PIEZO1 mechanically regulates the antitumour cytotoxicity of T lymphocytes.

The killing function of cytotoxic T cells can be enhanced biochemically. Here we show that blocking the mechanical sensor PIEZO1 in T cells strengthens their traction forces and augments their cytotoxicity against tumour cells. By leveraging cytotoxic T cells collected from tumour models in mice and from patients with cancers, we show that PIEZO1 upregulates the transcriptional factor GRHL3, which in turn induces the expression of the E3 ubiquitin ligase RNF114. RNF114 binds to filamentous actin, causing its downregulation and rearrangement, which depresses traction forces in the T cells. In mice with tumours, the injection of cytotoxic T cells collected from the animals and treated with a PIEZO1 antagonist promoted their infiltration into the tumour and attenuated tumour growth. As an immunomechanical regulator, PIEZO1 could be targeted to enhance the outcomes of cancer immunotherapies.

D-Limonene Affects the Feeding Behavior and the Acquisition and Transmission of Tomato Yellow Leaf Curl Virus by Bemisia tabaci.

Bemisia tabaci (Gennadius) is an important invasive pest transmitting plant viruses that are maintained through a plant-insect-plant cycle. Tomato yellow leaf curl virus (TYLCV) can be transmitted in a persistent manner by B. tabaci, which causes great losses to global agricultural production. From an environmentally friendly, sustainable, and efficient point of view, in this study, we explored the function of d-limonene in reducing the acquisition and transmission of TYLCV by B. tabaci as a repellent volatile. D-limonene increased the duration of non-feeding waves and reduced the duration of phloem feeding in non-viruliferous and viruliferous whiteflies by the Electrical Penetration Graph technique (EPG). Additionally, after treatment with d-limonene, the acquisition and transmission rate of TYLCV was reduced. Furthermore, BtabOBP3 was determined as the molecular target for recognizing d-limonene by real-time quantitative PCR (RT-qPCR), fluorescence competitive binding assays, and molecular docking. These results confirmed that d-limonene is an important functional volatile which showed a potential contribution against viral infections with potential implications for developing effective TYLCV control strategies.

Innovative insights into extrachromosomal circular DNAs in gynecologic tumors and reproduction.

Originating but free from chromosomal DNA, extrachromosomal circular DNAs (eccDNAs) are organized in circular form and have long been found in unicellular and multicellular eukaryotes. Their biogenesis and function are poorly understood as they are characterized by sequence homology with linear DNA, for which few detection methods are available. Recent advances in high-throughput sequencing technologies have revealed that eccDNAs play crucial roles in tumor formation, evolution, and drug resistance as well as aging, genomic diversity, and other biological processes, bringing it back to the research hotspot. Several mechanisms of eccDNA formation have been proposed, including the breakage-fusion-bridge (BFB) and translocation-deletion-amplification models. Gynecologic tumors and disorders of embryonic and fetal development are major threats to human reproductive health. The roles of eccDNAs in these pathological processes have been partially elucidated since the first discovery of eccDNA in pig sperm and the double minutes in ovarian cancer ascites. The present review summarized the research history, biogenesis, and currently available detection and analytical methods for eccDNAs and clarified their functions in gynecologic tumors and reproduction. We also proposed the application of eccDNAs as drug targets and liquid biopsy markers for prenatal diagnosis and the early detection, prognosis, and treatment of gynecologic tumors. This review lays theoretical foundations for future investigations into the complex regulatory networks of eccDNAs in vital physiological and pathological processes.

Development of a multicriteria decision-making model for evaluating hybrid offspring in the sweetpotato (Ipomoea batatas L.) breeding process.

Sweetpotato variety breeding is always a long process. Screening of hybrid offspring is dominated by empirical judgment in this process. Data analysis and decision fatigue have been troubling breeders. In recent years, the low-efficiency screening mode has been unable to meet the requirements of sweetpotato germplasm innovation. Therefore, it is necessary to construct a high-efficiency method that can screen germplasms for different usages, for mining elite genotypes, and to create dedicated sweetpotato varieties. In this article, the multicriteria decision-making (MCDM) model was constructed based on six agronomic traits, including fresh root yield, vine length, vine diameter, branch number, root number and the spatial distribution of storage roots, and five quality traits, including dry matter content, marketable root yield, uniformity of roots, starch content and the edible quality score. Among these, the edible quality score was calculated by using fuzzy comprehensive evaluation to integrate the sensory scores of color, odor, sweetness, stickiness and fibrous taste. The MCDM model was compared with the traditional screening method via an evaluation in 25 sweetpotato materials. The interference of subjective factors on the evaluation results was significantly reduced. The MCDM model is more overall, more accurate and faster than the traditional screening method in the selection of elite sweetpotato materials. It could be programmed to serve the breeders in combination with the traditional screening method.

Residue behavior and processing factors of thirteen field-applied pesticides during the production of Chinese traditional fermented chopped pepper and chili powder.

In this study, the fate, processing factors and relationship with physicochemical properties of thirteen pesticides in field-collected pepper samples during Chinese chopped pepper and chili powder production was systematically studied. The washing, air-drying, chopping and salting and fermentation processes reduced 24.8%-62.8%, 0.9%-26.4%, 25.1%-50.3% and 16.3%-90.0% of thirteen pesticide residues, respectively, while the sun-drying processing increased the residues of eleven pesticides by 1.27-5.19 fold. The PFs of thirteen pesticides were < 1 in chopped pepper production and the PFs of eleven pesticides were more than 1 for chili powder production. The chopped pepper processing efficiency have most negative correlation with octanol-water partition coefficient. In contrast, the chili powder processing efficiency have most positive correlation with vapour pressure. Thus, this study can offer important references for assessment the pesticide residue levels in Chinese traditional fermented chopped pepper and chili powder production from fresh peppers.

Unraveling the Guardians of Growth: A Comprehensive Analysis of the Aux/IAA and ARF Gene Families in Populus simonii.

The auxin/indole-3-acetic acid (Aux/IAA) and auxin response factor (ARF) genes are two crucial gene families in the plant auxin signaling pathway. Nonetheless, there is limited knowledge regarding the Aux/IAA and ARF gene families in Populus simonii. In this study, we first identified 33 putative PsIAAs and 35 PsARFs in the Populus simonii genome. Analysis of chromosomal location showed that the PsIAAs and PsARFs were distributed unevenly across 17 chromosomes, with the greatest abundance observed on chromosomes 2. Furthermore, based on the homology of PsIAAs and PsARFs, two phylogenetic trees were constructed, classifying 33 PsIAAs and 35 PsARFs into three subgroups each. Five pairs of PsIAA genes were identified as the outcome of tandem duplication, but no tandem repeat gene pairs were found in the PsARF family. The expression profiling of PsIAAs and PsARFs revealed that several genes exhibited upregulation in different tissues and under various stress conditions, indicating their potential key roles in plant development and stress responses. The variance in expression patterns of specific PsIAAs and PsARFs was corroborated through RT-qPCR analysis. Most importantly, we instituted that the PsIAA7 gene, functioning as a central hub, exhibits interactions with numerous Aux/IAA and ARF proteins. Furthermore, subcellular localization findings indicate that PsIAA7 functions as a protein localized within the nucleus. To conclude, the in-depth analysis provided in this study will contribute significantly to advancing our knowledge of the roles played by PsIAA and PsARF families in both the development of P. simonii tissue and its responses to stress. The insights gained will serve as a valuable asset for further inquiries into the biological functions of these gene families.

ZFP36 loss-mediated BARX1 stabilization promotes malignant phenotypes by transactivating master oncogenes in NSCLC.

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, with high morbidity and mortality worldwide. Although the dysregulation of BARX1 expression has been shown to be associated with malignant cancers, including NSCLC, the underlying mechanism remains elusive. In this study, we identified BARX1 as a common differentially expressed gene in lung squamous cell carcinoma and adenocarcinoma. Importantly, we uncovered a novel mechanism behind the regulation of BARX1, in which ZFP36 interacted with 3'UTR of BARX1 mRNA to mediate its destabilization. Loss of ZFP36 led to the upregulation of BARX1, which further promoted the proliferation, migration and invasion of NSCLC cells. In addition, the knockdown of BARX1 inhibited tumorigenicity in mouse xenograft. We demonstrated that BARX1 promoted the malignant phenotypes by transactivating a set of master oncogenes involved in the cell cycle, DNA synthesis and metastasis. Overall, our study provides insights into the mechanism of BARX1 actions in NSCLC and aids a better understanding of NSCLC pathogenesis.

The emergence of influenza B as a major respiratory pathogen in the absence of COVID-19 during the 2021-2022 flu season in China.

BACKGROUND: The emergence of COVID-19 and the implementation of preventive measures and behavioral changes have led to a significant decrease in the prevalence of other respiratory viruses. However, the manner in which seasonal viruses will reemerge in the absence of COVID-19-related restrictions remains unknown. METHODS: Patients presenting with influenza-like illness in two hospitals in Beijing were subjected to testing for COVID-19, influenza A, and influenza B to determine the causative agent for viral infections. The prevalence of influenza B across China was confirmed using data from the Centers for Disease Control, China (China CDC). Clinical characteristics, laboratory findings, imaging results, and mortality data were collected for a cohort of 70 hospitalized patients with confirmed influenza B from 9 hospitals across China. RESULTS: Starting from October 2021, a substantial increase in the number of patients visiting the designated fever clinics in Beijing was observed, with this trend continuing until January 2022. COVID-19 tests conducted on these patients yielded negative results, while the positivity rate for influenza rose from approximately 8% in October 2021 to over 40% by late January 2022. The cases started to decline after this peak. Data from China CDC confirmed that influenza B is a major pathogen during the season. Sequencing of the viral strain revealed the presence of the Victoria-like lineage of the influenza B strain, with minor variations from the Florida/39/2018 strain. Analysis of the hospitalized patients' characteristics indicated that severe cases were relatively more prevalent among younger individuals, with an average age of 40.9 ± 24.1 years. Among the seven patients who succumbed to influenza, the average age was 30 ± 30.1 years. These patients exhibited secondary infections involving either bacterial or fungal pathogens and displayed elevated levels of cell death markers (such as LDH) and coagulation pathway markers (D-dimer). CONCLUSION: Influenza B represents a significant infection threat and can lead to substantial morbidity and mortality, particularly among young patients. To mitigate morbidity and mortality rates, it is imperative to implement appropriate vaccination and other preventive strategies.

High-throughput functional screen identifies YWHAZ as a key regulator of pancreatic cancer metastasis.

Pancreatic cancer is a leading cause of cancer death due to its early metastasis and limited response to the current therapies. Metastasis is a complicated multistep process, which is determined by complex genetic alterations. Despite the identification of many metastasis-related genes, distinguishing the drivers from numerous passengers and establishing the causality in cancer pathophysiology remains challenging. Here, we established a high-throughput and piggyBac transposon-based genetic screening platform, which enables either reduced or increased expression of chromosomal genes near the incorporation site of the gene search vector cassette that contains a doxycycline-regulated promoter. Using this strategy, we identified YWHAZ as a key regulator of pancreatic cancer metastasis. We demonstrated that functional activation of Ywhaz by the gene search vector led to enhanced metastatic capability in mouse pancreatic cancer cells. The metastasis-promoting role of YWHAZ was further validated in human pancreatic cancer cells. Overexpression of YWHAZ resulted in more aggressive metastatic phenotypes in vitro and a shorter survival rate in vivo by modulating epithelial-to-mesenchymal transition. Hence, our study established a high-throughput screening method to investigate the functional relevance of novel genes and validated YWHAZ as a key regulator of pancreatic cancer metastasis.

Sulfur-decorated Fe/C composite synthesized from MIL-88A(Fe) for peroxymonosulfate activation towards tetracycline degradation: Multiple active sites and non-radical pathway dominated mechanism.

Peroxymonosulfate (PMS)-mediated advanced oxidation processes gain growing attention in degrading antibiotics (e.g., tetracycline (TC)) in wastewater for their high capacity and relatively low cost, while designing efficient catalysts for PMS activation remains a challenge. In this study, a sulfur-doped Fe/C catalyst (Fe@C-S) synthesized from iron metal-organic frameworks (Fe-MOFs) was developed for PMS activation towards TC removal. Under optimal conditions, the TC removal efficiency of Fe@C-S150/PMS system within 40 min was 91.2%. Meanwhile, the k value for Fe@C-S150/PMS system (0.2038 min-1) was 3.36-fold as high as the S-free Fe@C-based PMS system. Also, Fe@C-S150/PMS system showed high robustness in different water matrices. Further studies found that the TC degradation mechanism was mainly ascribed to the non-radical pathway (1O2 and electron transfer). Fe nanoparticles, S and CO groups on the catalyst all participated in the generation of reactive oxygen species (ROS). Besides, S species could enhance the Fe2+/Fe3+ redox cycle and accelerate the electron transfer process. This work highlights the critical role of S in enhancing the catalytic performance of Fe/C-based catalysts for PMS activation, which would provide meaningful insights into the design of high-performance PMS activators for the sustainable remediation of emerging contaminants-polluted water bodies.

The role and mechanism of the gut microbiota in the development and treatment of diabetic kidney disease.

Diabetic kidney disease (DKD) is a common complication in patients with diabetes mellitus (DM). Increasing evidence suggested that the gut microbiota participates in the progression of DKD, which is involved in insulin resistance, renin-angiotensin system (RAS) activation, oxidative stress, inflammation and immunity. Gut microbiota-targeted therapies including dietary fiber, supplementation with probiotics or prebiotics, fecal microbiota transplantation and diabetic agents that modulate the gut microbiota, such as metformin, glucagon-like peptide-1 (GLP-1) receptor agonists, dipeptidyl peptidase-4 (DPP-4) inhibitors, and sodium-glucose transporter-2 (SGLT-2) inhibitors. In this review, we summarize the most important findings about the role of the gut microbiota in the pathogenesis of DKD and the application of gut microbiota-targeted therapies.

Role of nuclear receptors NlHR3 and NlFTZ-F1 in regulating molting and reproduction in Nilaparvata lugens (stål).

The nuclear receptors HR3 and FTZ-F1 are highly conserved and function to regulate molting and reproduction in both hemimetabolous and holometabolous insects. However, their roles in Nilaparvata lugens are largely unknown. In the present study, we discover that NlHR3 and NlFTZ-F1 are activated in the nymph stages by ecdysone signaling. Transcription disruption of NlHR3 and NlFTZ-F1 expression prevents nymph ecdysis and metamorphosis, which leads to abnormal appearance, malformed ovaries, and lethal phenotypes. In addition, we demonstrate that NlHR3 and NlFTZ-F1 regulate molting and reproduction by interacting with the intrinsic 20E and JH signaling pathways. Our work offers a deep insight into the action mechanisms of HR3 and FTZ-F1 in insects. Moreover, NlHR3 and NlFTZ-F1 could properly be exploited as potential target genes for developing RNAi-based pesticides to control N. lugens.

DGCddG: Deep Graph Convolution for Predicting Protein-Protein Binding Affinity Changes Upon Mutations.

Effectively and accurately predicting the effects of interactions between proteins after amino acid mutations is a key issue for understanding the mechanism of protein function and drug design. In this study, we present a deep graph convolution (DGC) network-based framework, DGCddG, to predict the changes of protein-protein binding affinity after mutation. DGCddG incorporates multi-layer graph convolution to extract a deep, contextualized representation for each residue of the protein complex structure. The mined channels of the mutation sites by DGC is then fitted to the binding affinity with a multi-layer perceptron. Experiments with results on multiple datasets show that our model can achieve relatively good performance for both single and multi-point mutations. For blind tests on datasets related to angiotensin-converting enzyme 2 binding with the SARS-CoV-2 virus, our method shows better results in predicting ACE2 changes, may help in finding favorable antibodies. Code and data availability: https://github.com/lennylv/DGCddG.

Simultaneous Prediction of Interaction Sites on the Protein and Peptide Sides of Complexes through Multilayer Graph Convolutional Networks.

Identifying the binding residues of protein-peptide complexes is essential for understanding protein function mechanisms and exploring drug discovery. Recently, many computational methods have been developed to predict the interaction sites of either protein or peptide. However, to our knowledge, no prediction method can simultaneously identify the interaction sites on both the protein and peptide sides. Here, we propose a deep graph convolutional network (GCN)-based method called GraphPPepIS to predict the interaction sites of protein-peptide complexes using protein and peptide structural information. We also propose a companion method, SeqPPepIS, for assisting with the lack of structural information and the flexibility of peptides. SepPPepIS replaces the peptide structural features in GraphPPepIS by learning features from peptide sequences. We performed a comprehensive evaluation of the benchmark data sets, and the results show that our two methods outperform state-of-the-art methods on the accurate interaction sites of both protein and peptide sides. We show that our methods can help improve protein-peptide docking. For docking data sets, our methods maintain robust performance in identifying binding sites, thereby enhancing the prediction of peptide binding poses. Finally, we visualized the analysis of protein and peptide graph embedding to demonstrate the learning ability of graph convolution in predicting interaction sites, which was mainly obtained through the shared parameters of a protein graph and peptide graph.

Annotation and evaluation of base editing outcomes in multiple cell types using CRISPRbase.

CRISPR-Cas base editing (BE) system is a powerful tool to expand the scope and efficiency of genome editing with single-nucleotide resolution. The editing efficiency, product purity, and off-target effect differ among various BE systems. Herein, we developed CRISPRbase (http://crisprbase.maolab.org), by integrating 1 252 935 records of base editing outcomes in more than 50 cell types from 17 species. CRISPRbase helps to evaluate the putative editing precision of different BE systems by integrating multiple annotations, functional predictions and a blasting system for single-guide RNA sequences. We systematically assessed the editing window, editing efficiency and product purity of various BE systems. Intensive efforts were focused on increasing the editing efficiency and product purity of base editors since the byproduct could be detrimental in certain applications. Remarkably, more than half of cancer-related off-target mutations were non-synonymous and extremely damaging to protein functions in most common tumor types. Luckily, most of these cancer-related mutations were passenger mutations (4840/5703, 84.87%) rather than cancer driver mutations (863/5703, 15.13%), indicating a weak effect of off-target mutations on carcinogenesis. In summary, CRISPRbase is a powerful and convenient tool to study the outcomes of different base editors and help researchers choose appropriate BE designs for functional studies.

Effects of insulin-like peptide 7 in Bemisia tabaci MED on tomato chlorosis virus transmission.

BACKGROUND: Tomato chlorosis virus (ToCV) is a semi-persistent plant virus that is primarily transmitted by the whitefly Bemisia tabaci (Hemiptera: Aleyrodidae). It causes a serious disease that lowers tomato yield. Insulin-like peptide (ILP), an insulin homolog, regulates trehalose metabolism in a variety of insects. In a previous study, we discovered that trehalose metabolism is required for whiteflies to transmit ToCV effectively. Furthermore, transcriptome sequencing revealed that the BtILP7 gene was highly expressed in B. tabaci infected with ToCV. Therefore, the whitefly ILP7 gene may facilitate the transmission of ToCV and be an attractive target for the control of whiteflies and subsequently ToCV. RESULTS: The ToCV content in B. tabaci MED was found to be correlated with BtILP7 gene expression. Subsequent RNA interference (RNAi) of the BtILP7 gene had a significant impact on B. tabaci MED's trehalose metabolism and reproductive capacity, as well as ability to transmit ToCV. CONCLUSIONS: These results indicate that the BtILP7 gene was closely related to ToCV transmission by regulating trehalose metabolism and reproduction behavior, thus providing a secure and environmentally friendly management strategy for the control of whiteflies and ToCV-caused disease. © 2022 Society of Chemical Industry.

ctP2ISP: Protein-Protein Interaction Sites Prediction Using Convolution and Transformer With Data Augmentation.

Protein-protein interactions are the basis of many cellular biological processes, such as cellular organization, signal transduction, and immune response. Identifying protein-protein interaction sites is essential for understanding the mechanisms of various biological processes, disease development, and drug design. However, it remains a challenging task to make accurate predictions, as the small amount of training data and severe imbalanced classification reduce the performance of computational methods. We design a deep learning method named ctP2ISP to improve the prediction of protein-protein interaction sites. ctP2ISP employs Convolution and Transformer to extract information and enhance information perception so that semantic features can be mined to identify protein-protein interaction sites. A weighting loss function with different sample weights is designed to suppress the preference of the model toward multi-category prediction. To efficiently reuse the information in the training set, a preprocessing of data augmentation with an improved sample-oriented sampling strategy is applied. The trained ctP2ISP was evaluated against current state-of-the-art methods on six public datasets. The results show that ctP2ISP outperforms all other competing methods on the balance metrics: F1, MCC, and AUPRC. In particular, our prediction on open tests related to viruses may also be consistent with biological insights. The source code and data can be obtained from https://github.com/lennylv/ctP2ISP.

DeepNup: Prediction of Nucleosome Positioning from DNA Sequences Using Deep Neural Network.

Nucleosome positioning is involved in diverse cellular biological processes by regulating the accessibility of DNA sequences to DNA-binding proteins and plays a vital role. Previous studies have manifested that the intrinsic preference of nucleosomes for DNA sequences may play a dominant role in nucleosome positioning. As a consequence, it is nontrivial to develop computational methods only based on DNA sequence information to accurately identify nucleosome positioning, and thus intend to verify the contribution of DNA sequences responsible for nucleosome positioning. In this work, we propose a new deep learning-based method, named DeepNup, which enables us to improve the prediction of nucleosome positioning only from DNA sequences. Specifically, we first use a hybrid feature encoding scheme that combines One-hot encoding and Trinucleotide composition encoding to encode raw DNA sequences; afterwards, we employ multiscale convolutional neural network modules that consist of two parallel convolution kernels with different sizes and gated recurrent units to effectively learn the local and global correlation feature representations; lastly, we use a fully connected layer and a sigmoid unit serving as a classifier to integrate these learned high-order feature representations and generate the final prediction outcomes. By comparing the experimental evaluation metrics on two benchmark nucleosome positioning datasets, DeepNup achieves a better performance for nucleosome positioning prediction than that of several state-of-the-art methods. These results demonstrate that DeepNup is a powerful deep learning-based tool that enables one to accurately identify potential nucleosome sequences.