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ObjectiveTo comprehensively assess the global burden of syphilis and related risk factors over 1990–2021, forecast future disease trends, and understand the impact of syphilis on global health.MethodsGlobal Burden of Disease Study 2021 (GBD 2021) data were used for age-, sex-, and region-stratified analysis of the numbers and age-standardized rates (per 100,000 population) of syphilis incidence, prevalence, deaths, and disability-adjusted life years (DALYs). Next, a differential analysis of syphilis risk factors was performed. Finally, trends for years after 2021 were predicted using Bayesian age–period–cohort (BAPC) prediction models.ResultsIn 2021, the total number of syphilis prevalence globally was 70,541,482.80 (95% uncertainty interval: 54,910,897.66–88,207,651.97), with the highest numbers noted in Central Sub-Saharan Africa [4,622.60 (95% uncertainty interval: 3,591.97–5,753.45)]. Over 1990–2021, the global age-standardized prevalence and incidence rates increased, whereas the age-standardized death and DALY rates decreased. Among all groups, infants aged

About 47% of pathogenic point mutations could be corrected by ABE-induced A·T-to-G·C conversions. However, the applications of ABEs are still hindered by undesired editing efficiency, limited editing scopes, and off-targeting effects. Here, we develop a new adenine base editor, by embedding TadA-8e monomer into SpRY-nCas9, named as CE-8e-SpRY, which exhibits higher activity at NRN than NYN PAMs favored by SpRY nuclease. CE-8e-SpRY could target nearly all genomic sites in principle and induces the highest targeting efficiency among tested SpRY-based ABEs. In addition, CE-8e-SpRY also shows reduced RNA and DNA off-targeting activities. With optimized sgRNAs, CE-8e-SpRY induces efficient or desired target editing at some disease-relevant loci where conventional ABEs were unable to induce precise and satisfied editing. Taken together, our CE-8e-SpRY could broaden the applicability of ABEs in correcting or introducing pathogenic point mutations.

Abstract Peroxisome proliferator-activated receptor (PPAR)-γ is a key transcription activator controlling adipogenesis and lipid metabolism. PPARγ binds PPAR response elements (PPREs) as the obligate heterodimer with retinoid X receptor (RXR) α, but exactly how PPARγ orchestrates the transcriptional response is unknown. This study demonstrates that PPARγ forms phase-separated droplets in vitro and solid-like nuclear condensates in cell, which is intriguingly mediated by its DNA binding domain characterized by the zinc finger motif. Furthermore, PPARγ forms nuclear condensates at PPREs sites through phase separation to compartmentalize its heterodimer partner RXRα to initiate PPARγ-specific transcriptional activation. Finally, using an optogenetic approach, the enforced formation of PPARγ/RXRα condensates leads to preferential enrichment at PPREs sites and significantly promotes the expression of PPARγ target genes. These results define a novel mechanism by which PPARγ engages the phase separation principles for efficient and specific transcriptional activation.

Prime editing enables efficient introduction of targeted transversions, insertions, and deletions in mammalian cells and several organisms. However, genetic disease models with base deletions by prime editing have not yet been reported in mice. Here, we successfully generate a mouse model with a cataract disorder through microinjection of prime editor 3 (PE3) plasmids to efficiently induce targeted single-base deletion. Notably, a generated mouse with a high G-deletion rate (38.2%) displays a nuclear cataract phenotype; the PE3-induced deletions in mutant mice achieve high rates of germline transmission to their progenies, with phenotypic inheritance of cataract. Our data propose that modeling a genetic disease with a single nucleotide deletion in mice can be achieved with prime genome editing in vivo.

Abstract Selective inhibition of targeted protein kinases is an effective therapeutic approach for treatment of human malignancies, which interferes phosphorylation of cellular substrates. However, a drug‐imposed selection creates pressures for tumor cells to acquire chemoresistance‐conferring mutations or activating alternative pathways, which can bypass the inhibitory effects of kinase inhibitors. Thus, identifying downstream phospho‐substrates conferring drug resistance is of great importance for developing poly‐pharmacological and targeted therapies. To identify functional phosphorylation sites involved in 5‐fluorouracil (5‐FU) resistance during its treatment of colorectal cancer cells, CRISPR‐mediated cytosine base editor (CBE) and adenine base editor (ABE) are utilized for functional screens by mutating phosphorylated amino acids with two libraries specifically targeting 7779 and 10 149 phosphorylation sites. Among the top enriched gRNAs‐induced gain‐of‐function mutants, the target genes are involved in cell cycle and post‐translational covalent modifications. Moreover, several substrates of RSK2 and PAK4 kinases are discovered as main effectors in responding to 5‐FU chemotherapy, and combinational treatment of colorectal cancer cells with 5‐FU and RSK2 inhibitor or PAK4 inhibitor can largely inhibit cell growth and enhance cell apoptosis through a RSK2/TP53BP1/γ‐H2AX phosphorylation signaling axis. It is proposed that this screen approach can be used for functional phosphoproteomics in chemotherapy of various human diseases.

Base editors can induce transcriptome-wide RNA off-target editing independent of gRNA. Here, the authors engineer ABEmax variants with minimized RNA off-target activities.

Abstract Background Site-specific C>T DNA base editing has been achieved by recruiting cytidine deaminases to the target C using catalytically impaired Cas proteins; the target C is typically located within 5-nt editing window specified by the guide RNAs. The prototypical cytidine base editor BE3, comprising rat APOBEC1 (rA1) fused to nCas9, can indiscriminately deaminate multiple C’s within the editing window and also create substantial off-target edits on the transcriptome. A powerful countermeasure for the DNA off-target editing is to replace rA1 with APOBEC proteins which selectively edit C’s in the context of specific motifs, as illustrated in eA3A-BE3 which targets TC. However, analogous editors selective for other motifs have not been described. In particular, it has been challenging to target a particular C in C-rich sequences. Here, we sought to confront this challenge and also to overcome the RNA off-target effects seen in BE3. Results By replacing rA1 with an optimized human A3G (oA3G), we developed oA3G-BE3, which selectively targets CC and CCC and is also free of global off-target effects on the transcriptome. Furthermore, we created oA3G-BE4max, an upgraded version of oA3G-BE3 with robust on-target editing. Finally, we showed that oA3G-BE4max has negligible Cas9-independent off-target effects at the genome. Conclusions oA3G-BE4max can edit C(C)C with high efficiency and selectivity, which complements eA3A-editors to broaden the collective editing scope of motif selective editors, thus filling a void in the base editing tool box.

San-Huang-Yi-Shen capsule (SHYS) has been used in the treatment of diabetic nephropathy (DN) in clinic. However, the mechanisms of SHYS on DN remain unknown. In this study, we used a high-fat diet (HFD) combined with streptozotocin (STZ) injection to establish a DN rat model. Next, we used 16S rRNA sequencing and untargeted metabolomics to study the potential mechanisms of SHYS on DN. Our results showed that SHYS treatment alleviated the body weight loss, hyperglycemia, proteinuria, pathological changes in kidney in DN rats. SHYS could also inhibite the oxidative stress and inflammatory response in kidney. 16S rRNA sequencing analysis showed that SHYS affected the beta diversity of gut microbiota community in DN model rats. SHYX could also decrease the Firmicutes to Bacteroidetes (F to B) ratio in phylum level. In genus level, SHYX treatment affected the relative abundances of Lactobacillus, Ruminococcaceae UCG-005, Allobaculum, Anaerovibrio, Bacteroides and Candidatus_Saccharimonas. Untargeted metabolomics analysis showed that SHYX treatment altered the serum metabolic profile in DN model rats through affecting the levels of guanidineacetic acid, L-kynurenine, prostaglandin F1α, threonine, creatine, acetylcholine and other 21 kind of metabolites. These metabolites are mainly involved in glycerophospholipid metabolism, tryptophan metabolism, alanine, aspartate and glutamate metabolism, arginine biosynthesis, tricarboxylic acid (TCA) cycle, tyrosine metabolism, arginine and proline metabolism, arginine and proline metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, and D-glutamine and D-glutamate metabolism pathways. Spearman correlation analysis showed that Lactobacillus, Candidatus_Saccharimonas, Ruminococcaceae UCG-005, Anaerovibrio, Bacteroides, and Christensenellaceae_R-7_group were closely correlated with most of physiological data and the differential metabolites following SHYS treatment. In conclusion, our study revealed multiple ameliorative effects of SHYS on DN including the alleviation of hyperglycemia and the improvement of renal function, pathological changes in kidney, oxidative stress, and the inflammatory response. The mechanism of SHYS on DN may be related to the improvement of gut microbiota which regulates arginine biosynthesis, TCA cycle, tyrosine metabolism, and arginine and proline metabolism.

Summary: The advent of base editors (BEs) holds great potential for correcting pathogenic-related point mutations to treat relevant diseases. However, Cas9 nickase (nCas9)-derived BEs lead to DNA double-strand breaks, which can trigger unwanted DNA damage response (DDR). Here, we show that the original version of catalytically dead Cas12a (dCas12a)-conjugated BEs induce a basal level of DNA breaks and minimally activate DDR proteins, including H2AX, ATM, ATR, and p53. By fusing dCas12a with engineered human apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3A (APOBEC3A), we further develop the BEACON (base editing induced by human APOBEC3A and Cas12a without DNA break) system to achieve enhanced deamination efficiency and editing specificity. Efficient C-to-T editing is achieved by BEACON in mammalian cells at levels comparable to AncBE4max, with only low levels of DDR and minimal RNA off-target mutations. Importantly, BEACON induces in vivo base editing in mouse embryos, and targeted C-to-T conversions are detected in F0 mice.

CRISPR-based base editors allow for single nucleotide genome editing in a range of organisms. Here the authors demonstrate the in vivo generation of mouse models carrying clinically relevant mutations using C→T and A→G editors.

Dendrobium officinale is a widely used medicinal plant in China with numerous bio-activities. However, the main structure and anti-tumor activity of the polysaccharides from this plant have not been investigated. In this study, we elucidated the main structure of polysaccharides purified with DEAE and Sephadex G-25 from Dendrobium officinale grown under different planting conditions. In addition, the anti-tumor activity was tested via MTT assays. The results showed that the polysaccharides of Dendrobium officinale grown under different conditions were almost the same, with slight differences in the branched chain; both polysaccharide fractions consisted of (1→4)-linked mannose and (1→4)-linked glucose, with an O-acetyl group in the mannose. After degradation, the polysaccharide fractions from wild plants showed significant anti-proliferation activity in HeLa cells. The fractions F1 and F3 induced apoptosis by up-regulating the expression of ERK, JNK, and p38. We concluded that polysaccharides from Dendrobium officinale planted in the wild exhibit significant anti-tumor effects only after being degraded to smaller molecular weight species. The planting mode is a significant factor in the pharmacological activity of Dendrobium officinale. We advise that the planting conditions for Dendrobium officinale should be changed.

Anaerobic processing is a crucial factor influencing the formation of flavor quality in Gamma-aminobutyric acid (GABA) tea. In this study, headspace solid-phase microextraction combined with gas chromatography and mass spectrometry was employed to explore the flavor characteristics of different types of GABA tea. We utilized multivariate analyses to identify at least 146 volatile components (VOCs) across 12 functional groups in the GABA tea samples via principal component analysis (PCA). At least 40 differential VOCs were screened from the GABA tea samples via orthogonal partial least squares-discriminant analysis. Subsequently, a minimum of four VOCs were identified in the GABA tea samples via the Pearson correlation coefficient combined with relative odor activity values as potential markers for flowery and fruity aromas, clarifying the impact of the VOCs on these characteristics. The results of this study provide a theoretical basis for understanding the formation of flowery and fruity flavor characteristics in GABA tea.

Abstract To address the issues of low accuracy and slow response speed in tea disease classification and identification, an improved YOLOv7 lightweight model was proposed in this study. The lightweight MobileNeXt was used as the backbone network to reduce computational load and enhance efficiency. Additionally, a dual-layer routing attention mechanism was introduced to enhance the model’s ability to capture crucial details and textures in disease images, thereby improving accuracy. The SIoU loss function was employed to mitigate missed and erroneous judgments, resulting in improved recognition amidst complex image backgrounds.The revised model achieved precision, recall, and average precision of 93.5%, 89.9%, and 92.1%, respectively, representing increases of 4.5%, 1.9%, and 2.6% over the original model. Furthermore, the model’s volum was reduced by 24.69M, the total param was reduced by 12.88M, while detection speed was increased by 24.41 frames per second. This enhanced model efficiently and accurately identifies tea disease types, offering the benefits of lower parameter count and faster detection, thereby establishing a robust foundation for tea disease monitoring and prevention efforts.

Aiming at the problems of insufficient detection accuracy and high false detection rates of traditional pest detection models in the face of small targets and incomplete targets, this study proposes an improved target detection network, I-YOLOv10-SC. The network leverages Space-to-Depth Convolution to enhance its capability in detecting small insect targets. The Convolutional Block Attention Module is employed to improve feature representation and attention focus. Additionally, Shape Weights and Scale Adjustment Factors are introduced to optimize the loss function. The experimental results show that compared with the original YOLOv10, the model generated by the improved algorithm improves the accuracy by 5.88 percentage points, the recall rate by 6.67 percentage points, the balance score by 6.27 percentage points, the mAP value by 4.26 percentage points, the bounding box loss by 18.75%, the classification loss by 27.27%, and the feature point loss by 8%. The model oscillation has also been significantly improved. The enhanced I-YOLOv10-SC network effectively addresses the challenges of detecting small and incomplete insect targets in tea plantations, offering high precision and recall rates, thus providing a solid technical foundation for intelligent pest monitoring and precise prevention in smart tea gardens.

This study focuses on the renewal and iteration mechanisms of aged tea trees in interactions with their soil microbial communities, aiming to elucidate the impact of the planting age of tea trees on the structure and function of soil microbial communities and how these impacts are linked to the formation of tea quality. By conducting a comparative analysis of the cultivation soil from tea trees with varying planting ages ranging from 30 to 200 years, we employed microbial diversity sequencing, a soil physicochemical property analysis, and tea leaf chemical component detection. We combined these methods with redundancy analysis (RDA) and linear discriminant analysis effect size (LEfSe) to reveal significant correlations between the planting age of tea trees and the soil’s microbial diversity and function. The results indicate that as the planting age of tea trees increases, there are significant changes in the soil’s pH and nutrient content. Concurrently, the components of the tea leaves also change. Most notably, around the 120 years mark of the tea tree planting age, the diversity of the soil microbial community reaches a turning point. Key microbial community analyses revealed shifts in the dominant microbial populations within the soil across the various tea tree planting ages, exemplified by taxa such as Hygrocybe Mycena, Humicola, Bradyrhizobium, and Candidatus Solibacter. These alterations in microbial communities are closely associated with soil nutrient dynamics and the developmental stages of tea trees. These findings not only provide scientific guidance for tea garden management, tea tree cultivation, and tea production but also offer new insights into the impact of tea tree–soil–microbe interactions on tea quality, which is significantly important for enhancing tea quality.

Catechins are pivotal determinants of tea quality, with soil environmental factors playing a crucial role in the synthesis and accumulation of these compounds. To investigate the impact of changes in tea garden soil environments on the catechin content in sun-dried tea, this study measured the catechin content in soil samples and corresponding tea leaves from Nanhua, Yunnan, China. By integrating the variations in catechin content with those of 17 soil factors and employing COX regression factor analysis, it was found that pH, organic matter (OM), fluoride, arsenic (As), and chromium (Cr) were significantly correlated with catechin content (p < 0.05). Further, using the LASSO regression for variable selection, a model named LCLN-CA was constructed with four variables including pH, OM, fluoride, and As. The LCLN-CA model demonstrated high fitting accuracy with AUC values of 0.674, 0.784, and 0.749 for catechin content intervals of CA ≤ 10%, 10% < CA ≤ 20%, and 20% < CA ≤ 30% in the training set, respectively. The validation set showed AUC values of 0.630, 0.756, and 0.723, respectively, indicating a well-calibrated curve. Based on the LCLN-CA model and the DynNom framework, a visual prediction system for catechin content in Yunnan sun-dried tea was developed. External validation with a test dataset achieved an Accuracy of 0.870. This study explored the relationship between soil-related factors and variations in catechin content, paving a new way for the prediction of catechin content in tea and enhancing the practical application value of artificial intelligence technology in agricultural production.

Understanding the correlation between soil chemical properties and tea quality is essential for the comprehensive management of ancient tea gardens. However, the specific links between these factors in ancient tea gardens remain underexplored. This study analyzes the soil chemical properties of four distinct research regions in Nanhua County to explore their effects on key chemical components in ancient tea garden teas, providing a scientific basis for improving the quality of ancient tea garden teas through soil management. Employing high performance liquid chromatography (HPLC) and inductively coupled plasma mass spectrometry (ICP-MS), the chemical components of tea and the chemical properties of the soil were meticulously quantified. Following these measurements, the integrated fertility index (IFI) and the potential ecological risk index (PERI) were evaluated and correlation analysis was conducted. The results revealed that ancient tea garden tea quality is closely linked to soil chemical properties. Soil’s total nitrogen (TN), total sulfur (TS), and available potassium (AK) negatively correlate with tea’s catechin gallate (CG) component and AK also with polyphenols. Most other soil properties show positive correlations with tea components. The research also evaluated soil heavy metals’ IFI and PERI. IFI varied significantly among regions. Hg’s high pollution index indicates ecological risks; Cd in Xiaochun (XC) region poses a moderate risk. PERI suggests moderate risk for XC and Banpo (BP), with other areas classified as low risk. Implementing reasonable fertilization and soil amelioration measures to enhance soil fertility and ensure adequate supply of key nutrients will improve the quality of ancient tea gardens. At the same time, soil management measures should effectively control heavy metal pollution to ensure the quality and safety of tea products. Insights from this study are crucial for optimizing soil management in ancient tea gardens, potentially improving tea quality and sustainability.

To explore the effects of different withering methods on the quality of Congou black tea, this study focused on five different withering methods: natural withering, warm-air withering, sun–natural combined withering, sun withering, and shaking withering. Gas chromatography‒mass spectrometry (GC‒MS), high-performance liquid chromatography (HPLC), and ion-exchange chromatography techniques were used to analyze the nonvolatile and volatile components and composition of the tea. The results revealed significant differences (p < 0.05) in the contents of nonvolatile constituents including caffeine, polyphenols, soluble sugars, free amino acids and their components, theaflavins, thearubigins, and catechins among the five different withering methods, with varying degrees of correlation between these components. A total of 227 aroma compounds were detected, and significant differences in the contents of alcohols, aldehydes, and ketones were observed. A relative odor activity value (ROVA) analysis of the aroma compounds revealed that 19 compounds had an ROVA > 1. Among them, benzylaldehyde, trans-2-decenal, decanal, benzaldehyde, nonanal, hexanal, trans-linalool, and geraniol from the shaking withering method had significantly higher ROVA values than those from the other withering methods, which may be the reason for the prominent floral and fruity aroma of shaking withering. This study revealed the impact of different withering methods on the quality of Congou black tea, providing a scientific basis for the development of Congou black tea with different flavors and the improvement of Congou black tea processing techniques.

China’s Yunnan Province, known for its tea plantations, faces significant challenges in smart pest management due to its ecologically intricate environment. To enable the intelligent monitoring of pests within tea plantations, this study introduces a novel image recognition algorithm, designated as YOLOv8n-WSE-pest. Taking into account the pest image data collected from organic tea gardens in Yunnan, this study utilizes the YOLOv8n network as a foundation and optimizes the original loss function using WIoU-v3 to achieve dynamic gradient allocation and improve the prediction accuracy. The addition of the Spatial and Channel Reconstruction Convolution structure in the Backbone layer reduces redundant spatial and channel features, thereby reducing the model’s complexity. The integration of the Efficient Multi-Scale Attention Module with Cross-Spatial Learning enables the model to have more flexible global attention. The research results demonstrate that compared to the original YOLOv8n model, the improved YOLOv8n-WSE-pest model shows increases in the precision, recall, mAP50, and F1 score by 3.12%, 5.65%, 2.18%, and 4.43%, respectively. In external validation, the mAP of the model outperforms other deep learning networks such as Faster-RCNN, SSD, and the original YOLOv8n, with improvements of 14.34%, 8.85%, and 2.18%, respectively. In summary, the intelligent tea garden pest identification model proposed in this study excels at precise the detection of key pests in tea plantations, enhancing the efficiency and accuracy of pest management through the application of advanced techniques in applied science.