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Hou B, Wang D, Yan F, Cheng X, Xu Y, Xi X, Ge W, Sun S, Su P, Zhao L, Lyu Z, Hao Y, Wang H, Kong L. Fhb7-GST catalyzed glutathionylation effectively detoxifies the trichothecene family. Food Chem 2024; 439:138057. [PMID: 38100874 DOI: 10.1016/j.foodchem.2023.138057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 11/05/2023] [Accepted: 11/20/2023] [Indexed: 12/17/2023]
Abstract
Trichothecene (TCN) contamination in food and feed is a serious challenge due to the negative health and economic impacts. Here, we confirmed that the glutathione S-transferase (GST) Fhb7-GST could broadly catalyze type A, type B and type D TCNs into glutathione epoxide adducts (TCN-13-GSHs). To evaluate the toxicity of TCN-13-GSH adducts, we performed cell proliferation assays in vitro, which demonstrated decreased cytotoxicity of the adducts. Moreover, in vivo assays (repeated-dose treatment in mice) confirmed that TCN-13-GSH adducts were dramatically less toxic than the corresponding TCNs. To establish whether TCN-13-GSH was metabolized back to free toxin during digestion, single-dose metabolic tests were performed in rats; DON-13-GSH was not hydrolyzed in vivo, but rather was quickly metabolized to another low-toxicity compound, DON-13-N-acetylcysteine. These results demonstrate the promise of Fhb7-GST as a candidate of detoxification enzyme potentially applied in TCN-contaminated agricultural samples, minimizing the detrimental effects of the mycotoxin.
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Affiliation(s)
- Bingqian Hou
- State Key Laboratory of Wheat Breeding, College of Agronomy, Shandong Agricultural University, Tai'an 271018, PR China
| | - Dawei Wang
- State Key Laboratory of Wheat Breeding, College of Agronomy, Shandong Agricultural University, Tai'an 271018, PR China
| | - Fangfang Yan
- State Key Laboratory of Wheat Breeding, College of Agronomy, Shandong Agricultural University, Tai'an 271018, PR China
| | - Xinxin Cheng
- State Key Laboratory of Wheat Breeding, College of Agronomy, Shandong Agricultural University, Tai'an 271018, PR China
| | - Yongchang Xu
- State Key Laboratory of Wheat Breeding, College of Agronomy, Shandong Agricultural University, Tai'an 271018, PR China
| | - Xuepeng Xi
- College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an 271018, PR China
| | - Wenyang Ge
- College of Agronomy, Anhui Agricultural University, Key Laboratory of Wheat Biology and Genetic Improvement on Southern Yellow & Huai River Valley, Ministry of Agriculture and Rural Affairs, Hefei 230036, PR China
| | - Silong Sun
- State Key Laboratory of Wheat Breeding, College of Agronomy, Shandong Agricultural University, Tai'an 271018, PR China
| | - Peisen Su
- College of Agronomy, Liaocheng University, Liaocheng 252059, PR China
| | - Lanfei Zhao
- Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA
| | - Zhongfan Lyu
- Shool of Life Sciences and Medicine, Shandong University of Technology, Zibo 255000, PR China
| | - Yongchao Hao
- State Key Laboratory of Wheat Breeding, College of Agronomy, Shandong Agricultural University, Tai'an 271018, PR China
| | - Hongwei Wang
- State Key Laboratory of Wheat Breeding, College of Agronomy, Shandong Agricultural University, Tai'an 271018, PR China.
| | - Lingrang Kong
- State Key Laboratory of Wheat Breeding, College of Agronomy, Shandong Agricultural University, Tai'an 271018, PR China
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Jiang J, Rezaeitaleshmahalleh M, Lyu Z, Mu N, Ahmed AS, Md CMS, Gemmete JJ, Pandey AS. Augmenting Prediction of Intracranial Aneurysms' Risk Status Using Velocity-Informatics: Initial Experience. J Cardiovasc Transl Res 2023; 16:1153-1165. [PMID: 37160546 PMCID: PMC10949935 DOI: 10.1007/s12265-023-10394-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 04/26/2023] [Indexed: 05/11/2023]
Abstract
Our primary goal here is to demonstrate that innovative analytics of aneurismal velocities, named velocity-informatics, enhances intracranial aneurysm (IA) rupture status prediction. 3D computer models were generated using imaging data from 112 subjects harboring anterior IAs (4-25 mm; 44 ruptured and 68 unruptured). Computational fluid dynamics simulations and geometrical analyses were performed. Then, computed 3D velocity vector fields within the IA dome were processed for velocity-informatics. Four machine learning methods (support vector machine, random forest, generalized linear model, and GLM with Lasso or elastic net regularization) were employed to assess the merits of the proposed velocity-informatics. All 4 ML methods consistently showed that, with velocity-informatics metrics, the area under the curve and prediction accuracy both improved by approximately 0.03. Overall, with velocity-informatics, the support vector machine's prediction was most promising: an AUC of 0.86 and total accuracy of 77%, with 60% and 88% of ruptured and unruptured IAs being correctly identified, respectively.
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Affiliation(s)
- J Jiang
- Department of Biomedical Engineering, Michigan Technological University, Houghton, MI, 49931, USA.
- Center for Biocomputing and Digital Health, Health Research Institute, and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, MI, USA.
- Department of Medical Physics, University of Wisconsin, Madison, WI, USA.
| | - M Rezaeitaleshmahalleh
- Department of Biomedical Engineering, Michigan Technological University, Houghton, MI, 49931, USA
- Center for Biocomputing and Digital Health, Health Research Institute, and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, MI, USA
| | - Z Lyu
- Department of Biomedical Engineering, Michigan Technological University, Houghton, MI, 49931, USA
- Center for Biocomputing and Digital Health, Health Research Institute, and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, MI, USA
| | - Nan Mu
- Department of Biomedical Engineering, Michigan Technological University, Houghton, MI, 49931, USA
- Center for Biocomputing and Digital Health, Health Research Institute, and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, MI, USA
| | - A S Ahmed
- Department of Neurosurgery, University of Wisconsin, Madison, WI, USA
- Department of Radiology, University of Wisconsin, Madison, WI, USA
| | - C M Strother Md
- Department of Radiology, University of Wisconsin, Madison, WI, USA
| | - J J Gemmete
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - A S Pandey
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI, USA
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Xu S, Lyu Z, Zhang N, Li M, Wei X, Gao Y, Cheng X, Ge W, Li X, Bao Y, Yang Z, Ma X, Wang H, Kong L. Genetic mapping of the wheat leaf rust resistance gene Lr19 and development of translocation lines to break its linkage with yellow pigment. Theor Appl Genet 2023; 136:200. [PMID: 37639002 DOI: 10.1007/s00122-023-04425-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 07/24/2023] [Indexed: 08/29/2023]
Abstract
KEY MESSAGE The leaf rust resistance gene Lr19, which is present on the long arm of chromosome 7E1 in Thinopyrum ponticum, was mapped within a 0.3-cM genetic interval, and translocation lines were developed to break its linkage with yellow pigmentation The leaf rust resistance locus Lr19, which was transferred to wheat (Triticum aestivum) from its relative Thinopyrum ponticum in 1966, still confers broad resistance to most known races of the leaf rust pathogen Puccinia triticina (Pt) worldwide. However, this gene has not previously been fine-mapped, and its tight linkage with a gene causing yellow pigmentation has limited its application in bread wheat breeding. In this study, we genetically mapped Lr19 using a bi-parental population from a cross of two wheat-Th. ponticum substitution lines, the Lr19-carrying line 7E1(7D) and the leaf rust-susceptible line 7E2(7D). Genetic analysis of the F2 population and the F2:3 families showed that Lr19 was a single dominant gene. Genetic markers allowed the gene to be mapped within a 0.3-cM interval on the long arm of Th. ponticum chromosome 7E1, flanked by markers XsdauK3734 and XsdauK2839. To reduce the size of the Th. ponticum chromosome segment carrying Lr19, the Chinese Spring Ph1b mutant was employed to promote recombination between the homoeologous chromosomes of the wheat chromosome 7D and the Th. ponticum chromosome 7E1. Two translocation lines with short Th. ponticum chromosome fragments carrying Lr19 were identified using the genetic markers closely linked to Lr19. Both translocation lines were resistant to 16 Pt races collected throughout China. Importantly, the linkage between Lr19 and yellow pigment content was broken in one of the lines. Thus, the Lr19 linked markers and translocation lines developed in this study are valuable resources in marker-assisted selection as part of common wheat breeding programs.
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Affiliation(s)
- Shoushen Xu
- National Key Laboratory of Wheat Improvement, Shandong Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, Shandong, People's Republic of China
| | - Zhongfan Lyu
- National Key Laboratory of Wheat Improvement, Shandong Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, Shandong, People's Republic of China
| | - Na Zhang
- College of Plant Protection, Technological Innovation Center for Biological Control Crop Diseases and Insect Pests of Hebei Province, Hebei Agricultural University, Baoding, 071001, Hebei, People's Republic of China
| | - Mingzhu Li
- National Key Laboratory of Wheat Improvement, Shandong Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, Shandong, People's Republic of China
| | - Xinyi Wei
- National Key Laboratory of Wheat Improvement, Shandong Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, Shandong, People's Republic of China
| | - Yuhang Gao
- National Key Laboratory of Wheat Improvement, Shandong Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, Shandong, People's Republic of China
| | - Xinxin Cheng
- National Key Laboratory of Wheat Improvement, Shandong Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, Shandong, People's Republic of China
| | - Wenyang Ge
- National Key Laboratory of Wheat Improvement, Shandong Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, Shandong, People's Republic of China
| | - Xuefeng Li
- National Key Laboratory of Wheat Improvement, Shandong Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, Shandong, People's Republic of China
| | - Yinguang Bao
- National Key Laboratory of Wheat Improvement, Shandong Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, Shandong, People's Republic of China
| | - Zujun Yang
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, Sichun, People's Republic of China
| | - Xin Ma
- National Key Laboratory of Wheat Improvement, Shandong Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, Shandong, People's Republic of China
| | - Hongwei Wang
- National Key Laboratory of Wheat Improvement, Shandong Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, Shandong, People's Republic of China.
| | - Lingrang Kong
- National Key Laboratory of Wheat Improvement, Shandong Key Laboratory of Wheat Improvement, College of Agronomy, Shandong Agricultural University, Tai'an, 271018, Shandong, People's Republic of China.
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Mu N, Rezaeitaleshmahalleh M, Lyu Z, Wang M, Tang J, Strother CM, Gemmete JJ, Pandey AS, Jiang J. Can we explain machine learning-based prediction for rupture status assessments of intracranial aneurysms? Biomed Phys Eng Express 2023; 9:037001. [PMID: 36626819 PMCID: PMC9999353 DOI: 10.1088/2057-1976/acb1b3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 01/04/2023] [Accepted: 01/10/2023] [Indexed: 01/11/2023]
Abstract
Although applying machine learning (ML) algorithms to rupture status assessment of intracranial aneurysms (IA) has yielded promising results, the opaqueness of some ML methods has limited their clinical translation. We presented the first explainability comparison of six commonly used ML algorithms: multivariate logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron neural network (MLPNN), and Bayesian additive regression trees (BART). A total of 112 IAs with known rupture status were selected for this study. The ML-based classification used two anatomical features, nine hemodynamic parameters, and thirteen morphologic variables. We utilized permutation feature importance, local interpretable model-agnostic explanations (LIME), and SHapley Additive exPlanations (SHAP) algorithms to explain and analyze 6 Ml algorithms. All models performed comparably: LR area under the curve (AUC) was 0.71; SVM AUC was 0.76; RF AUC was 0.73; XGBoost AUC was 0.78; MLPNN AUC was 0.73; BART AUC was 0.73. Our interpretability analysis demonstrated consistent results across all the methods; i.e., the utility of the top 12 features was broadly consistent. Furthermore, contributions of 9 important features (aneurysm area, aneurysm location, aneurysm type, wall shear stress maximum during systole, ostium area, the size ratio between aneurysm width, (parent) vessel diameter, one standard deviation among time-averaged low shear area, and one standard deviation of temporally averaged low shear area less than 0.4 Pa) were nearly the same. This research suggested that ML classifiers can provide explainable predictions consistent with general domain knowledge concerning IA rupture. With the improved understanding of ML algorithms, clinicians' trust in ML algorithms will be enhanced, accelerating their clinical translation.
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Affiliation(s)
- N Mu
- Biomedical Engineering, Michigan Technological University, Houghton, MI, United States of America
| | - M Rezaeitaleshmahalleh
- Biomedical Engineering, Michigan Technological University, Houghton, MI, United States of America
| | - Z Lyu
- Biomedical Engineering, Michigan Technological University, Houghton, MI, United States of America
| | - M Wang
- Department of Management Science and Statistics, The University of Texas at San Antonio, San Antonino, TX, United States of America
| | - J Tang
- Department of Health Administration and Policy, George Mason University, Fairfax, VA, United States of America
| | - C M Strother
- Department of Radiology, University of Wisconsin, Madison, WI, United States of America
| | - J J Gemmete
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States of America
| | - A S Pandey
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI, United States of America
| | - J Jiang
- Biomedical Engineering, Michigan Technological University, Houghton, MI, United States of America
- Center for Biocomputing and Digital Health, Health Research Institute and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, MI, United States of America
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Lyu Z, Hao Y, Chen L, Xu S, Wang H, Li M, Ge W, Hou B, Cheng X, Li X, Che N, Zhen T, Sun S, Bao Y, Yang Z, Jia J, Kong L, Wang H. Wheat- Thinopyrum Substitution Lines Imprint Compensation Both From Recipients and Donors. Front Plant Sci 2022; 13:837410. [PMID: 35498638 PMCID: PMC9051513 DOI: 10.3389/fpls.2022.837410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 02/04/2022] [Indexed: 06/14/2023]
Abstract
Even frequently used in wheat breeding, we still have an insufficient understanding of the biology of the products via distant hybridization. In this study, a transcriptomic analysis was performed for six Triticum aestivum-Thinopyrum elongatum substitution lines in comparison with the host plants. All the six disomic substitution lines showed much stronger "transcriptomic-shock" occurred on alien genomes with 57.43-69.22% genes changed expression level but less on the recipient genome (2.19-8.97%). Genome-wide suppression of alien genes along chromosomes was observed with a high proportion of downregulated genes (39.69-48.21%). Oppositely, the wheat recipient showed genome-wide compensation with more upregulated genes, occurring on all chromosomes but not limited to the homeologous groups. Moreover, strong co-upregulation of the orthologs between wheat and Thinopyrum sub-genomes was enriched in photosynthesis with predicted chloroplastic localization, which indicates that the compensation happened not only on wheat host genomes but also on alien genomes.
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Affiliation(s)
- Zhongfan Lyu
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Taian, China
| | - Yongchao Hao
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Taian, China
| | - Liyang Chen
- Smartgenomics Technology Institute, Tianjin, China
| | - Shoushen Xu
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Taian, China
| | - Hongjin Wang
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Mengyao Li
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Taian, China
| | - Wenyang Ge
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Taian, China
| | - Bingqian Hou
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Taian, China
| | - Xinxin Cheng
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Taian, China
| | - Xuefeng Li
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Taian, China
| | - Naixiu Che
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Taian, China
| | - Tianyue Zhen
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Taian, China
| | - Silong Sun
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Taian, China
| | - Yinguang Bao
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Taian, China
| | - Zujun Yang
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jizeng Jia
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
| | - Lingrang Kong
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Taian, China
| | - Hongwei Wang
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Taian, China
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Li GR, Li H, Lyu Z, Chen Z, Wang YG. [Bilateral superior cervical ganglionectomy attenuates cardiac remodeling and improves cardiac function in pressure-overloaded heart failure mice]. Zhonghua Xin Xue Guan Bing Za Zhi 2021; 49:345-352. [PMID: 33874684 DOI: 10.3760/cma.j.cn112148-20200603-00458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To investigate the effect of bilateral superior cervical ganglionectomy on cardiac remodeling and function in pressure-overloaded heart failure (HF) mice. Methods: Pressure-overloaded HF mouse model was produced by severe thoracic aorta banding (sTAB). Bilateral superior cervical ganglionectomy (SCGx) was performed 2 weeks after sTAB. Twenty four 6-week-old male C57BL/6 mice were randomized divided into 4 groups (n=6 each): control group: sham sTAB+sham SCGx; denervated group: sham sTAB+SCGx; HF group: sTAB+sham SCGx; denervated HF group: sTAB+SCGx. Cardiac function was measured by echocardiography at week 0, 1, 2, and 4 after sTAB, respectively. All mice were sacrificed at the end of week 4 and heart tissues were harvested. HE and Masson staining were performed. Immunohistochemical staining (IHC) for tyrosine hydroxylase (TH), adrenergic receptor β1 (AR-β1) and CD68 was performed. Western blot was used to determine the protein expression level of TH, B type natriuretic peptide (BNP), and AR-β1. Results: Left ventricular ejection fraction (LVEF) declined continuously in HF group. LVEF was similar between denervated HF group and control group at various time points (P>0.05). LVEF was significantly higher in denervated HF group than in HF group at the end of week 4 (P<0.05). HE staining showed that cross sectional cardiomyocyte area was significantly larger in HF group than in control group and denervated HF group (P<0.05), which was similar between denervated HF group and control group (P>0.05). Masson staining showed that fibrosis level was significantly lower in denervated HF group than in HF group (P<0.05). IHC showed that TH+nerves and CD68+ macrophages were significantly increased in HF mice as compared to control mice (P<0.05), whereas this change was abolished in denervated HF group. AR-β1 was significantly down-regulated in HF group compared with control group (P<0.05), which was not affected by denervation (P>0.05). Western blot demonstrated that the expression level of TH and BNP was significantly higher in HF group compared with the control group (P<0.05), whereas this difference was diminished in denervated HF group (P>0.05). Conclusion: Bilateral superior cervical ganglionectomy can reduce sympathetic innervation and macrophage infiltration in pressure overloaded failure heart, thus attenuate cardiac remodeling and improve cardiac function.
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Affiliation(s)
- G R Li
- Department of Cardiology, Zhongnan Hospital of Wuhan University, Wuhan 430071, China
| | - H Li
- Department of Cardiology, Zhongnan Hospital of Wuhan University, Wuhan 430071, China
| | - Z Lyu
- Department of Cardiology, Zhongnan Hospital of Wuhan University, Wuhan 430071, China
| | - Z Chen
- Department of Cardiology, Zhongnan Hospital of Wuhan University, Wuhan 430071, China
| | - Y G Wang
- Department of Internal Medicine, Zhongnan Hospital of Wuhan University, Wuhan 430071, China
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Wang H, Sun S, Ge W, Zhao L, Hou B, Wang K, Lyu Z, Chen L, Xu S, Guo J, Li M, Su P, Li X, Wang G, Bo C, Fang X, Zhuang W, Cheng X, Wu J, Dong L, Chen W, Li W, Xiao G, Zhao J, Hao Y, Xu Y, Gao Y, Liu W, Liu Y, Yin H, Li J, Li X, Zhao Y, Wang X, Ni F, Ma X, Li A, Xu SS, Bai G, Nevo E, Gao C, Ohm H, Kong L. Horizontal gene transfer of Fhb7 from fungus underlies Fusarium head blight resistance in wheat. Science 2020; 368:science.aba5435. [PMID: 32273397 DOI: 10.1126/science.aba5435] [Citation(s) in RCA: 265] [Impact Index Per Article: 66.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 03/26/2020] [Indexed: 12/22/2022]
Abstract
Fusarium head blight (FHB), a fungal disease caused by Fusarium species that produce food toxins, currently devastates wheat production worldwide, yet few resistance resources have been discovered in wheat germplasm. Here, we cloned the FHB resistance gene Fhb7 by assembling the genome of Thinopyrum elongatum, a species used in wheat distant hybridization breeding. Fhb7 encodes a glutathione S-transferase (GST) and confers broad resistance to Fusarium species by detoxifying trichothecenes through de-epoxidation. Fhb7 GST homologs are absent in plants, and our evidence supports that Th. elongatum has gained Fhb7 through horizontal gene transfer (HGT) from an endophytic Epichloë species. Fhb7 introgressions in wheat confers resistance to both FHB and crown rot in diverse wheat backgrounds without yield penalty, providing a solution for Fusarium resistance breeding.
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Affiliation(s)
- Hongwei Wang
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China.
| | - Silong Sun
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Wenyang Ge
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Lanfei Zhao
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Bingqian Hou
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Kai Wang
- Novogene Bioinformatics Institute, Beijing 100083, PR China
| | - Zhongfan Lyu
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Liyang Chen
- Novogene Bioinformatics Institute, Beijing 100083, PR China
| | - Shoushen Xu
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Jun Guo
- Crop Research Institute, Shandong Academy of Agricultural Sciences, Jinan, Shandong 250100, PR China
| | - Min Li
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Peisen Su
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Xuefeng Li
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Guiping Wang
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Cunyao Bo
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Xiaojian Fang
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Wenwen Zhuang
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Xinxin Cheng
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Jianwen Wu
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Luhao Dong
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Wuying Chen
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Wen Li
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Guilian Xiao
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Jinxiao Zhao
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Yongchao Hao
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Ying Xu
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Yu Gao
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Wenjing Liu
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Yanhe Liu
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Huayan Yin
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Jiazhu Li
- College of Chemistry and Chemical Engineering, Yantai University, Yantai, Shandong 264005, PR China
| | - Xiang Li
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Yan Zhao
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Xiaoqian Wang
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Fei Ni
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Xin Ma
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Anfei Li
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China
| | - Steven S Xu
- USDA-ARS, Cereal Crops Research Unit, Edward T. Schafer Agricultural Research Center, Fargo, ND 58102, USA
| | - Guihua Bai
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, KS 66506, USA
| | - Eviatar Nevo
- Institute of Evolution, University of Haifa, Mount Carmel, Haifa 3498838, Israel
| | - Caixia Gao
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, PR China
| | - Herbert Ohm
- Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA
| | - Lingrang Kong
- State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong 271018, PR China.
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Li X, Wang G, Feng X, Lyu Z, Wei L, Chen S, Wu S, Dai M, Li N, He J. Metabolic syndrome and renal cell cancer risk in Chinese males: a population-based prospective study. Eur J Public Health 2019. [DOI: 10.1093/eurpub/ckz185.125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
Metabolic syndrome (MetS) is now a common public health problem. Few researches have reported the relationship between MetS and the risk of renal cell cancer (RCC). To investigate the association of metabolic syndrome and its components with the risk of RCC in Chinese males, the study was performed in the Kailuan male cohort, a large prospective cohort study.
Methods
A total of 104,333 eligible males enrolled in the every 2-year health checkup were involved in the Kailuan male cohort study (2006-2015). Information on demographic and socioeconomic characteristics, lifestyle, medical history and laboratory tests at baseline entry was obtained. Univariable and multivariable Cox proportional hazards regression models were used to estimate the association between MetS and the RCC risk.
Results
During a median follow-up of 8.9 years, 131 RCC cases were verified over a total of 824,211.96 person-years. Among the 5 single MetS components, hypertension (Systolic/diastolic blood pressure≥130/85 mm Hg or antihypertensive drug treatment of previously hypertension) (HR = 2.35, 95%CI:1.48-3.72) and elevated triglyceride (TG) (≥1.7mmol/L) (HR = 1.78, 95%CI:1.23-2.56) showed significant risk for RCC. Multivariate analysis showed that compared to those who did not meet MetS diagnostic criteria (number of abnormal MetS components<3), HR of RCC risk for participants with MetS was 1.95 (95% CI 1.35-2.83). The number of abnormal MetS components was linearly associated with an increased risk of RCC (P trend<0.001), and the HRs of RCC risk for males with 1, 2 and ≥3 MetS components were 1.27 (0.56-2.90), 2.42 (1.12-5.20) and 3.32 (1.56-7.07), respectively, compared with subjects without MetS components.
Conclusions
MetS was inversely associated with of RCC risk in males.
Key messages
MetS might be one of the scientific and important predictors of RCC. Controlling metabolic syndrome may potentially have key scientific and clinical significance for RCC prevention.
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Affiliation(s)
- X Li
- Office of Cancer Screening, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - G Wang
- Department of Oncology, Kailuan General Hospital, Tangshan, China
| | - X Feng
- Office of Cancer Screening, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Z Lyu
- Office of Cancer Screening, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - L Wei
- Office of Cancer Screening, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - S Chen
- Health Department of Kailuan (Group), Tangshan, China
| | - S Wu
- Health Department of Kailuan (Group), Tangshan, China
| | - M Dai
- Office of Cancer Screening, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - N Li
- Office of Cancer Screening, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - J He
- Office of Cancer Screening, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Feng X, Li N, Wang G, Chen S, Lyu Z, Wei L, Li X, Wen Y, Giovannucci E, Wu S, Dai M, He J. Development of a liver cancer risk prediction model for the general population in china: A potential tool for screening. Ann Oncol 2019. [DOI: 10.1093/annonc/mdz422.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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10
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Mao JB, Yu XT, Shen LJ, Wu MY, Lyu Z, Lao JM, Li HX, Wu HF, Chen YQ. [Risk factors of retinopathy of prematurity in extremely low birth weight infants by strictly controlling oxygen inhalation after birth]. Zhonghua Yan Ke Za Zhi 2019; 55:280-288. [PMID: 30982290 DOI: 10.3760/cma.j.issn.0412-4081.2019.04.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To observe the incidence and severity of retinopathy of prematurity (ROP) in extremely low birth weight (ELBW) infants by strictly controlling the risk factors of ROP, such as oxygen inhalation after birth, to explore the related factors of ROP in ELBW infants. Methods: This was a cross-sectional study. 166 ELBW infants underwent neonatal screening were enrolled in this study, whose birth weight was less than 1 000 g. There were 79 males and 87 females infants, whose average gestational age was (27.99±1.73)weeks, and average birth weight was (904.45±80.23)g. According to the final screening results, the ELBW infants were grouped as follows: (1)ROP group and non-ROP group; (2)severe ROP group and mild or no ROP group. Risk factors included gestational age, birth weight, test-tube infants, fetuses number, complications during pregnancy, delivery mode and Apgar scores in 1 to 10 minutes, weight and weight gain proportion at 1-6 weeks after birth, postnatal feeding mode, history of oxygen inhalation, anemia and blood transfusion, and other systemic diseases were recorded. And their correlation with severe ROP was analyzed by SPSS 20.0 statistical software. Results: Ninty-four (56.63%) ELBW infants developed ROP, 16 (9.64%) were severe ROP and 14(8.43%) received treatment. Average birth weight between ROP group (911.95±72.80)g and non-ROP group (894.67±88.58)g had no difference(t=1.379, P=0.170). Average gestational age between ROP group (27.49±1.53) weeks and non-ROP group (28.64±1.76) weeks had significant difference(t=-4.491,P<0.001).And pregnancy-induced hypertension during pregnancy (χ(2)=4.479, P=0.034), Apgar score in 5 minutes (t=-2.760, P=0.006) and 10 minutes (t=-2.099, P=0.043), pneumonia (χ(2)=6.233, P=0.013), neonatal pneumonia (χ(2)=18.026, P<0.001) had significant difference between ROP group and non-ROP group. There was no effect on weight (F=0.009,P=0.753) or weight gain proportion (F=2.394,P=0.124) at 1-6 weeks after birth in ELBW infants with or without ROP. Average birth weight between severe ROP group(875.63±74.85)g and mild or no ROP group(907.53±80.41)g had no difference(t=-1.518, P=0.131).Average gestational age between severe ROP group(26.88±1.31)weeks and mild or no ROP group (28.11±1.73)weeks had significant difference(t=-2.766,P=0.006).And only fundus hemorrhage (χ(2)=4.507,P=0.034) had significant difference between severe ROP group and mild or no ROP group. There was no effect on weight (F=2.683,P=0.103) or weight gain proportion (F=0.431,P=0.513) at 1-6 weeks after birth in ELBW infants with or without ROP. Logistic regression analysis revealed that only gestational age was correlated to the incidence (β=-0.437,P<0.001) and severity (β=-0.616,P=0.007) of ROP significantly. Conclusion: By strictly controlling the risk factors of ROP, such as oxygen inhalation after birth, the severe rate of ROP in ELBW infants is low. However, gestational age is still the inevitable independent high risk factor for the incidence of ROP in ELBW infants. (Chin J Ophthalmol, 2019, 55:280-288).
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Affiliation(s)
- J B Mao
- The Affiliated Eye Hospital of Whenzhou Medical University at HangZhou, HangZhou 310020, China
| | - X T Yu
- The Affiliated Eye Hospital of Whenzhou Medical University at HangZhou, HangZhou 310020, China
| | - L J Shen
- The Affiliated Eye Hospital of Whenzhou Medical University at HangZhou, HangZhou 310020, China
| | - M Y Wu
- The Affiliated Obstetrics and Gynecology Hospital of Medical College of Zhejiang University, HangZhou 310006, China
| | - Z Lyu
- The Affiliated Eye Hospital of Whenzhou Medical University at HangZhou, HangZhou 310020, China
| | - J M Lao
- The Affiliated Eye Hospital of Whenzhou Medical University at HangZhou, HangZhou 310020, China
| | - H X Li
- The Affiliated Eye Hospital of Whenzhou Medical University at HangZhou, HangZhou 310020, China
| | - H F Wu
- The Affiliated Eye Hospital of Whenzhou Medical University at HangZhou, HangZhou 310020, China
| | - Y Q Chen
- The Affiliated Eye Hospital of Whenzhou Medical University at HangZhou, HangZhou 310020, China
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Wu MA, Wu MY, Wu SJ, Zhu JJ, Lyu Z, Li CL, Shen LJ. [Analysis of corneal and conjunctival sensitivities and its related factors of premature babies]. Zhonghua Yan Ke Za Zhi 2018; 54:115-119. [PMID: 29429296 DOI: 10.3760/cma.j.issn.0412-4081.2018.02.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To analyse the corneal and conjunctival sensitivities of premature babies and to study the relevant influencing factors. Methods: Cross-sectional study. One hundred premature infants born at Women's Hospital School of Medicine Zhejiang University between May 2015 and September 2015 were enrolled, among which 51 were male (51%) and 49 were female (49%), the mean gestational age was (30.93±1.75)w, the mean corrected gestational age was (33.65±1.53)w, the mean birth weight was (1 592±336)g. The thresholds of cornea and conjunctiva of infants' left or right eyes were measured with Cochet-Bonnet aesthesiometer at 8-10 o'clock every morning when they naturally woke up, the minimum length of nylon wire that induced three successive times of eye-blink responses was recorded. Paired sample t test was used to compare the corneal and conjunctival sensitivities, the ocular surface sensitivities of preterm infants of different gender were compared using independent samples t-test, Pearson correlation and multiple linear regression analysis was conducted to analyze the correlation of corneal and conjuncitval sensitivities with gestational age, birth weight, age and corrected gestational age. Results: The mean corneal sensitivity was (44.85±5.53) mm and the mean conjunctival sensitivity was (23.50±5.48)mm in premature babies, corneal sensitivity was significantly higher than conjunctival sensitivity (t=25.620, P<0.001). No statistical significance was found between male and female preterm infants in corneal sensitivity [(44.80±5.83) mm vs. (44.90±5.25) mm, t=-0.085, P=0.933] and conjunctival sensitivity[(23.14±5.83) mm vs. (23.88±5.13) mm, t=-0.673, P=0.502]. Pearson correlation analysis showed that corneal sensitivity was significantly associated with conjunctival sensitivity in prematurity(r=0.676, P<0.001). There was significant correlation between corneal sensitivity and age, corrected gestational age (r=0.238, P=0.017; r=0.679, P<0.001), however no significant correlation was found between corneal sensitivity and gestational age, birth weight in preterm infants (r=0.067, P=0.510; r=-0.179, P=0.075). There was significant correlation between conjunctival sensitivity and corrected gestational age (r=0.490, P<0.001), however no significant correlation was found between conjunctival sensitivity and gestational age, birth weight and age in preterm infants (r=0.078, P=0.439; r=-0.096, P=0.344; r=0.151, P=0.133). Multiple linear regression revealed that corneal sensitivity(Y1) was positively correlated with corrected gestational age(X), the regression equation was Y1=2.45X-37.52, the conjunctical sensitivity(Y2) was also positively correlated with corrected gestational age(X), the regression equation was Y2=1.75X-35.41. Conclusions: The corneal sensitivity is higher than conjunctival sensitivity in premature babies.No statistical significance is found between male and female preterm infants in corneal sensitivity and conjunctival sensitivity. The corneal sensitivity and conjunctival sensitivity are correlated with corrected gestational age in preterm infants. The corneal and conjunctival sensitivities of premature babies tend to increase along with the increase of corrected gestational age. (Chin J Ophthalmol, 2018, 54: 115-119).
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Affiliation(s)
- M A Wu
- Eye Hospital of Wenzhou Medical University, Wenzhou 325027, China
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Li ZY, Lyu Z. [Discussion on the related problems of pediatric burn treatment]. Zhonghua Shao Shang Za Zhi 2017; 33:401-403. [PMID: 28763904 DOI: 10.3760/cma.j.issn.1009-2587.2017.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The incidence of pediatric burn is high currently. Many clinical problems in the treatment of pediatric burn are composed of fluid replacement during shock stage, wound treatment, nutrition and metabolism etc, which urgently need to be sorted out and updated again to make corresponding clinical guidelines, criteria, or consensus for standardizing the clinical diagnosis and treatment, so as to improve the clinical treatment level of pediatric burn.
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Affiliation(s)
- Z Y Li
- Department of Burns, the Fifth Hospital of Harbin, Harbin 150040, China
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