via various automated tools, including: CarveMe, gapseq, KBase, and a consensus approach. We revealed substantial structural disparities in model outcomes, primarily attributed to variations in the employed databases. A key finding underscored the substantial impact of the reconstruction approach on the set of exchanged metabolites, emphasizing the necessity for enhanced data integration strategies. The consensus models emerge as a powerful solution, exhibiting improved functional capabilities by encompassing a greater number of reactions, metabolites, and genes. This not only offers a more comprehensive representation of metabolic networks within bacterial communities but also shows promise in reducing variability for more accurate predictions of exchange metabolites. Overall, our research provides a critical framework for refining microbial community simulations, impacting fields from ecology to synthetic biology.

systems biology↗

bioRxiv2023

Harnessing the endogenous Type I-C CRISPR-Cas system for genome editing in Bifidobacterium breve

Bifidobacterium breve, one of the main bifidobacterial species colonizing the human gastrointestinal tract in early life, has received extensive attention for its purported beneficial effects on human health. However, exploration of the mode of action of such beneficial effects exerted by B. breve is cumbersome due to the lack of effective genetic tools, which limits its synthetic biology application. Given the widespread presence of endogenous CRISPR-Cas systems in B. breve, the current study developed an endogenous CRISPR-based gene editing toolkit for genetic manipulation of B. breve. Deletion of the gene coding uracil phosphoribosyl-transferase (upp) was achieved in two different B. breve strains using this system. In addition, translational termination of uracil phosphoribosyl-transferase was successfully achieved in B. breve FJSWX38M7 by single-base substitution of the upp gene and insertion of three stop codons. The gene encoding linoleic acid isomerase (bbi) in B. breve, being a characteristic trait, was deleted after plasmid curing, which rendered it unable to convert linoleic acid into conjugated linoleic acid, demonstrating the feasibility of successive editing. This study expanded the gene manipulation toolkit of B. breve and provides a reference for functional genome editing and analysis using an endogenous CRISPR-Cas system in Bifidobacterium. ImportanceThe lack of effective genetic tools for Bifidobacterium breve is an obstacle to studying the molecular mechanisms of its health-promoting effects, hindering the development of next-generation probiotics. Here, we introduce a gene editing method based on the endogenous CRISPR-Cas system, which can achieve gene deletion, single base substitution, gene insertion and continuous gene editing in B. breve. This study will promote the excavation of functional genes and elucidation of molecular mechanisms of B. breve.

molecular biology↗

bioRxiv2023

PhysioFit: a software to quantify cell growth parameters and extracellular fluxes

SummaryQuantification of growth parameters and extracellular uptake and production fluxes is central in systems and synthetic biology. Fluxes can be estimated using various mathematical models by fitting time-course measurements of the concentration of cells and extracellular substrates and products. A single tool is available to non-computational biologists to calculate extracellular fluxes, but it is hardly interoperable and is limited to a single hard-coded growth model. We present our open-source flux calculation software, PhysioFit, which can be used with any growth model and is interoperable by design. PhysioFit includes some of the most common growth models, and advanced users can implement additional models to calculate extracellular fluxes and other growth parameters for metabolic systems or experimental setups that follow alternative kinetics. PhysioFit can be used as a Python library and offers a graphical user interface for intuitive use by end-users and a command-line interface to streamline integration into existing pipelines. Availability and ImplementationPhysioFit is implemented in Python 3 and was tested on Windows, Unix and MacOS platforms. PhysioFit is also freely available online at https://workflow4metabolomics.org. The source code, the data and the documentation are freely distributed under GPL3 license at https://github.com/MetaSys-LISBP/PhysioFit/ and https://physiofit.readthedocs.io/.

bioinformatics↗

bioRxiv2023

The H3.3 K36M oncohistone disrupts the establishment of epigenetic memory through loss of DNA methylation

Histone H3.3 is frequently mutated in cancers, with the lysine 36 to methionine mutation (K36M) being a hallmark of chondroblastomas. While it is known that H3.3K36M changes the cellular epigenetic landscape, it remains unclear how it affects the dynamics of gene expression. Here, we use a synthetic reporter to measure the effect of H3.3K36M on silencing and epigenetic memory after recruitment of KRAB: a member of the largest class of human repressors, commonly used in synthetic biology, and associated with H3K9me3. We find that H3.3K36M, which decreases H3K36 methylation, leads to a decrease in epigenetic memory and promoter methylation weeks after KRAB release. We propose a new model for establishment and maintenance of epigenetic memory, where H3K36 methylation is necessary to convert H3K9me3 domains into DNA methylation for stable epigenetic memory. Our quantitative model can inform oncogenic mechanisms and guide development of epigenetic editing tools.

cell biology↗

bioRxiv2023

Perturbation-response analysis of in silico metabolic dynamics: Hard-coded responsiveness in the cofactor and network sparsity

Homeostasis is a fundamental characteristic of living systems. Unlike rigidity, homeostasis necessitates that systems respond flexibly to diverse environments. Understanding the dynamics of biochemical systems when subjected to perturbations is essential for the development of a quantitative theory of homeostasis. In this study, we analyze the response of bacterial metabolism to externally imposed perturbations using kinetic models of Escherichia colis central carbon metabolism in nonlinear regimes. We found that three distinct kinetic models consistently display strong responses to perturbations; In the strong responses, minor initial discrepancies in metabolite concentrations from steady-state values amplify over time, resulting in significant deviations. This pronounced responsiveness is a characteristic feature of metabolic dynamics, especially since such strong responses are seldom seen in toy models of the metabolic network. Subsequent numerical studies show that adenyl cofactors consistently influence the responsiveness of the metabolic systems across models. Additionally, we examine the impact of network structure on metabolic dynamics, demonstrating that as the metabolic network becomes denser, the perturbation response diminishes--a trend observed commonly in the models. To confirm the significance of cofactors and network structure, we constructed a simplified metabolic network model, underscoring their importance. By identifying the structural determinants of responsiveness, our findings offer implications for bacterial physiology, the evolution of metabolic networks, and the design principles for robust artificial metabolism in synthetic biology and bioengineering.

systems biology↗

bioRxiv2023

Deep Learning enables reliable and comprehensive profiling of invertible promoters in microbes

Invertible promoters (invertons) are regulatory elements found in bacteria, with inverted repeat sequences at both ends, leading to alternating changes in the expression of the regulated genes. Since invertons were present in more than 20% of bacterial genomes, while they regulated more than 5% of genes in these genomes, they are of pivotal importance for microbial functional dynamics especially when under stress. However, the prevalence of invertons, as well as the full spectrum of gene functions regulated by them, remain poorly understood. In this study, we developed DeepInverton, a deep learning model capable of accurately identifying novel inverton sequences without sequencing reads, which could profile inverton sequences from large genomic and metagenomic datasets. We conducted a pan-genomic and pan-metagenomic analysis of invertons on 68,969 bacterial genomes and 8,516 metagenome samples, resulting in a comprehensive overview of more than 200,000 nonredundant invertons and their regulated gene functional patterns. This result suggests that invertons, as a key player for bacterial adaptation to environmental stresses, are prevalent in bacterial genomes. Among the genomes analyzed, we observed a profound enrichment of invertons in pathogen such as Bordetella pertussis, and discovered a significant increase of inverton enrichment rates in strains associated with recent pertussis outbreaks, as well as novel evolving strains, unveiling a hidden link between the evolution of Bordetella pertussis and its inverton enrichment. We also utilized DeepInverton to explore inverton profiles mong human and marine metagenomes. Results revealed an unprecedented diversity of functional genes regulated by invertons, including antimicrobial resistance, biofilm formation and flagella, indicating their potential role in facilitating environmental adaptation. The in vitro experiments have confirmed the functions of tens of novel invertons that we have identified. Overall, we developed the DeepInverton model for exploration of invertons at unprecedented scale, which enabled our comprehensive profiling of invertons and their regulated genes. The comprehensive inverton profiles have deepen our understanding of invertons at pan-genome and metagenome scale, and could enabled a broad spectrum of inverton-related applications in microbial ecology and synthetic biology.

bioinformatics↗

bioRxiv2023

Engineering water exchange is a safe and effective method for magnetic resonance imaging in diverse cell types

Aquaporin-1 (Aqp1), a water channel, has garnered significant interest for cell-based medicine and in vivo synthetic biology due to its ability to be genetically encoded to produce magnetic resonance signals by increasing the rate of water diffusion in cells. However, concerns regarding the effects of Aqp1 overexpression and increased membrane diffusivity on cell physiology have limited its widespread use as a deep-tissue reporter. In this study, we present evidence that Aqp1 generates strong diffusion-based magnetic resonance signals without adversely affecting cell viability or morphology in diverse cell lines derived from mice and humans. Our findings indicate that Aqp1 overexpression does not induce ER stress, which is frequently associated with heterologous expression of membrane proteins. Furthermore, we observed that Aqp1 expression had no detrimental effects on native biological activities, such as phagocytosis, immune response, insulin secretion, and tumor cell migration in the analyzed cell lines. These findings should serve to alleviate any lingering safety concerns regarding the utilization of Aqp1 as a genetic reporter and should foster its broader application as a noninvasive reporter for in vivo studies.

bioengineering↗