Editing Methods Research Articles

Precise Structural and Dynamical Details in Zeolites Revealed by Coupling-Edited 1H-17O Double Resonance NMR Spectroscopy.

Despite the extensive industrial and research interests in zeolites, their intrinsic catalytic nature is not fully understood due to the complexity of the hydroxyl-aluminum moieties. 17O NMR would provide irreplaceable opportunities for much-needed fine structural determination given the ubiquitous presence of oxygen atoms in nearly all species; however, the low sensitivity and quadrupolar nature of oxygen-17 make its NMR spectroscopic elucidation challenging. Here, we show that state-of-the-art double resonance solid-state NMR techniques have been combined with spectral editing methods based on scalar (through-bond) and dipolar (through-space) couplings, which allowed us to address the subtle protonic structures in zeolites. Notably, the often-neglected and undesired second-order quadrupolar-dipolar cross-term interaction ("2nd-QD interaction") can actually be exploited and can help gain invaluable information. Eventually, a comprehensive set of 1H-17O/1H-27Al double resonance NMR with J-/D-coupling spectral editing techniques have been designed in this work and enabled us to reveal atomic-scale precise structural and dynamical details in zeolites including: 1) The jump rate of the bridging acid site (BAS) proton is relatively low, i.e., far less than 100 s-1 at room temperature. 2) The Al-OH groups with 1H chemical shift at 2.6-2.8 ppm, at least for nonseverely dealuminated H-ZSM-5 catalysts, exhibit a rigid bridging environment similar to that of BAS. 3) The Si-OH groups at 2.0 ppm are not hydrogen bonded and undergo fast cone-rotational motion. The results in this study predict the 2nd-QD interaction to be universal for any rigid -17O-H environment, such as those in metal oxide surfaces or biomaterials.

HyperEditor: Achieving Both Authenticity and Cross-Domain Capability in Image Editing via Hypernetworks

Editing real images authentically while also achieving cross-domain editing remains a challenge. Recent studies have focused on converting real images into latent codes and accomplishing image editing by manipulating these codes. However, merely manipulating the latent codes would constrain the edited images to the generator's image domain, hindering the attainment of diverse editing goals. In response, we propose an innovative image editing method called HyperEditor, which utilizes weight factors generated by hypernetworks to reassign the weights of the pre-trained StyleGAN2's generator. Guided by CLIP's cross-modal image-text semantic alignment, this innovative approach enables us to simultaneously accomplish authentic attribute editing and cross-domain style transfer, a capability not realized in previous methods. Additionally, we ascertain that modifying only the weights of specific layers in the generator can yield an equivalent editing result. Therefore, we introduce an adaptive layer selector, enabling our hypernetworks to autonomously identify the layers requiring output weight factors, which can further improve our hypernetworks' efficiency. Extensive experiments on abundant challenging datasets demonstrate the effectiveness of our method.

History Matters: Temporal Knowledge Editing in Large Language Model

The imperative task of revising or updating the knowledge stored within large language models arises from two distinct sources: intrinsic errors inherent in the model which should be corrected and outdated knowledge due to external shifts in the real world which should be updated. Prevailing efforts in model editing conflate these two distinct categories of edits arising from distinct reasons and directly modify the original knowledge in models into new knowledge. However, we argue that preserving the model's original knowledge remains pertinent. Specifically, if a model's knowledge becomes outdated due to evolving worldly dynamics, it should retain recollection of the historical knowledge while integrating the newfound knowledge. In this work, we introduce the task of Temporal Knowledge Editing (TKE) and establish a benchmark AToKe (Assessment of TempOral Knowledge Editing) to evaluate current model editing methods. We find that while existing model editing methods are effective at making models remember new knowledge, the edited model catastrophically forgets historical knowledge. To address this gap, we propose a simple and general framework termed Multi-Editing with Time Objective (METO) for enhancing existing editing models, which edits both historical and new knowledge concurrently and optimizes the model's prediction for the time of each fact. Our assessments demonstrate that while AToKe is still difficult, METO maintains the effectiveness of learning new knowledge and meanwhile substantially improves the performance of edited models on utilizing historical knowledge.

TexFit: Text-Driven Fashion Image Editing with Diffusion Models

Fashion image editing aims to edit an input image to obtain richer or distinct visual clothing matching effects. Existing global fashion image editing methods are difficult to achieve rich outfit combination effects while local fashion image editing is more in line with the needs of diverse and personalized outfit matching. The local editing techniques typically depend on text and auxiliary modalities (e.g., human poses, human keypoints, garment sketches, etc.) for image manipulation, where the auxiliary modalities essentially assist in locating the editing region. Since these auxiliary modalities usually involve additional efforts in practical application scenarios, text-driven fashion image editing shows high flexibility. In this paper, we propose TexFit, a Text-driven Fashion image Editing method using diffusion models, which performs the local image editing only with the easily accessible text. Our approach employs a text-based editing region location module to predict precise editing region in the fashion image. Then, we take the predicted region as the generation condition of diffusion models together with the text prompt to achieve precise local editing of fashion images while keeping the rest part intact. In addition, previous fashion datasets usually focus on global description, lacking local descriptive information that can guide the precise local editing. Therefore, we develop a new DFMM-Spotlight dataset by using region extraction and attribute combination strategies. It focuses locally on clothes and accessories, enabling local editing with text input. Experimental results on the DFMM-Spotlight dataset demonstrate the effectiveness of our model. Code and Datasets are available at https://texfit.github.io/.

Towards Efficient Diffusion-Based Image Editing with Instant Attention Masks

Diffusion-based Image Editing (DIE) is an emerging research hot-spot, which often applies a semantic mask to control the target area for diffusion-based editing. However, most existing solutions obtain these masks via manual operations or off-line processing, greatly reducing their efficiency. In this paper, we propose a novel and efficient image editing method for Text-to-Image (T2I) diffusion models, termed Instant Diffusion Editing (InstDiffEdit). In particular, InstDiffEdit aims to employ the cross-modal attention ability of existing diffusion models to achieve instant mask guidance during the diffusion steps. To reduce the noise of attention maps and realize the full automatics, we equip InstDiffEdit with a training-free refinement scheme to adaptively aggregate the attention distributions for the automatic yet accurate mask generation. Meanwhile, to supplement the existing evaluations of DIE, we propose a new benchmark called Editing-Mask to examine the mask accuracy and local editing ability of existing methods. To validate InstDiffEdit, we also conduct extensive experiments on ImageNet and Imagen, and compare it with a bunch of the SOTA methods. The experimental results show that InstDiffEdit not only outperforms the SOTA methods in both image quality and editing results, but also has a much faster inference speed, i.e., +5 to +6 times. Our code available at https://anonymous.4open.science/r/InstDiffEdit-C306

MELO: Enhancing Model Editing with Neuron-Indexed Dynamic LoRA

Large language models (LLMs) have shown great success in various Natural Language Processing (NLP) tasks, whist they still need updates after deployment to fix errors or keep pace with the changing knowledge in the world. Researchers formulate such problem as Model Editing and have developed various editors focusing on different axes of editing properties. However, current editors can hardly support all properties and rely on heavy computational resources. In this paper, we propose a plug-in Model Editing method based on neuron-indexed dynamic LoRA (MELO), which alters the behavior of language models by dynamically activating certain LoRA blocks according to the index built in an inner vector database. Our method satisfies various editing properties with high efficiency and can be easily integrated into multiple LLM backbones. Experimental results show that our proposed MELO achieves state-of-the-art editing performance on three sequential editing tasks (document classification, question answering and hallucination correction), while requires the least trainable parameters and computational cost.

First-class - biosynthesis of 6-MSA and bostrycoidin type I polyketides in Yarrowia lipolytica.

Fungal polyketides are a large group of secondary metabolites, valuable due to their diverse spectrum of pharmacological activities. Polyketide biosynthesis in filamentous fungi presents some challenges: small yield and low-purity titers. To tackle these issues, we switched to the yeast Yarrowia lipolytica, an easily cultivable heterologous host. As an oleaginous yeast, Y. lipolytica displays a high flux of acetyl- and malonyl-CoA precursors used in lipid synthesis. Likewise, acetyl- and malonyl-CoA are the building blocks of many natural polyketides, and we explored the possibility of redirecting this flux toward polyketide production. Despite its promising prospect, Y. lipolytica has so far only been used for heterologous expression of simple type III polyketide synthases (PKSs) from plants. Therefore, we decided to evaluate the potential of Y. lipolytica by targeting the more complex fungal polyketides synthesized by type I PKSs. We employed a CRISPR-Cas9-mediated genome editing method to achieve markerless gene integration of the genes responsible for bostrycoidin biosynthesis in Fusarium solani (fsr1, fsr2, and fsr3) and 6-methylsalicylic acid (6-MSA) biosynthesis in Aspergillus hancockii (6MSAS). Moreover, we attempted titer optimization through metabolic engineering by overexpressing two enzymes, TGL4 and AOX2, involved in lipid β-oxidation, but we did not observe an effect on polyketide production. With maximum titers of 403 mg/L 6-MSA and 35 mg/L bostrycoidin, the latter being substantially higher than our previous results in Saccharomyces cerevisiae (2.2 mg/L), this work demonstrates the potential of Y. lipolytica as a platform for heterologous production of complex fungal polyketides.

Surface-Enhanced Raman Spectroscopy for Monitoring the Biochemical Changes Due to DNA Mutations Induced by CRISPR-Cas9 Genome Editing in the Aspergillus niger Fungus.

In this study, surface-enhanced Raman spectroscopy (SERS) technique, along with principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA), is used as a simple, quick, and cost-effective analysis method for identifying biochemical changes occurring due to induced mutations in the Aspergillus niger fungus strain. The goal of this study is to identify the biochemical changes in the mutated fungal cells (cell mass) as compared to the control/nonmutated cells. Furthermore, multivariate data analysis tools, including PCA and PLS-DA, are used to further confirm the differentiating SERS spectral features among fungal samples. The mutations are caused in A. niger by the clustered regularly interspaced palindromic repeat CRISPR-Cas9 genomic editing method to improve their biotechnological potential for the production of cellulase enzyme. SERS was employed to detect the changes in the cells of mutated A. niger fungal strains, including one mutant producing low levels of an enzyme and another mutant producing high levels of the enzyme as a result of mutation as compared with an unmutated fungal strain as a control sample. The distinctive features of SERS corresponding to nucleic acids and proteins appear at 546, 622, 655, 738, 802, 835, 959, 1025, 1157, 1245, 1331, 1398, and 1469 cm-1. Furthermore, PLS-DA is used to confirm the 89% accuracy, 87.7% precision, 87% sensitivity, and 88.9% specificity of this method, and the value of the area under the curve (AUROC) is 0.67. It has been shown that surface-enhanced Raman spectroscopy is an effective method for identifying and differentiating biochemical changes in genome-modified fungal samples.

An efficient and cost-effective method for disrupting genes in RAW264.7 macrophages using CRISPR-Cas9.

The clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated protein 9 (Cas9) are widely used for genome editing in cultured cell lines. However, the implementation of genome editing is still challenging due to the complex and often costly multi-step process associated with this technique. Moreover, the efficiency of genome editing varies across cell types, often limiting utility. Herein, we describe pCRISPR-EASY, a vector for simplified cloning of single guide RNAs (sgRNAs) and its simultaneous introduction with CRISPR-Cas9 into cultured cells using a non-viral delivery system. We outline a comprehensive, step-by-step protocol for genome editing in RAW264.7 macrophages, a mouse macrophage cell line widely used in biomedical research for which genome editing using CRISPR-Cas9 has been restricted to lentiviral or expensive commercial reagents. This provides an economical, highly efficient and reliable method for genome editing that can easily be adapted for use in other systems.

Streamlined, single-step non-viral CRISPR-Cas9 knockout strategy enhances gene editing efficiency in primary human chondrocyte populations

BackgroundCRISPR-Cas9-based genome engineering represents a powerful therapeutic tool for cartilage tissue engineering and for understanding molecular pathways driving cartilage diseases. However, primary chondrocytes are difficult to transfect and rapidly dedifferentiate during monolayer (2D) cell culture, making the lengthy expansion of a single-cell-derived edited clonal population not feasible. For this reason, functional genetics studies focused on cartilage and rheumatic diseases have long been carried out in cellular models that poorly recapitulate the native molecular properties of human cartilaginous tissue (e.g., cell lines, induced pluripotent stem cells). Here, we set out to develop a non-viral CRISPR-Cas9, bulk-gene editing method suitable for chondrocyte populations from different cartilaginous sources.MethodsWe screened electroporation and lipid nanoparticles for ribonucleoprotein (RNP) delivery in primary polydactyly chondrocytes, and optimized RNP reagents assembly. We knocked out RELA (also known as p65), a subunit of the nuclear factor kappa B (NF-κB), in polydactyly chondrocytes and further characterized knockout (KO) cells with RT-qPCR and Western Blot. We tested RELA KO in chondrocytes from diverse cartilaginous sources and characterized their phenotype with RT-qPCR. We examined the chondrogenic potential of wild-type (WT) and KO cell pellets in presence and absence of interleukin-1β (IL-1β).ResultsWe established electroporation as the optimal transfection technique for chondrocytes enhancing transfection and editing efficiency, while preserving high cell viability. We knocked out RELA with an unprecedented efficiency of ~90%, confirming lower inflammatory pathways activation upon IL-1β stimulation compared to unedited cells. Our protocol could be easily transferred to primary human chondrocytes harvested from osteoarthritis (OA) patients, human FE002 chondroprogenitor cells, bovine chondrocytes, and a human chondrocyte cell line, achieving comparable mean RELA KO editing levels using the same protocol. All KO pellets from primary human chondrocytes retained chondrogenic ability equivalent to WT cells, and additionally displayed enhanced matrix retention under inflamed conditions.ConclusionsWe showcased the applicability of our bulk gene editing method to develop effective autologous and allogeneic off-the-shelf gene therapies strategies and to enable functional genetics studies in human chondrocytes to unravel molecular mechanisms of cartilage diseases.

Novel electroporation-based genome editing of carnation plant tissues using RNPs targeting the anthocyanidin synthase gene.

A novel electroporation method for genome editing was performed using plant tissue samples by direct RNPs-introduction in carnation. Genome editing is becoming a very useful tool in plant breeding. In this study, a novel electroporation method was performed for genome editing using plant tissue samples. The objective was to create a flower color mutant using the pink-flowered carnation 'Kane Ainou 1-go'. For this purpose, a ribonucleoprotein consisting of guide RNA and clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein 9 (Cas9) was introduced into the stem tissue to induce mutations in the anthocyanidin synthase (ANS) gene, which is involved in anthocyanin biosynthesis. As the ANS of 'Kane Ainou 1-go' has not been previously isolated, we initially isolated the ANS gene from 'Kane Ainou 1-go' for characterization. Southern hybridization analysis confirmed that the ANS gene was present in the genome as a two-allele gene with a pair of homologous sequences (ANS-1 and 2); these sequences were used as the target for genome editing. Genome editing was performed by introducing #2_single-guide RNA into the stem tissue using the ribonucleoprotein. This molecule was used because it exhibited the highest efficiency in an analysis of cleavage activity against the target sequence in vitro. Cleaved amplified polymorphic sequence analysis of genomic DNA extracted from 85 regenerated individuals after genome editing was performed. The results indicated that mutations in the ANS gene may have been introduced into two lines. Cloning of the ANS gene in these two lines confirmed the introduction of a single nucleotide substitution mutation for ANS-1 in both lines, and a single amino acid substitution in one line. We discussed the possibility of color change by the amino acid substitution, and also the future applications of this technology.

Glycosylation as a tracer of off-target Cre-lox activation in development.

The Cre-lox system is one of the most widely used methods for lineage-specific and inducible genome editing in vivo. However, incomplete penetrance and off-target effects due to transient promoter expression in a stem or pluripotent precursor cell can be problematic and difficult to detect, especially if the target gene is not normally present in the fully differentiated but off-target cells. Yet, the loss of the target gene through the transient expression of Cre may impact the differentiation of those cells by virtue of transient expression in a precursor population. In these situations, off-target effects in an unknown precursor cell can, at best, complicate conclusions drawn from the model, and at worst, invalidate all data generated from that knockout strain. Thus, identifying Cre-driver promoter expression along entire cell lineages is crucial to improve rigor and reproducibility. As an example, transient expression in an early precursor cell has been documented in a variety of Cre strains such as the Tie2-based Cre-driver system that is used as an "endothelial cell-specific" model 1. Yet, Tie2 is now known to be transiently expressed in a stem cell upstream of both hematopoietic and endothelial cell lineages. Here, we use the Tie2 Cre-driver strain to demonstrate that due to its ubiquitous nature, plasma membrane glycans are a useful marker of both penetrance and specificity of a Cre-based knockout.

A Redactable Blockchain-Based Data Management Scheme for Agricultural Product Traceability.

With the development of agricultural information technology, the Internet of Things and blockchain have become important in the traceability of agricultural products. Sensors collect real-time data in agricultural production and a blockchain provides a secure and transparent storage medium for these data, which improves the transparency and credibility of agricultural product traceability. However, existing agricultural product traceability solutions are limited by the immutability of the blockchain, making it difficult to delete erroneous data and modify the scope of data sharing. This damages the credibility of traceability data and is not conducive to the exchange and sharing of information among enterprises. In this article, we propose an agricultural product traceability data management scheme based on a redactable blockchain. This scheme allows agricultural enterprises to encrypt data to protect privacy. In order to facilitate the maintenance and sharing of data, we introduce a chameleon hash function to provide data modification capabilities. Enterprises can fix erroneous data and update the access permissions of the data. To improve the efficiency of block editing, our scheme adopts a distributed block editing method. This method supports threshold editing operations, avoiding single-point-of-failure issues. We save records of data modifications on the blockchain and establish accountability mechanisms to identify malicious entities. Finally, in this paper we provide a security analysis of our proposed solution and verify its effectiveness through experiments. Compared with the existing scheme, the block generating speed is improved by 42% and the block editing speed is improved by 29.3% at 125 nodes.

Integrating Digital Editions and Methods for Text Editing and Analysis in Undergraduate Literary Studies

This article evaluates the integration of digital editions, computational text analysis and digital scholarly editing in the context of an introductory undergraduate course on Italian literature and digital humanities taught at a US university. It offers specific examples of employing the apparatus of several digital platforms dedicated to the study of foundational authors in the Italian literary tradition (Dante, Petrarch, Boccaccio and Leopardi), and of gaining familiarity with a suite of digital tools for text analysis and editing, namely Voyant Tools, Recogito, Oxygen, Gephi, Transkribus Lite and OpenRefine. The discussion of digital project interfaces examines the student user experience of different design approaches, while the illustrations of tool exercises explore how these could support the close attention to a text and facilitate the navigation between its micro and macro frameworks of interpretation. The article furthermore suggests that digital text analysis could reinforce student appreciation of the signifying value of textual form and genre, and that the pedagogical method of digital text editing creates opportunities for situated learning. In conclusion, it argues that the academic work of students at the undergraduate level could be harnessed by the scaffolded methods of faculty-led digital research projects and contribute to the creation of public knowledge.

An Elevation Editing Method for Water Bodies in InSAR derived DSM

In the InSAR-DSM, which is the Digital Surface Model (DSM) acquired through Interferometric Synthetic Aperture Radar (InSAR), the presence of significant noise, voids, and elevation anomalies in water bodies is pervasive. These disparities arise from factors such as diminished backscattering due to electromagnetic wave absorption by water, surface-based specular reflection, and the impact of temporal decorrelation. To tackle this challenge, an automated elevation editing method for water bodies based on InSAR-DSM is introduced. Water bodies are automatically categorized as oceans, lakes, or rivers based on their morphological characteristics and location criteria. Distinct algorithms are subsequently deployed for elevation editing within each category. Results demonstrate that after editing water body elevations, all missing values within the original InSAR-DSM for water bodies are effectively addressed. Elevation anomalous pixels within water bodies are reduced by 68.5%, and the elevation standard deviation, which reflects elevation variability in water bodies, is decreased by 47.31%. This process effectively rectifies water body elevations within InSAR-DSM.

Atherosclerosis: an overview of mouse models and a detailed methodology to quantify lesions in the aortic root.

Cardiovascular disease, the primary cause of human mortality globally, is predominantly caused by a progressive disorder known as atherosclerosis. Atherosclerosis refers to the process of accumulation of cholesterol-enriched lipoproteins and the concomitant initiation of inflammatory processes in the arterial wall, including the recruitment of immune cells. This leads to the formation of atherosclerotic plaques, initially causing a thickening of the arterial wall and narrowing of arteries. However, as plaque formation progresses, atherosclerotic plaques may become unstable and rupture, leading to a blood clot that blocks the affected artery or travels through the blood to block blood flow elsewhere. In the early 1990s, emerging gene editing methods enabled the development of apolipoprotein E knockout (Apoe-/- ) and low-density lipoprotein receptor knockout (Ldlr-/- ) mice. These mice have been instrumental in unraveling the complex pathogenesis of atherosclerosis. Around the same time, human APOE*3-Leiden transgenic mice were generated, which were more recently cross-bred with human cholesteryl ester transfer protein (CETP) transgenic mice to generate APOE*3-Leiden.CETP mice. This model appears to closely mimic human lipoprotein metabolism and responds to classic lipid-lowering interventions due to an intact ApoE-LDLR pathway of lipoprotein remnant clearance. In this review, we describe the role of lipid metabolism and inflammation in atherosclerosis development and highlight the characteristics of the frequently used animal models to study atherosclerosis, with a focus on mouse models, discussing their advantages and limitations. Moreover, we present a detailed methodology to quantify atherosclerotic lesion area within the aortic root region of the murine heart, as well as details required for scoring atherosclerotic lesion severity based on guidelines of the American Heart Association adapted for mice.

PRLDPC: A Heuristics Prototype Reduction Method Based on Supervised Local Density Clustering for Instance-Based Classifiers

The prototype reduction (PR) methods, as an important data pre-processing task, can improve instance-based classifiers by removing noise and/or redundant samples. Recently, a series of PR methods with different heuristic strategies have been developed. Among them, clustering-based PR methods have shown competitive performance. Yet, they still suffer from the following issues: (a) most methods heavily rely on parameters; (b) most fail to remove suspicious noisy samples from the training set; (c) almost all fail to handle manifold data with nonspherical distributions effectively; (d) some have a relatively high time complexity. To advance the state of the art of clustering-based PR methods by overcoming the above issues, a novel heuristics PR method based on supervised local density peaks clustering (PRLDPC) is proposed. The main ideas of PRLDPC are concluded as follows: (a) a supervised local density peaks clustering (SLDPC) is first proposed to divide the training set into homogeneous and heterogeneous sub-clusters; (b) SLDPC-based edition method is second proposed to identify and remove noisy samples from heterogeneous sub-clusters; (c) an SLDPC-based condensing method is third proposed to obtain reduced samples from homogeneous sub-clusters and pruned heterogeneous sub-clusters. Intensive experiments have proven that (a) PRLDPC can outperform six state-of-the-art PR methods on extensive UCI and Kaggle data sets in weighing the reduction rate and classification accuracy of three instance-based classifiers; (b) PRLDPC is relatively fast and has a relatively low time complexity [Formula: see text].

A method of genetic transformation and gene editing of succulents without tissue culture.

Succulents, valued for their drought tolerance and ornamental appeal, are important in the floriculture market. However, only a handful of succulent species can be genetically transformed, making it difficult to improve these plants through genetic engineering. In this study, we adapted the recently developed cut-dip-budding (CDB) gene delivery system to transform three previously recalcitrant succulent varieties - the dicotyledonous Kalanchoe blossfeldiana and Crassula arborescens and the monocotyledonous Sansevieria trifasciata. Capitalizing on the robust ability of cut leaves to regenerate shoots, these plants were successfully transformed by directly infecting cut leaf segments with the Agrobacterium rhizogenes strain K599. The transformation efficiencies were approximately 74%, 5% and 3.9%-7.8%, respectively, for K. blossfeldiana and C. arborescens and S. trifasciata. Using this modified CDB method to deliver the CRISPR/Cas9 construct, gene editing efficiency in K. blossfeldiana at the PDS locus was approximately 70%. Our findings suggest that succulents with shoot regeneration ability from cut leaves can be genetically transformed using the CDB method, thus opening up an avenue for genetic engineering of these plants.

Evaluation of Parameters Affecting Agrobacterium-Mediated Transient Gene Expression in Industrial Hemp (Cannabis sativa L.).

Industrial hemp Cannabis sativa L. is an economically important crop mostly grown for its fiber, oil, and seeds. Due to its increasing applications in the pharmaceutical industry and a lack of knowledge of gene functions in cannabinoid biosynthesis pathways, developing an efficient transformation platform for the genetic engineering of industrial hemp has become necessary to enable functional genomic and industrial application studies. A critical step in the development of Agrobacterium tumefaciens-mediated transformation in the hemp genus is the establishment of optimal conditions for T-DNA gene delivery into different explants from which whole plantlets can be regenerated. As a first step in the development of a successful Agrobacterium tumefaciens-mediated transformation method for hemp gene editing, the factors influencing the successful T-DNA integration and expression (as measured by transient β-glucuronidase (GUS) and Green Florescent Protein (GFP) expression) were investigated. In this study, the parameters for an agroinfiltration system in hemp, which applies to the stable transformation method, were optimized. In the present study, we tested different explants, such as 1- to 3-week-old leaves, cotyledons, hypocotyls, root segments, nodal parts, and 2- to 3-week-old leaf-derived calli. We observed that the 3-week-old leaves were the best explant for transient gene expression. Fully expanded 2- to 3-week-old leaf explants, in combination with 30 min of immersion time, 60 µM silver nitrate, 0.5 µM calcium chloride, 150 µM natural phenolic compound acetosyringone, and a bacterial density of OD600nm = 0.4 resulted in the highest GUS and GFP expression. The improved method of genetic transformation established in the present study will be useful for the introduction of foreign genes of interest, using the latest technologies such as genome editing, and studying gene functions that regulate secondary metabolites in hemp.

Distribution analysis of TRH in Bactrocera dorsalis using a CRISPR/Cas9-mediated reporter knock-in strain.

Although the study of many genes and their protein products is limited by the availability of high-quality antibodies, this problem could be solved by fusing a tag/reporter to an endogenous gene using a gene-editing approach. The type II bacterial CRISPR/Cas system has been demonstrated to be an efficient gene-targeting technology for many insects, including the oriental fruit fly Bactrocera dorsalis. However, knocking in, an important editing method of the CRISPR/Cas9 system, has lagged in its application in insects. Here, we describe a highly efficient homology-directed genome editing system for B. dorsalis that incorporates coinjection of embryos with Cas9 protein, guide RNA and a short single-stranded oligodeoxynucleotide donor. This one-step procedure generates flies carrying V5 tag (42 bp) in the BdorTRH gene. In insects, as in other invertebrates and in vertebrates, the neuronal tryptophan hydroxylase (TRH) gene encodes the rate-limiting enzyme for serotonin biosynthesis in the central nervous system. Using V5 monoclonal antibody, the distribution of TRH in B. dorsalis at different developmental stages was uncovered. Our results will facilitate the generation of insects carrying precise DNA inserts in endogenous genes and will lay foundation for the investigation of the neural mechanisms underlying the serotonin-mediated behaviour of B. dorsalis.