Cooperative sensing of mitochondrial DNA by ZBP1 and cGAS promotes cardiotoxicity.

Mitochondrial DNA (mtDNA) is a potent agonist of the innate immune system; however, the exact immunostimulatory features of mtDNA and the kinetics of detection by cytosolic nucleic acid sensors remain poorly defined. Here, we show that mitochondrial genome instability promotes Z-form DNA accumulation. Z-DNA binding protein 1 (ZBP1) stabilizes Z-form mtDNA and nucleates a cytosolic complex containing cGAS, RIPK1, and RIPK3 to sustain STAT1 phosphorylation and type I interferon (IFN-I) signaling. Elevated Z-form mtDNA, ZBP1 expression, and IFN-I signaling are observed in cardiomyocytes after exposure to Doxorubicin, a first-line chemotherapeutic agent that induces frequent cardiotoxicity in cancer patients. Strikingly, mice lacking ZBP1 or IFN-I signaling are protected from Doxorubicin-induced cardiotoxicity. Our findings reveal ZBP1 as a cooperative partner for cGAS that sustains IFN-I responses to mitochondrial genome instability and highlight ZBP1 as a potential target in heart failure and other disorders where mtDNA stress contributes to interferon-related pathology.

A subset of megakaryocytes regulates development of hematopoietic stem cell precursors.

Understanding the regulatory mechanisms facilitating hematopoietic stem cell (HSC) specification during embryogenesis is important for the generation of HSCs in vitro. Megakaryocyte emerged from the yolk sac and produce platelets, which are involved in multiple biological processes, such as preventing hemorrhage. However, whether megakaryocytes regulate HSC development in the embryonic aorta-gonad-mesonephros (AGM) region is unclear. Here, we use platelet factor 4 (PF4)-Cre;Rosa-tdTomato+ cells to report presence of megakaryocytes in the HSC developmental niche. Further, we use the PF4-Cre;Rosa-DTA (DTA) depletion model to reveal that megakaryocytes control HSC specification in the mouse embryos. Megakaryocyte deficiency blocks the generation and maturation of pre-HSCs and alters HSC activity at the AGM. Furthermore, megakaryocytes promote endothelial-to-hematopoietic transition in a OP9-DL1 coculture system. Single-cell RNA-sequencing identifies megakaryocytes positive for the cell surface marker CD226 as the subpopulation with highest potential in promoting the hemogenic fate of endothelial cells by secreting TNFSF14. In line, TNFSF14 treatment rescues hematopoietic cell function in megakaryocyte-depleted cocultures. Taken together, megakaryocytes promote production and maturation of pre-HSCs, acting as a critical microenvironmental control factor during embryonic hematopoiesis.

Frizzled 7 modulates goblet and Paneth cell fate, and maintains homeostasis in mouse intestine.

Intestinal homeostasis depends on interactions between the intestinal epithelium, the immune system and the microbiota. Because of these complicated connections, there are many problems that need to be solved. Current research has indicated that genes targeted by Wnt signaling are responsible for controlling intestinal stem cell fate and for modulating intestinal homeostasis. Our data show that loss of frizzled 7 (Fzd7), an important element in Wnt signaling, interrupts the differentiation of mouse intestinal stem cells into absorptive progenitors instead of secretory progenitors (precursors of goblet and Paneth cells). The alteration in canonical Wnt and Notch signaling pathways interrupts epithelial homeostasis, resulting in a decrease in physical protection in the intestine. Several phenotypes in our Fzd7-deleted model were similar to the features of enterocolitis, such as shortened intestines, decreased numbers of goblet cells and Paneth cells, and severe inflammation. Additionally, loss of Fzd7 exacerbated the defects in a chemical-induced colitis model and could initiate tumorigenesis. These findings may provide important information for the discovery of efficient therapeutic methods to treat enterocolitis and related cancers in the intestines.

MS-BACL: enhancing metabolic stability prediction through bond graph augmentation and contrastive learning.

MOTIVATION: Accurately predicting molecular metabolic stability is of great significance to drug research and development, ensuring drug safety and effectiveness. Existing deep learning methods, especially graph neural networks, can reveal the molecular structure of drugs and thus efficiently predict the metabolic stability of molecules. However, most of these methods focus on the message passing between adjacent atoms in the molecular graph, ignoring the relationship between bonds. This makes it difficult for these methods to estimate accurate molecular representations, thereby being limited in molecular metabolic stability prediction tasks. RESULTS: We propose the MS-BACL model based on bond graph augmentation technology and contrastive learning strategy, which can efficiently and reliably predict the metabolic stability of molecules. To our knowledge, this is the first time that bond-to-bond relationships in molecular graph structures have been considered in the task of metabolic stability prediction. We build a bond graph based on 'atom-bond-atom', and the model can simultaneously capture the information of atoms and bonds during the message propagation process. This enhances the model's ability to reveal the internal structure of the molecule, thereby improving the structural representation of the molecule. Furthermore, we perform contrastive learning training based on the molecular graph and its bond graph to learn the final molecular representation. Multiple sets of experimental results on public datasets show that the proposed MS-BACL model outperforms the state-of-the-art model. AVAILABILITY AND IMPLEMENTATION: The code and data are publicly available at https://github.com/taowang11/MS.

DNA-terminus-dependent transcription by T7 RNA polymerase and its C-helix mutants.

The remarkable success of messenger RNA (mRNA)-based vaccines has underscored their potential as a novel biotechnology platform for vaccine development and therapeutic protein delivery. However, the single-subunit RNA polymerase from bacteriophage T7 widely used for in vitro transcription is well known to generate double-stranded RNA (dsRNA) by-products that strongly stimulate the mammalian innate immune response. The dsRNA was reported to be originated from self-templated RNA extension or promoter-independent transcription. Here, we identified that the primary source of the full-length dsRNA during in vitro transcription is the DNA-terminus-initiated transcription by T7 RNA polymerase. Guanosines or cytosines at the end of DNA templates enhance the DNA-terminus-initiated transcription. Moreover, we found that aromatic residues located at position 47 in the C-helix lead to a significant reduction in the production of full-length dsRNA. As a result, the mRNA synthesized using the T7 RNA polymerase G47W mutant exhibits higher expression efficiency and lower immunogenicity compared to the mRNA produced using the wild-type T7 RNA polymerase.

AnimalMetaOmics: a multi-omics data resources for exploring animal microbial genomes and microbiomes.

The Animal Meta-omics landscape database (AnimalMetaOmics, https://yanglab.hzau.edu.cn/animalmetaomics#/) is a comprehensive and freely available resource that includes metagenomic, metatranscriptomic, and metaproteomic data from various non-human animal species and provides abundant information on animal microbiomes, including cluster analysis of microbial cognate genes, functional gene annotations, active microbiota composition, gene expression abundance, and microbial protein identification. In this work, 55 898 microbial genomes were annotated from 581 animal species, including 42 924 bacterial genomes, 12 336 virus genomes, 496 archaea genomes and 142 fungi genomes. Moreover, 321 metatranscriptomic datasets were analyzed from 31 animal species and 326 metaproteomic datasets from four animal species, as well as the pan-genomic dynamics and compositional characteristics of 679 bacterial species and 13 archaea species from animal hosts. Researchers can efficiently access and acquire the information of cross-host microbiota through a user-friendly interface, such as species, genomes, activity levels, expressed protein sequences and functions, and pan-genome composition. These valuable resources provide an important reference for better exploring the classification, functional diversity, biological process diversity and functional genes of animal microbiota.

Glutathione S-transferase-Pi 1 protects cells from irradiation-induced death by inhibiting ferroptosis in pancreatic cancer.

Glutathione S-transferase-Pi 1 (GSTP1) is an isozyme that plays a key role in detoxification and antioxidative damage. It also confers resistance to tumor therapy. However, the specific role of GSTP1 in radiotherapy resistance in pancreatic cancer (PC) is not known. In this study, we investigated how GSTP1 imparts radioresistance in PC. The findings of previous studies and this study revealed that ionizing radiation (IR) induces ferroptosis in pancreatic cancer cells, primarily by upregulating the expression of ACSL4. Our results showed that after IR, GSTP1 prolonged the survival of pancreatic cancer cells by inhibiting ferroptosis but did not affect apoptosis. The expression of GSTP1 reduced cellular ferroptosis by decreasing the levels of ACSL4 and increasing the GSH content. These changes increase the resistance of pancreatic cancer cells and xenograft tumors to IR. Our findings indicate that ferroptosis participates in irradiation-induced cell death and that GSTP1 prevents IR-induced death of pancreatic cancer cells by inhibiting ferroptosis.

ECD-CDGI: An efficient energy-constrained diffusion model for cancer driver gene identification.

The identification of cancer driver genes (CDGs) poses challenges due to the intricate interdependencies among genes and the influence of measurement errors and noise. We propose a novel energy-constrained diffusion (ECD)-based model for identifying CDGs, termed ECD-CDGI. This model is the first to design an ECD-Attention encoder by combining the ECD technique with an attention mechanism. ECD-Attention encoder excels at generating robust gene representations that reveal the complex interdependencies among genes while reducing the impact of data noise. We concatenate topological embedding extracted from gene-gene networks through graph transformers to these gene representations. We conduct extensive experiments across three testing scenarios. Extensive experiments show that the ECD-CDGI model possesses the ability to not only be proficient in identifying known CDGs but also efficiently uncover unknown potential CDGs. Furthermore, compared to the GNN-based approach, the ECD-CDGI model exhibits fewer constraints by existing gene-gene networks, thereby enhancing its capability to identify CDGs. Additionally, ECD-CDGI is open-source and freely available. We have also launched the model as a complimentary online tool specifically crafted to expedite research efforts focused on CDGs identification.

mRNA-CLA: An interpretable deep learning approach for predicting mRNA subcellular localization.

Messenger RNA (mRNA) is vital for post-transcriptional gene regulation, acting as the direct template for protein synthesis. However, the methods available for predicting mRNA subcellular localization need to be improved and enhanced. Notably, few existing algorithms can annotate mRNA sequences with multiple localizations. In this work, we propose the mRNA-CLA, an innovative multi-label subcellular localization prediction framework for mRNA, leveraging a deep learning approach with a multi-head self-attention mechanism. The framework employs a multi-scale convolutional layer to extract sequence features across different regions and uses a self-attention mechanism explicitly designed for each sequence. Paired with Position Weight Matrices (PWMs) derived from the convolutional neural network layers, our model offers interpretability in the analysis. In particular, we perform a base-level analysis of mRNA sequences from diverse subcellular localizations to determine the nucleotide specificity corresponding to each site. Our evaluations demonstrate that the mRNA-CLA model substantially outperforms existing methods and tools.

SGAE-MDA: Exploring the MiRNA-disease associations in herbal medicines based on semi-supervised graph autoencoder.

Research indicates that miRNAs present in herbal medicines are crucial for identifying disease markers, advancing gene therapy, facilitating drug delivery, and so on. These miRNAs maintain stability in the extracellular environment, making them viable tools for disease diagnosis. They can withstand the digestive processes in the gastrointestinal tract, positioning them as potential carriers for specific oral drug delivery. By engineering plants to generate effective, non-toxic miRNA interference sequences, it's possible to broaden their applicability, including the treatment of diseases such as hepatitis C. Consequently, delving into the miRNA-disease associations (MDAs) within herbal medicines holds immense promise for diagnosing and addressing miRNA-related diseases. In our research, we propose the SGAE-MDA model, which harnesses the strengths of a graph autoencoder (GAE) combined with a semi-supervised approach to uncover potential MDAs in herbal medicines more effectively. Leveraging the GAE framework, the SGAE-MDA model exactly integrates the inherent feature vectors of miRNAs and disease nodes with the regulatory data in the miRNA-disease network. Additionally, the proposed semi-supervised learning approach randomly hides the partial structure of the miRNA-disease network, subsequently reconstructing them within the GAE framework. This technique effectively minimizes network noise interference. Through comparison against other leading deep learning models, the results consistently highlighted the superior performance of the proposed SGAE-MDA model. Our code and dataset can be available at: https://github.com/22n9n23/SGAE-MDA.

Systematic single-cell analysis reveals dynamic control of transposable element activity orchestrating the endothelial-to-hematopoietic transition.

BACKGROUND: The endothelial-to-hematopoietic transition (EHT) process during definitive hematopoiesis is highly conserved in vertebrates. Stage-specific expression of transposable elements (TEs) has been detected during zebrafish EHT and may promote hematopoietic stem cell (HSC) formation by activating inflammatory signaling. However, little is known about how TEs contribute to the EHT process in human and mouse. RESULTS: We reconstructed the single-cell EHT trajectories of human and mouse and resolved the dynamic expression patterns of TEs during EHT. Most TEs presented a transient co-upregulation pattern along the conserved EHT trajectories, coinciding with the temporal relaxation of epigenetic silencing systems. TE products can be sensed by multiple pattern recognition receptors, triggering inflammatory signaling to facilitate HSC emergence. Interestingly, we observed that hypoxia-related signals were enriched in cells with higher TE expression. Furthermore, we constructed the hematopoietic cis-regulatory network of accessible TEs and identified potential TE-derived enhancers that may boost the expression of specific EHT marker genes. CONCLUSIONS: Our study provides a systematic vision of how TEs are dynamically controlled to promote the hematopoietic fate decisions through transcriptional and cis-regulatory networks, and pre-train the immunity of nascent HSCs.

Risk modeling of single-cell transcriptomes reveals the heterogeneity of immune infiltration in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common primary hepatic malignancies. E2F transcription factors play an important role in the tumorigenesis and progression of HCC, mainly through the RB/E2F pathway. Prognostic models for HCC based on gene signatures have been developed rapidly in recent years; however, their discriminating ability at the single-cell level remains elusive, which could reflect the underlying mechanisms driving the sample bifurcation. In this study, we constructed and validated a predictive model based on E2F expression, successfully stratifying patients with HCC into two groups with different survival risks. Then we used a single-cell dataset to test the discriminating ability of the predictive model on infiltrating T cells, demonstrating remarkable cellular heterogeneity as well as altered cell fates. We identified distinct cell subpopulations with diverse molecular characteristics. We also found that the distribution of cell subpopulations varied considerably across onset stages among patients, providing a fundamental basis for patient-oriented precision evaluation. Moreover, single-sample gene set enrichment analysis revealed that subsets of CD8+ T cells with significantly different cell adhesion levels could be associated with different patterns of tumor cell dissemination. Therefore, our findings linked the conventional prognostic gene signature to the immune microenvironment and cellular heterogeneity at the single-cell level, thus providing deeper insights into the understanding of HCC tumorigenesis.

The transcription factor MYC1 interacts with FIT to negatively regulate iron homeostasis in Arabidopsis thaliana.

Iron (Fe) is an indispensable trace mineral element for the normal growth of plants, and it is involved in different biological processes; Fe shortage in plants can induce chlorosis and yield loss. The objective of this research is to identify novel genes that participated in the regulation of Fe-deficiency stress in Arabidopsis thaliana. A basic helix-loop-helix (bHLH) transcription factor (MYC1) was identified to be interacting with the FER-LIKE IRON DEFICIENCY-INDUCED TRANSCRIPTION FACTOR (FIT) using a yeast-two-hybrid assay. Transcript-level analysis showed that there was a decrease in MYC1 expression in Arabidopsis to cope with Fe-deficiency stress. Functional deficiency of MYC1 in Arabidopsis leads to an increase in Fe-deficiency tolerance and Fe-accumulation, whereas MYC1-overexpressing plants have an enhanced sensitivity to Fe-deficiency stress. Additionally, MYC1 inhibited the formation of FIT and bHLH38/39 heterodimers, which suppressed the expressed level for Fe acquisition genes FRO2 and IRT1 during Fe-deficiency stress. These results showed that MYC1 functions as a negative modulator of the Fe-deficiency stress response by inhibiting the formation of FIT and bHLH38/39 heterodimers, thereby suppressing the binding of FIT and bHLH38/39 heterodimers to the promoters of FRO2 and IRT1 to modulate Fe intake during Fe-deficiency stress. Overall, the findings of this study elucidated the role of MYC1 in coping with Fe-deficiency stress, and provided potential targets for the developing of crop varieties resistant to Fe-deficiency stress.

New insights into the function of plant tannase with promiscuous acyltransferase activity.

Plant tannases (TAs) or tannin acyl hydrolases, a class of recently reported carboxylesterases in tannin-rich plants, are involved in the degalloylation of two important groups of secondary metabolites: flavan-3-ol gallates and hydrolyzable tannins. In this paper, we have made new progress in studying the function of tea (Camellia sinensis) (Cs) TA-it is a hydrolase with promiscuous acyltransferase activity in vitro and in vivo and promotes the synthesis of simple galloyl glucoses and flavan-3-ol gallates in plants. We studied the functions of CsTA through enzyme analysis, protein mass spectrometry, and metabolic analysis of genetically modified plants. Firstly, CsTA was found to be not only a hydrolase but also an acyltransferase. In the two-step catalytic reaction where CsTA hydrolyzes the galloylated compounds epigallocatechin-3-gallate or 1,2,3,4,6-penta-O-galloyl-ß-d-glucose into their degalloylated forms, a long-lived covalently bound Ser159-linked galloyl-enzyme intermediate is also formed. Under nucleophilic attack, the galloyl group on the intermediate is transferred to the nucleophilic acyl acceptor (such as water, methanol, flavan-3-ols, and simple galloyl glucoses). Then, metabolic analysis suggested that transient overexpression of TAs in young strawberry (Fragaria × ananassa) fruits, young leaves of tea plants, and young leaves of Chinese bayberry (Myrica rubra) actually increased the total contents of simple galloyl glucoses and flavan-3-ol gallates. Overall, these findings provide new insights into the promiscuous acyltransferase activity of plant TA.

Supporting youth mental health with arts-based strategies: a global perspective.

The devastating impact of youth mental health concerns is increasingly evident on a global scale. This crisis calls for innovative solutions that are sufficiently accessible, scalable, and cost-effective to support diverse communities around the world. One such solution involves engagement in the arts: incorporating and building upon existing local resources and cultural practices to bolster youth mental health. In this article, we describe the global youth mental health crisis and note major gaps in the knowledge and resources needed to address it. We then discuss the potential for arts- and culture-based strategies to help meet this challenge, review the mounting evidence regarding art's ability to support mental health, and call for action to undertake critical research and its translation into accessible community practices. Four steps are suggested: (1) elevate and prioritize youth voice, (2) develop core outcome measures, (3) identify and analyze successful models around the globe, and (4) generate clear funding pathways for research and translational efforts. Worldwide implementation of arts- and culture-based strategies to address youth mental health will provide critical resources to support the health, wellbeing and flourishing of countless youth across the globe.

Tailored Yolk-Shell Design to Silicon Microparticles via Scalable and Template-Free Synthesis for Superior Lithium Storage.

Micrometer-sized Si particles are beneficial to practical lithium-ion batteries in regard to low cost and high volumetric energy density in comparison with nanostructured Si anodes. However, both the issues of electrical contact loss and overgrowth of solid electrolyte interface for microscale Si induced by colossal volume change still remain to be addressed. Herein, a scalable and template-free method is introduced to fabricate yolk-shell structured Si anode from commercially available Si microparticles. The void is created via a one-step alkali etching process with the remaining silicon core as the yolk, and a double-walled shell is formed from simultaneous in situ growth of the conformal native oxide layer and subsequent carbon coating. In this configuration, the well-defined void spaces allow the Si core to expand without compromising structural integrity, while the double-walled shell acts as a static capsule to confine silicon fragments despite likely particle fracture. Therefore, electrical connectivity is maintained on both the particle and electrode level during deep galvanostatic cycling, and the solid-electrolyte interface is stabilized on the shell surface. Owing to the benefits of tailored design, excellent cycling stability (capacity retention of 95% after 100 cycles) and high coulombic efficiency (99.5%) are realized in a practical full-cell demonstration.

A High-Entropy Prussian Blue Analog for Aqueous Potassium-Ion Batteries.

Aqueous potassium-ion batteries (AKIBs) are considered promising electrochemical energy storage systems owing to their high safety and cost-effectiveness. However, the structural degradation resulting from the repeated accommodation of large K-ions and the dissolution of active electrode materials in highly dielectric aqueous electrolytes often lead to unsatisfactory electrochemical performance. This study introduces a high-entropy Prussian blue analog (HEPBA) cathode material for AKIBs, demonstrating significantly enhanced structural stability and reduced dissolution. The HEPBA exhibits a highly reversible specific capacity of 102.4 mAh g-1, with 84.4% capacity retention after undergoing 3448 cycles over a duration of 270 days. Mechanistic insights derived from comprehensive experimental investigations, supported by theoretical calculations, reveal that the HEPBA features a robust structure resistant to dissolution, a solid-solution reaction pathway with negligible volume variation during charge-discharge, and efficient ion transport kinetics characterized by a reduced band gap and a low energy barrier. This study represents a measurable step forward in the development of long-lasting electrode materials for aqueous AKIBs.

Guard Cell-Inspired Ion Channels: Harnessing the Photomechanical Effect via Supramolecular Assembly of Cross-Linked Azobenzene/Polymers.

Stimuli-responsive ion nanochannels have attracted considerable attention in various fields because of their remote controllability of ionic transportation. For photoresponsive ion nanochannels, however, achieving precise regulation of ion conductivity is still challenging, primarily due to the difficulty of programmable structural changes in confined environments. Moreover, the relationship between noncontact photo-stimulation in nanoscale and light-induced ion conductivity has not been well understood. In this work, a versatile design for fabricating guard cell-inspired photoswitchable ion channels is presented by infiltrating azobenzene-cross-linked polymer (AAZO-PDAC) into nanoporous anodic aluminum oxide (AAO) membranes. The azobenzene-cross-linked polymer is formed by azobenzene chromophore (AAZO)-cross-linked poly(diallyldimethylammonium chloride) (PDAC) with electrostatic interactions. Under UV irradiation, the trans-AAZO isomerizes to the cis-AAZO, causing the volume compression of the polymer network, whereas, in darkness, the cis-AAZO reverts to the trans-AAZO, leading to the recovery of the structure. Consequently, the resultant nanopore sizes can be manipulated by the photomechanical effect of the AAZO-PDAC polymers. By adding ionic liquids, the ion conductivity of the light-driven ion nanochannels can be controlled with good repeatability and fast responses (within seconds) in multiple cycles. The ion channels have promising potential in the applications of biomimetic materials, sensors, and biomedical sciences.

Mass spectrometric approaches in discovering lipid biomarkers for COVID-19 by lipidomics: Future challenges and perspectives.

Coronavirus disease 2019 (COVID-19) has emerged as a global health threat and has rapidly spread worldwide. Significant changes in the lipid profile before and after COVID-19 confirmed the significance of lipid metabolism in regulating the response to viral infection. Therefore, understanding the role of lipid metabolism may facilitate the development of new therapeutics for COVID-19. Owing to their high sensitivity and accuracy, mass spectrometry (MS)-based methods are widely used for rapidly identifying and quantifying of thousands of lipid species present in a small amount of sample. To enhance the capabilities of MS for the qualitative and quantitative analysis of lipids, different platforms have been combined to cover a wide range of lipidomes with high sensitivity, specificity, and accuracy. Currently, MS-based technologies are being established as efficient methods for discovering potential diagnostic biomarkers for COVID-19 and related diseases. As the lipidome of the host cell is drastically affected by the viral replication process, investigating lipid profile alterations in patients with COVID-19 and targeting lipid metabolism pathways are considered to be crucial steps in host-directed drug targeting to develop better therapeutic strategies. This review summarizes various MS-based strategies that have been developed for lipidomic analyzes and biomarker discoveries to combat COVID-19 by integrating various other potential approaches using different human samples. Furthermore, this review discusses the challenges in using MS technologies and future perspectives in terms of drug discovery and diagnosis of COVID-19.

Deep learning and targeted metabolomics-based monitoring of chewing insects in tea plants and screening defense compounds.

Tea is an important cash crop that is often consumed by chewing pests, resulting in reduced yields and economic losses. It is important to establish a method to quickly identify the degree of damage to tea plants caused by leaf-eating insects and screen green control compounds. This study was performed through the combination of deep learning and targeted metabolomics, in vitro feeding experiment, enzymic analysis and transient genetic transformation. A small target damage detection model based on YOLOv5 with Transformer Prediction Head (TPH-YOLOv5) algorithm for the tea canopy level was established. Orthogonal partial least squares (OPLS) was used to analyze the correlation between the degree of damage and the phenolic metabolites. A potential defensive compound, (-)-epicatechin-3-O-caffeoate (EC-CA), was screened. In vitro feeding experiments showed that compared with EC and epicatechin gallate, Ectropis grisescens exhibited more significant antifeeding against EC-CA. In vitro enzymatic experiments showed that the hydroxycinnamoyl transferase (CsHCTs) recombinant protein has substrate promiscuity and can catalyze the synthesis of EC-CA. Transient overexpression of CsHCTs in tea leaves effectively reduced the degree of damage to tea leaves. This study provides important reference values and application prospects for the effective monitoring of pests in tea gardens and screening of green chemical control substances.