Article
Biochemistry & Molecular Biology
Minnie Jacob, Refat M. Nimer, Mohamad S. Alabdaljabar, Essa M. Sabi, Mysoon M. Al-Ansari, Maged Housien, Khalid M. Sumaily, Lina A. Dahabiyeh, Anas M. Abdel Rahman
Summary: This study used liquid chromatography-mass spectrometry (LC-MS) metabolomics to analyze the metabolome of serum from patients with nephrotic syndrome (NS) and found significant changes in 176 metabolites compared to the control group. Dysregulation of arginine, proline, and tryptophan metabolism, as well as arginine, phenylalanine, tyrosine, and tryptophan biosynthesis, were the most common metabolic pathways affected in NS.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Multidisciplinary Sciences
Changmeng Cui, Li Zhu, Qian Wang, Ruijuan Liu, Dadi Xie, Yujin Guo, Dingyi Yu, Changshui Wang, Dan Chen, Pei Jiang
Summary: The study investigated the metabolic changes after vancomycin administration in mice, revealing that vancomycin affects the metabolism in various organs, providing new insights for identification of vancomycin-induced toxicity.
Article
Biochemical Research Methods
T. Mouskeftara, C. Virgiliou, G. Theodoridis, H. Gika
Summary: The sensitive and precise GC-MS/MS method was developed for quantifying a large number of organic acids in human urine, based on key metabolic intermediates. Extensively validated, it can find wide applicability in metabolomics for clinical or nutritional studies.
JOURNAL OF CHROMATOGRAPHY A
(2021)
Article
Environmental Sciences
Shiyuan Zhao, Haitao Zhong, Chunmei Geng, Hongjia Xue, Changshui Wang, Wenxue Sun, Ruili Dang, Wenxiu Han, Pei Jiang
Summary: In this study, gas chromatography-mass spectrometry was used to investigate the toxic mechanisms of ACR in the main target tissues of rats, revealing 14 metabolic pathways related to amino acid, fatty acid, purine, and energy metabolism. The study suggests that the toxic mechanism of ACR may involve oxidative stress, inflammation, amino acid metabolism, and energy disorders.
ENVIRONMENTAL POLLUTION
(2021)
Article
Chemistry, Analytical
Krzysztof Ossolinski, Tomasz Ruman, Valerie Copie, Brian P. Tripet, Artur Kolodziej, Aneta Plaza-Altamer, Anna Ossolinska, Tadeusz Ossolinski, Anna Nieczaj, Joanna Niziol
Summary: This study investigated polar metabolite profiles in urine samples from BC patients and normal controls using NMR and LDI-MS and identified potential markers for bladder cancer. The metabolites were able to distinguish urine samples from BC and NCs individuals, and correlate with tumor stages and grades. The findings suggest that these metabolite markers may be useful for non-invasive detection and monitoring of bladder cancer.
JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS
(2023)
Article
Dermatology
Ali Azimi, Ellis Patrick, Rachel Teh, Jennifer Kim, Pablo Fernandez-Penas
Summary: Despite advances in managing metastatic melanomas, patients' overall survival remains low. This study investigated the proteome-wide changes associated with melanoma metastasis using mass spectrometry-based proteomics and bioinformatics analysis. Protein signatures were identified that distinguished regional lymph node and distant organ metastatic melanomas from primary lesions, providing valuable information for potential targeted therapies.
EXPERIMENTAL DERMATOLOGY
(2023)
Article
Environmental Sciences
Naeun Kim, Yongtae Ahn, Jungman Jo, Heesoo Pyo, Jeongae Lee, Jaeyoung Choi
Summary: This study investigated the effects of accidental contamination of soils with various chemicals, and found that phytosphingosine and Sphingomonas could potentially serve as biomarkers to evaluate soil contamination. Rain played an important role in the recovery of microbial and metabolic profiles after chemical accidents.
Article
Chemistry, Analytical
Olivier Perruchon, Isabelle Schmitz-Afonso, Carlos Afonso, Abdelhakim Elomri
Summary: The yeast Saccharomyces cerevisiae has been extensively studied in various aspects over the past two decades, with its metabolic pathways and metabolites analyzed using techniques such as mass spectrometry and nuclear magnetic resonance. These methods provide detailed information on the yeast's metabolism, aiding in global screening and structural elucidation.
MICROCHEMICAL JOURNAL
(2021)
Article
Biochemical Research Methods
Malena Manzi, Nicolas Zabalegui, Maria Eugenia Monge
Summary: The study evaluated a lipid panel that differentiated healthy individuals from clear cell renal cell carcinoma patients, and found that it could serve as an indicator for metabolic restoration after surgery. Specific lipids were able to distinguish patients with poor prognosis and could be used as prognostic tools during patient follow-up care.
JOURNAL OF PROTEOME RESEARCH
(2023)
Article
Nutrition & Dietetics
Stefania Noerman, Jyrki K. Virtanen, Marko Lehtonen, Carl Brunius, Kati Hanhineva
Summary: The study aimed to identify fasting serum metabolites associated with whole grain intake in a free-living population. The findings suggested strong associations between whole grain intake and several metabolites, which remained significant even after adjusting for potential confounders. These metabolites have the potential to serve as robust biomarkers for whole grain consumption.
EUROPEAN JOURNAL OF NUTRITION
(2023)
Article
Chemistry, Medicinal
Yahao Gao, Di Jiang, Changshui Wang, Gang An, Li Zhu, Changmeng Cui
Summary: This study comprehensively evaluated the toxicity of sodium valproate (VPA) using metabolomics, and found that VPA-induced toxicity is closely related to 12 key pathways including oxidative stress, inflammation, amino acid metabolism, lipid metabolism, and energy disorder.
DRUG DESIGN DEVELOPMENT AND THERAPY
(2022)
Article
Parasitology
Mingxing Zhu, Xiancai Du, Hongxia Xu, Songhao Yang, Chan Wang, Yazhou Zhu, Tingrui Zhang, Wei Zhao
Summary: Metabolomics approach revealed that the metabolism of nucleotides, alkaloids, amino acids, amides, and organic acids in mice is closely interrelated with E. granulosus infection. The study found that metabolic pathways in the liver such as tyrosine and tryptophan biosynthesis, phenylalanine, valine, leucine and isoleucine biosynthesis, and phenylalanine metabolism were notably associated with the occurrence and development of hydatid disease.
PARASITES & VECTORS
(2021)
Review
Medicine, Research & Experimental
Wenyong Peng, Wei Lu, Xiaofeng Jiang, Chang Xiong, Hua Chai, Libin Cai, Zhijian Lan
Summary: Postoperative cognitive dysfunction (POCD) is a common complication of the central nervous system (CNS) in elderly patients after surgery, characterized by cognitive changes and influenced by various risk factors. Central nervous inflammation is believed to play a critical role in its pathogenesis. The current diagnostic rate of POCD is relatively low, and the underlying mechanisms are still unclear. Early diagnosis and long-term treatment of POCD, as well as intervention strategies targeting central nervous inflammation, are of great significance for the prevention and treatment of POCD.
CURRENT MOLECULAR MEDICINE
(2023)
Article
Genetics & Heredity
Jinliang Peng, Chongrong Qiu, Jun Zhang, Xiaoliu Xiao
Summary: By using serum metabolomics, this study identified differential expression of metabolites in sepsis patients, which could potentially serve as diagnostic markers for sepsis. The pathogenesis of sepsis was found to be associated with mTOR signaling, NF-κB signaling pathway, calcium signaling, calcium transport, and tRNA charging pathway.
BMC MEDICAL GENOMICS
(2023)
Article
Chemistry, Analytical
Yuting Wang, Yisheng Wang, Chen Chen, Fang Ren, Rui Cao, Yuefei Wang, Pin Han, Xiaoyan Zhang, Congjian Xu, Xinyu Liu, Guowang Xu
Summary: The study identified lipid disturbances in the serum of patients with epithelial ovarian cancer (EOC) and established 4 lipids as potential markers for aiding EOC diagnosis. These markers showed high accuracy in distinguishing EOC and early EOC from non-cancer, with good specificity and sensitivity. The research not only revealed the characteristics of lipid metabolism in EOC, but also provided a potential marker pattern for aiding EOC diagnosis.
JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS
(2021)
Article
Chemistry, Multidisciplinary
Hai-yan Wang, Pian Yu, Xi-sha Chen, Hui Wei, Shi-jie Cao, Meng Zhang, Yi Zhang, Yong-guang Tao, Dong-sheng Cao, Feng Qiu, Yan Cheng
Summary: Physapubenolide (PB), a compound extracted from Physalis minima L., has shown cytotoxic effects on cancer cells by targeting HMGCR. This inhibition leads to decreased proliferation and migration in melanoma cells, as well as increased sensitivity to vemurafenib.
ACTA PHARMACOLOGICA SINICA
(2022)
Article
Chemistry, Multidisciplinary
Xue-ping Hu, Liu Yang, Xin Chai, Yi-xuan Lei, Md Shah Alam, Lu Liu, Chao Shen, De-jun Jiang, Zhe Wang, Zhi-yong Liu, Lei Xu, Kang-lin Wan, Tian-yu Zhang, Yue-lan Yin, Dan Li, Dong-sheng Cao, Ting-jun Hou
Summary: This study utilized an integrated molecular modeling strategy to identify two lead compounds that could inhibit DprE1 and showed inhibitory activity against Mycobacterium tuberculosis in vitro, with low cytotoxicity against mouse embryo fibroblast NIH-3T3 cells. This research provides an effective strategy for discovering novel anti-TB lead compounds.
ACTA PHARMACOLOGICA SINICA
(2022)
Article
Biochemical Research Methods
Mingyang Wang, Huiyong Sun, Jike Wang, Jinping Pang, Xin Chai, Lei Xu, Honglin Li, Dongsheng Cao, Tingjun Hou
Summary: This study evaluates the advantages and disadvantages of deep learning-based de novo drug design methods by comparing their performance in different scenarios. The results show that DL-based models have significant advantages in learning the property distributions of the training sets and are more suitable for target-specific tasks. However, these models cannot fully exploit the scaffolds of the training sets and generate molecules with lower scaffold diversity.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Dejun Jiang, Huiyong Sun, Jike Wang, Chang-Yu Hsieh, Yuquan Li, Zhenxing Wu, Dongsheng Cao, Jian Wu, Tingjun Hou
Summary: In this study, a data-driven deep graph learning framework called SuperAtomicCharge was developed to accurately predict partial charges derived from high-level quantum mechanics calculations. The model simultaneously utilized 2D and 3D structural information of molecules, resulting in improved prediction accuracy. Compared to other baseline models, SuperAtomicCharge showed superior performance on external test sets and had better usability and portability. Furthermore, the predicted partial charges from SuperAtomicCharge were found to be more robust and effective in drug design applications. The research also provided online server access and source code for easy use of SuperAtomicCharge services.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Xi Yang, Wei Wang, Jing-Lun Ma, Yan-Long Qiu, Kai Lu, Dong-Sheng Cao, Cheng-Kun Wu
Summary: This paper introduces a deep biological network model BioNet with a graph encoder-decoder architecture for predicting chemical-gene interactions. BioNet utilizes graph convolution to learn latent information from complex interactions among chemicals, genes, diseases and biological pathways. Through parallel training algorithm and multiple GPUs, BioNet achieves outstanding prediction performance in CGI prediction, surpassing current state-of-the-art methods.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Pharmacology & Pharmacy
Lingjie Bao, Zhe Wang, Zhenxing Wu, Hao Luo, Jiahui Yu, Yu Kang, Dongsheng Cao, Tingjun Hou
Summary: In this study, a model called AMGU was developed to predict the inhibitory activities of small molecules against various kinases. The AMGU model outperformed other models on both internal and external test sets, demonstrating its enhanced generalizability. Additionally, a method called edges masking was devised to explain the predictive mechanisms, and a web server called KIP was developed for predicting the polypharmacology effects of small molecules on the kinome.
ACTA PHARMACEUTICA SINICA B
(2023)
Article
Engineering, Environmental
Haoshi Gao, Stanislav Kan, Zhuyifan Ye, Yuchen Feng, Lei Jin, Xudong Zhang, Jiayin Deng, Ging Chan, Yuanjia Hu, Yongjun Wang, Dongsheng Cao, Yuanhui Ji, Mingtao Liang, Haifeng Li, Defang Ouyang
Summary: siRNA gene silencing therapy has great potential for treating diseases, but the formulation design of siRNA-LNP faces challenges. A novel integrated computer methodology based on machine learning and molecular dynamic simulation was successfully developed for siRNA-LNP formulation design.
CHEMICAL ENGINEERING JOURNAL
(2022)
Article
Chemistry, Medicinal
Jiahui Yu, Jike Wang, Hong Zhao, Junbo Gao, Yu Kang, Dongsheng Cao, Zhe Wang, Tingjun Hou
Summary: In this study, a data-driven interpretable prediction framework called GASA was proposed to evaluate the synthetic accessibility of small molecules. GASA, using a graph neural network architecture, achieved remarkable performance by automatically capturing important structural features to distinguish easy-to-synthesize and hard-to-synthesize compounds. GASA also learned the important features affecting molecular synthetic accessibility by assigning attention weights.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2022)
Article
Chemistry, Medicinal
Mingyang Wang, Chang-Yu Hsieh, Jike Wang, Dong Wang, Gaoqi Weng, Chao Shen, Xiaojun Yao, Zhitong Bing, Honglin Li, Dongsheng Cao, Tingjun Hou
Summary: The paper proposes a new 3D-based generative model called RELATION, which efficiently generates novel molecules with favorable binding affinity and pharmacophore features.
JOURNAL OF MEDICINAL CHEMISTRY
(2022)
Article
Multidisciplinary Sciences
Hongyan Du, Dejun Jiang, Junbo Gao, Xujun Zhang, Lingxiao Jiang, Yundian Zeng, Zhenxing Wu, Chao Shen, Lei Xu, Dongsheng Cao, Tingjun Hou, Peichen Pan
Summary: Covalent ligands have unique advantages and developing computational methods to identify their binding sites is crucial. DeepCoSI is the first deep learning model for identifying ligandable covalent sites in proteins, and it demonstrates excellent predictive performance.
Article
Biochemistry & Molecular Biology
Gaoqi Weng, Xuanyan Cai, Dongsheng Cao, Hongyan Du, Chao Shen, Yafeng Deng, Qiaojun He, Bo Yang, Dan Li, Tingjun Hou
Summary: PROTAC-DB 2.0 is an updated online database that contains structural and experimental data about PROTACs. This second version expands the number of PROTACs to 3270 and provides additional information to aid in the understanding and design of PROTACs.
NUCLEIC ACIDS RESEARCH
(2023)
Article
Chemistry, Multidisciplinary
Gaoang Wang, Jiahui Yu, Hongyan Du, Chao Shen, Xujun Zhang, Yifei Liu, Yangyang Zhang, Dongsheng Cao, Peichen Pan, Tingjun Hou
Summary: As an important member of ion channels family, the voltage-gated sodium channel is associated with various diseases. Researchers have developed the first open-source database for voltage-gated sodium channels, providing comprehensive compound data for users to search and query.
JOURNAL OF CHEMINFORMATICS
(2022)
Article
Chemistry, Medicinal
Teng-Zhi Long, Shao-Hua Shi, Shao Liu, Ai-Ping Lu, Zhao-Qian Liu, Min Li, Ting-Jun Hou, Dong-Sheng Cao
Summary: This study constructed a high-quality dataset and established a series of classification models using machine learning algorithms to predict hematotoxicity. The best model based on Attentive FP showed excellent performance on both the validation and test sets. Additionally, the study utilized SHAP and atom heatmap methods to identify important features and structural fragments related to hematotoxicity, and employed MMPA and representative substructure derivation technique to further investigate the transformation principles and distinctive structural features of hematotoxic chemicals.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Instruments & Instrumentation
Jiayin Deng, Zhuyifan Ye, Wenwen Zheng, Jian Chen, Haoshi Gao, Zheng Wu, Ging Chan, Yongjun Wang, Dongsheng Cao, Yanqing Wang, Simon Ming-Yuen Lee, Defang Ouyang
Summary: Microspheres have attracted attention from the pharmaceutical and medical industry due to their excellent biodegradability and long controlled-release characteristics. This research successfully built a prediction model using machine learning techniques to accelerate microspheres product development for small-molecule drugs. The consensus model achieved high accuracy in predicting the in vitro drug release profiles and can provide meaningful insights for microspheres development.
DRUG DELIVERY AND TRANSLATIONAL RESEARCH
(2023)
Article
Biochemistry & Molecular Biology
Jiashun Mao, Shenghui Guan, Yongqing Chen, Amir Zeb, Qingxiang Sun, Ranlan Lu, Jie Dong, Jianmin Wang, Dongsheng Cao
Summary: Antimicrobial resistance could be a serious threat to millions of lives. Antimicrobial peptides (AMPs) offer an alternative to conventional antibiotics for combating infectious diseases. However, developing and optimizing AMPs face significant challenges, and advanced methods are needed to overcome these challenges and create effective AMP-driven treatments.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2023)
