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Article ; Online: Application of dsRNA in the Pine Wood Nematode, Bursaphelenchus xylophilus.

Wang, Chunyu / Guo, Kai

Methods in molecular biology (Clifton, N.J.)

2024 Volume 2771, Page(s) 133–139

Abstract: The pine wood nematode Bursaphelenchus xylophilus is one of the most destructive invasive species worldwide, causing the wilting and eventual death of pine trees. Despite recognition of their economic and environmental significance, it has thus far been ... ...

Abstract	The pine wood nematode Bursaphelenchus xylophilus is one of the most destructive invasive species worldwide, causing the wilting and eventual death of pine trees. Despite recognition of their economic and environmental significance, it has thus far been impossible to study the detailed gene functions of plant parasitic nematodes through conventional forward genetics and transgenic methods. RNA interference (RNAi), as a reverse genetics technology, offers great convenience for studying the functional genes of nematodes, including B. xylophilus. We here outline a protocol for RNAi of the ppm-1 gene in B. xylophilus, which has been reported to play crucial roles in the development and reproduction of other pathogenic nematodes. For RNAi, the T7 promoter was linked to the 5'-terminal of the target fragment by polymerase chain reaction (PCR), and then double-stranded RNA (dsRNA) was synthesized by in vitro transcription. Subsequently, dsRNA delivery was accomplished by soaking nematodes with the dsRNA solution mixed with synthetic neurostimulants. Synchronized eggs, juveniles, and adults of B. xylophilus (approximately 20,000 individuals of each stage) were washed and soaked in dsRNA (0.8 μg/mL) with the soaking buffer for 24 h in the dark at 25 °C. The same quantity of nematodes was placed in the soaking buffer without dsRNA or with green fluorescent protein dsRNA as a control. After soaking, the expression level of the target transcripts was determined by real-time quantitative PCR. The effects of RNAi were then confirmed by microscopic observation of the phenotypes and comparison of the body size of adults among groups. The current protocol can help to progress research to understand the functions of the genes of B. xylophilus and other parasitic nematodes toward developing control strategies through genetic engineering.
MeSH term(s)	Humans ; Adult ; RNA, Double-Stranded/genetics ; Xylophilus ; Eggs ; Pinus ; Real-Time Polymerase Chain Reaction
Chemical Substances	RNA, Double-Stranded
Language	English
Publishing date	2024-01-29
Publishing country	United States
Document type	Journal Article
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-3702-9_18
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Diagnosis and prognosis prediction model for digestive system tumors based on immunologic gene sets.

Zhou, Lin / Wang, Chunyu

Frontiers in oncology

2023 Volume 13, Page(s) 1107532

Abstract: According to 2020 global cancer statistics, digestive system tumors (DST) are ranked first in both incidence and mortality. This study systematically investigated the immunologic gene set (IGS) to discover effective diagnostic and prognostic biomarkers. ... ...

Abstract	According to 2020 global cancer statistics, digestive system tumors (DST) are ranked first in both incidence and mortality. This study systematically investigated the immunologic gene set (IGS) to discover effective diagnostic and prognostic biomarkers. Gene set variation (GSVA) analysis was used to calculate enrichment scores for 4,872 IGSs in patients with digestive system tumors. Using the machine learning algorithm XGBoost to build a classifier that distinguishes between normal samples and cancer samples, it shows high specificity and sensitivity on both the validation set and the overall dataset (area under the receptor operating characteristic curve [AUC]: validation set = 0.993, overall dataset = 0.999). IGS-based digestive system tumor subtypes (IGTS) were constructed using a consistent clustering approach. A risk prediction model was developed using the Least Absolute Shrinkage and Selection Operator (LASSO) method. DST is divided into three subtypes: subtype 1 has the best prognosis, subtype 3 is the second, and subtype 2 is the worst. The prognosis model constructed using nine gene sets can effectively predict prognosis. Prognostic models were significantly associated with tumor mutational burden (TMB), tumor immune microenvironment (TIME), immune checkpoints, and somatic mutations. A composite nomogram was constructed based on the risk score and the patient's clinical information, with a well-fitted calibration curve (AUC = 0.762). We further confirmed the reliability and validity of the diagnostic and prognostic models using other cohorts from the Gene Expression Omnibus database. We identified diagnostic and prognostic models based on IGS that provide a strong basis for early diagnosis and effective treatment of digestive system tumors.
Language	English
Publishing date	2023-03-03
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2649216-7
ISSN	2234-943X
ISSN	2234-943X
DOI	10.3389/fonc.2023.1107532
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Article ; Online: A computational model of circRNA-associated diseases based on a graph neural network: prediction and case studies for follow-up experimental validation.

Niu, Mengting / Wang, Chunyu / Zhang, Zhanguo / Zou, Quan

BMC biology

2024 Volume 22, Issue 1, Page(s) 24

Abstract: Background: Circular RNAs (circRNAs) have been confirmed to play a vital role in the occurrence and development of diseases. Exploring the relationship between circRNAs and diseases is of far-reaching significance for studying etiopathogenesis and ... ...

Abstract	Background: Circular RNAs (circRNAs) have been confirmed to play a vital role in the occurrence and development of diseases. Exploring the relationship between circRNAs and diseases is of far-reaching significance for studying etiopathogenesis and treating diseases. To this end, based on the graph Markov neural network algorithm (GMNN) constructed in our previous work GMNN2CD, we further considered the multisource biological data that affects the association between circRNA and disease and developed an updated web server CircDA and based on the human hepatocellular carcinoma (HCC) tissue data to verify the prediction results of CircDA. Results: CircDA is built on a Tumarkov-based deep learning framework. The algorithm regards biomolecules as nodes and the interactions between molecules as edges, reasonably abstracts multiomics data, and models them as a heterogeneous biomolecular association network, which can reflect the complex relationship between different biomolecules. Case studies using literature data from HCC, cervical, and gastric cancers demonstrate that the CircDA predictor can identify missing associations between known circRNAs and diseases, and using the quantitative real-time PCR (RT-qPCR) experiment of HCC in human tissue samples, it was found that five circRNAs were significantly differentially expressed, which proved that CircDA can predict diseases related to new circRNAs. Conclusions: This efficient computational prediction and case analysis with sufficient feedback allows us to identify circRNA-associated diseases and disease-associated circRNAs. Our work provides a method to predict circRNA-associated diseases and can provide guidance for the association of diseases with certain circRNAs. For ease of use, an online prediction server ( http://server.malab.cn/CircDA ) is provided, and the code is open-sourced ( https://github.com/nmt315320/CircDA.git ) for the convenience of algorithm improvement.
MeSH term(s)	Humans ; RNA, Circular/genetics ; RNA, Circular/analysis ; Carcinoma, Hepatocellular/genetics ; Follow-Up Studies ; Liver Neoplasms/genetics ; Neural Networks, Computer ; Computer Simulation ; Computational Biology/methods
Chemical Substances	RNA, Circular
Language	English
Publishing date	2024-01-29
Publishing country	England
Document type	Journal Article
ZDB-ID	2133020-7
ISSN	1741-7007 ; 1741-7007
ISSN (online)	1741-7007
ISSN	1741-7007
DOI	10.1186/s12915-024-01826-z
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Article ; Online: CircSI-SSL: circRNA-binding site identification based on self-supervised learning.

Cao, Chao / Wang, Chunyu / Yang, Shuhong / Zou, Quan

Bioinformatics (Oxford, England)

2024 Volume 40, Issue 1

Abstract: Motivation: In recent years, circular RNAs (circRNAs), the particular form of RNA with a closed-loop structure, have attracted widespread attention due to their physiological significance (they can directly bind proteins), leading to the development of ... ...

Abstract	Motivation: In recent years, circular RNAs (circRNAs), the particular form of RNA with a closed-loop structure, have attracted widespread attention due to their physiological significance (they can directly bind proteins), leading to the development of numerous protein site identification algorithms. Unfortunately, these studies are supervised and require the vast majority of labeled samples in training to produce superior performance. But the acquisition of sample labels requires a large number of biological experiments and is difficult to obtain. Results: To resolve this matter that a great deal of tags need to be trained in the circRNA-binding site prediction task, a self-supervised learning binding site identification algorithm named CircSI-SSL is proposed in this article. According to the survey, this is unprecedented in the research field. Specifically, CircSI-SSL initially combines multiple feature coding schemes and employs RNA_Transformer for cross-view sequence prediction (self-supervised task) to learn mutual information from the multi-view data, and then fine-tuning with only a few sample labels. Comprehensive experiments on six widely used circRNA datasets indicate that our CircSI-SSL algorithm achieves excellent performance in comparison to previous algorithms, even in the extreme case where the ratio of training data to test data is 1:9. In addition, the transplantation experiment of six linRNA datasets without network modification and hyperparameter adjustment shows that CircSI-SSL has good scalability. In summary, the prediction algorithm based on self-supervised learning proposed in this article is expected to replace previous supervised algorithms and has more extensive application value. Availability and implementation: The source code and data are available at https://github.com/cc646201081/CircSI-SSL.
MeSH term(s)	RNA, Circular ; Binding Sites ; RNA ; Algorithms ; Supervised Machine Learning
Chemical Substances	RNA, Circular ; RNA (63231-63-0)
Language	English
Publishing date	2024-01-05
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btae004
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Article ; Online: A Hierarchical Graph Neural Network Framework for Predicting Protein-Protein Interaction Modulators with Functional Group Information and Hypergraph Structure.

Zhang, Zitong / Zhao, Lingling / Wang, Junjie / Wang, Chunyu

IEEE journal of biomedical and health informatics

2024 Volume PP

Abstract: Accurate prediction of small molecule modulators targeting protein-protein interactions (PPIMs) remains a significant challenge in drug discovery. Existing machine learning-based models rely on manual feature engineering, which is tedious and task- ... ...

Abstract	Accurate prediction of small molecule modulators targeting protein-protein interactions (PPIMs) remains a significant challenge in drug discovery. Existing machine learning-based models rely on manual feature engineering, which is tedious and task-specific. Recently, deep learning models based on graph neural networks have made remarkable progress in molecular representation learning. However, many graph-based approaches ignore molecular hierarchical structure modeling guided by domain knowledge. In chemistry, the functional groups of a molecule determine its interaction with specific targets. Therefore, we propose a hierarchical graph neural network framework (called HiGPPIM) for predicting PPIMs by integrating atom-level and functional group-level features of molecules. HiGPPIM constructs atom-level and functional group-level graphs based on chemical knowledge and learns graph representations using graph attention networks. Furthermore, a hypergraph attention network is designed in HiGPPIM to aggregate and transform two-level graph information. We evaluate the performance of HiGPPIM on eight PPI families and two prediction tasks, namely PPIM identification and potency prediction. Experimental results demonstrate that HiGPPIM achieves state-of-the-art performance on both tasks and that using functional group information to guide PPIM prediction is effective. The source code and datasets are freely available at https://github.com/1zzt/HiGPPIM.
Language	English
Publishing date	2024-04-02
Publishing country	United States
Document type	Journal Article
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2024.3384238
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Article ; Online: AutoEdge-CCP: A novel approach for predicting cancer-associated circRNAs and drugs based on automated edge embedding.

Chen, Yaojia / Wang, Jiacheng / Wang, Chunyu / Zou, Quan

PLoS computational biology

2024 Volume 20, Issue 1, Page(s) e1011851

Abstract: The unique expression patterns of circRNAs linked to the advancement and prognosis of cancer underscore their considerable potential as valuable biomarkers. Repurposing existing drugs for new indications can significantly reduce the cost of cancer ... ...

Abstract	The unique expression patterns of circRNAs linked to the advancement and prognosis of cancer underscore their considerable potential as valuable biomarkers. Repurposing existing drugs for new indications can significantly reduce the cost of cancer treatment. Computational prediction of circRNA-cancer and drug-cancer relationships is crucial for precise cancer therapy. However, prior computational methods fail to analyze the interaction between circRNAs, drugs, and cancer at the systematic level. It is essential to propose a method that uncover more valuable information for achieving cancer-centered multi-association prediction. In this paper, we present a novel computational method, AutoEdge-CCP, to unveil cancer-associated circRNAs and drugs. We abstract the complex relationships between circRNAs, drugs, and cancer into a multi-source heterogeneous network. In this network, each molecule is represented by two types information, one is the intrinsic attribute information of molecular features, and the other is the link information explicitly modeled by autoGNN, which searches information from both intra-layer and inter-layer of message passing neural network. The significant performance on multi-scenario applications and case studies establishes AutoEdge-CCP as a potent and promising association prediction tool.
MeSH term(s)	Humans ; RNA, Circular/genetics ; RNA, Circular/metabolism ; Neoplasms/drug therapy ; Neoplasms/genetics ; Neural Networks, Computer ; Biomarkers
Chemical Substances	RNA, Circular ; Biomarkers
Language	English
Publishing date	2024-01-30
Publishing country	United States
Document type	Journal Article
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1011851
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Article: Development and application of low-density polyethylene-based multilayer film incorporating potassium permanganate and pumice for avocado preservation

Wang, Chunyu / Ajji, Abdellah

Food chemistry. 2023 Feb. 01, v. 401

2023

Abstract: This study aimed to develop a low-density polyethylene-based multilayer active packaging film with three layers. The core layer was an active layer containing pumice and potassium permanganate, while the skin layer was the barrier layer impregnated with ... ...

Abstract	This study aimed to develop a low-density polyethylene-based multilayer active packaging film with three layers. The core layer was an active layer containing pumice and potassium permanganate, while the skin layer was the barrier layer impregnated with sodium chloride. The multilayer film showed an ethylene scavenging capacity of 1.6 μmol/(25 in²) within 8 d at 25 °C and was endowed with water absorption capacity. In addition, the oxygen and water vapor permeability of the multilayer film were improved in comparison to the neat one. Further, the multilayer film extended the shelf life of avocado from less than 10 d to 16 d at 25 °C, controlled ethylene and carbon dioxide concentrations, and caused a reduction in the loss of flesh firmness and weight. More importantly, according to migration testing, active agents in the core layer would not migrate to avocado peel, which ensured that avocados would not be contaminated.
Keywords	avocados ; carbon dioxide ; ethylene ; firmness ; food chemistry ; oxygen ; permeability ; potassium permanganate ; pumice ; shelf life ; sodium chloride ; water binding capacity ; water vapor
Language	English
Dates of publication	2023-0201
Publishing place	Elsevier Ltd
Document type	Article
ZDB-ID	243123-3
ISSN	1873-7072 ; 0308-8146
ISSN (online)	1873-7072
ISSN	0308-8146
DOI	10.1016/j.foodchem.2022.134162
Database	NAL-Catalogue (AGRICOLA)

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Article: Hedgehog Autoprocessing: From Structural Mechanisms to Drug Discovery.

Kandel, Nabin / Wang, Chunyu

Frontiers in molecular biosciences

2022 Volume 9, Page(s) 900560

Abstract: Hedgehog (Hh) signaling plays pivotal roles in embryonic development. In adults, Hh signaling is mostly turned off but its abnormal activation is involved in many types of cancer. Hh signaling is initiated by the Hh ligand, generated from the Hh ... ...

Abstract	Hedgehog (Hh) signaling plays pivotal roles in embryonic development. In adults, Hh signaling is mostly turned off but its abnormal activation is involved in many types of cancer. Hh signaling is initiated by the Hh ligand, generated from the Hh precursor by a specialized autocatalytic process called Hh autoprocessing. The Hh precursor consists of an N-terminal signaling domain (HhN) and a C-terminal autoprocessing domain (HhC). During Hh autoprocessing, the precursor is cleaved between N- and C-terminal domain followed by the covalent ligation of cholesterol to the last residue of HhN, which subsequently leads to the generation of Hh ligand for Hh signaling. Hh autoprocessing is at the origin of canonical Hh signaling and precedes all downstream signaling events. Mutations in the catalytic residues in HhC can lead to congenital defects such as holoprosencephaly (HPE). The aim of this review is to provide an in-depth summary of the progresses and challenges towards an atomic level understanding of the structural mechanisms of Hh autoprocessing. We also discuss drug discovery efforts to inhibit Hh autoprocessing as a new direction in cancer therapy.
Language	English
Publishing date	2022-05-20
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2814330-9
ISSN	2296-889X
ISSN	2296-889X
DOI	10.3389/fmolb.2022.900560
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Article ; Online: Development and application of low-density polyethylene-based multilayer film incorporating potassium permanganate and pumice for avocado preservation.

Wang, Chunyu / Ajji, Abdellah

Food chemistry

2022 Volume 401, Page(s) 134162

Abstract	This study aimed to develop a low-density polyethylene-based multilayer active packaging film with three layers. The core layer was an active layer containing pumice and potassium permanganate, while the skin layer was the barrier layer impregnated with sodium chloride. The multilayer film showed an ethylene scavenging capacity of 1.6 μmol/(25 in
MeSH term(s)	Polyethylene ; Food Packaging ; Persea ; Potassium Permanganate ; Steam/analysis ; Carbon Dioxide ; Sodium Chloride ; Ethylenes ; Oxygen
Chemical Substances	Polyethylene (9002-88-4) ; Potassium Permanganate (00OT1QX5U4) ; pumice (NT5NN5KL16) ; Steam ; Carbon Dioxide (142M471B3J) ; Sodium Chloride (451W47IQ8X) ; ethylene (91GW059KN7) ; Ethylenes ; Oxygen (S88TT14065)
Language	English
Publishing date	2022-09-08
Publishing country	England
Document type	Journal Article
ZDB-ID	243123-3
ISSN	1873-7072 ; 0308-8146
ISSN (online)	1873-7072
ISSN	0308-8146
DOI	10.1016/j.foodchem.2022.134162
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Effect of a 2-Acrylamido-2-methylpropanesulfonic Acid-Based Fluid Loss Additive on the Hydration of Oil Well Cement.

Chen, Wei / Wang, Chunyu / Yao, Xiao / Song, Weikai / Zou, Yiwei

ACS omega

2024 Volume 9, Issue 8, Page(s) 9090–9097

Abstract: The fluid loss additive is to prevent the cement slurry from filtrating water to the formation under pressure. 2-Acrylamido-2-methylpropanesulfonic acid (AMPS)-based fluid loss additive mainly works by adsorbing on the surface of cement particles. The ... ...

Abstract	The fluid loss additive is to prevent the cement slurry from filtrating water to the formation under pressure. 2-Acrylamido-2-methylpropanesulfonic acid (AMPS)-based fluid loss additive mainly works by adsorbing on the surface of cement particles. The adsorption affects cement hydration. In this paper, the effect of one kind of AMPS-based fluid loss additive (A-FLA) on the hydration of oil well cement was studied. The water loss, setting time, thickening time, and compressive strength of cement slurry with various amounts of FLA were measured. In addition, the hydration heat of the cement slurry, FLA adsorption isotherm on cement particles, and hydration minerals were studied. The results showed that A-FLA had a good water loss control ability. The water loss of the cement slurry decreased slowly with the increase of A-FLA dosage when the adsorption capacity exceeded 4.47 mg/g. The low adsorption capacity of A-FLA (less than 4.47 mg/g) had a significant impact on the thickening time. With an adsorption capacity greater than 4.47 mg/g, the thickening time varied minimally. A-FLA mainly delayed the hydration of C
Language	English
Publishing date	2024-02-15
Publishing country	United States
Document type	Journal Article
ISSN	2470-1343
ISSN (online)	2470-1343
DOI	10.1021/acsomega.3c07890
Database	MEDical Literature Analysis and Retrieval System OnLINE

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