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Liu M, Yao X, Wang H, Xu X, Kong J, Wang Y, Chen W, Bai H, Wang Z, Setati ME, Crauwels S, Blancquaert E, Fan P, Liang Z, Dai Z. Carposphere microbiota alters grape volatiles and shapes the wine grape typicality. THE NEW PHYTOLOGIST 2025; 246:2280-2294. [PMID: 40247820 DOI: 10.1111/nph.70152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2024] [Accepted: 03/24/2025] [Indexed: 04/19/2025]
Abstract
While specific environments are known to shape plant metabolomes and the makeup of their associated microbiome, it is as yet unclear whether carposphere microbiota contribute to the characteristics of grape fruit flavor of a particular wine region. Here, carposphere microbiomes and berry transcriptomes and metabolomes of three grape cultivars growing at six geographic sites were analyzed. The composition of the carposphere microbiome was determined mainly by environmental conditions, rather than grape genotype. Bacterial microbiota likely contributed to grape volatile profiles. Particularly, candidate operational taxonomic units (OTUs) in genus Sphingomonas were highly correlated with grape C6 aldehyde volatiles (also called green leaf volatiles, GLVs), which contribute to a fresh taste. Furthermore, a core set of expressed genes was enriched in lipid metabolism, which is responsible for bacterial colonization and C6 aldehyde volatile synthesis activation. Finally, a similar grape volatile profile was observed after inoculating the berry skin of two grape cultivars with Sphingomonas sp., thus providing evidence for the hypothetical microbe-metabolite relationship. These results provide novel insight into how the environment-microbiome-plant quality (E × Mi × Q) interaction may shape berry flavor and thereby typicality, serving as a foundation for decision-making in vineyard microbial management.
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Affiliation(s)
- Menglong Liu
- State Key Laboratory of Plant Diversity and Specialty Crops, Beijing Key Laboratory of Grape Sciences and Enology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- China National Botanical Garden, Beijing, 100093, China
| | - Xuenan Yao
- State Key Laboratory of Plant Diversity and Specialty Crops, Beijing Key Laboratory of Grape Sciences and Enology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- China National Botanical Garden, Beijing, 100093, China
| | - Haiqi Wang
- State Key Laboratory of Plant Diversity and Specialty Crops, Beijing Key Laboratory of Grape Sciences and Enology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- China National Botanical Garden, Beijing, 100093, China
| | - Xiaobo Xu
- State Key Laboratory of Plant Diversity and Specialty Crops, Beijing Key Laboratory of Grape Sciences and Enology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- China National Botanical Garden, Beijing, 100093, China
| | - Junhua Kong
- State Key Laboratory of Plant Diversity and Specialty Crops, Beijing Key Laboratory of Grape Sciences and Enology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- China National Botanical Garden, Beijing, 100093, China
| | - Yongjian Wang
- State Key Laboratory of Plant Diversity and Specialty Crops, Beijing Key Laboratory of Grape Sciences and Enology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- China National Botanical Garden, Beijing, 100093, China
| | - Weiping Chen
- Horticultural Research Institute, Ningxia Academy of Agriculture and Forestry Sciences, Ningxia, 750002, China
| | - Huiqing Bai
- Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Zixuan Wang
- University of Chinese Academy of Sciences, Beijing, 100049, China
- China National Botanical Garden, Beijing, 100093, China
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Mathabatha Evodia Setati
- South African Grape and Wine Research Institute (SAGWRI), Stellenbosch University, Private Bag X1, Matieland, 7600, South Africa
| | - Sam Crauwels
- Centre of Microbial and Plant Genetics (CMPG), Laboratory for Process Microbial Ecology and Bioinspirational Management (PME&BIM), Department of Microbial and Molecular Systems (M2S), KU Leuven, Leuven, 3001, Belgium
| | - Erna Blancquaert
- South African Grape and Wine Research Institute (SAGWRI), Stellenbosch University, Private Bag X1, Matieland, 7600, South Africa
| | - Peige Fan
- State Key Laboratory of Plant Diversity and Specialty Crops, Beijing Key Laboratory of Grape Sciences and Enology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- China National Botanical Garden, Beijing, 100093, China
| | - Zhenchang Liang
- State Key Laboratory of Plant Diversity and Specialty Crops, Beijing Key Laboratory of Grape Sciences and Enology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- China National Botanical Garden, Beijing, 100093, China
| | - Zhanwu Dai
- State Key Laboratory of Plant Diversity and Specialty Crops, Beijing Key Laboratory of Grape Sciences and Enology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- China National Botanical Garden, Beijing, 100093, China
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Lin W, Yang Y, Zhu Y, Pan R, Liu C, Pan J. Linking Gut Microbiota, Oral Microbiota, and Serum Metabolites in Insomnia Disorder: A Preliminary Study. Nat Sci Sleep 2024; 16:1959-1972. [PMID: 39664229 PMCID: PMC11633293 DOI: 10.2147/nss.s472675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2024] [Accepted: 11/19/2024] [Indexed: 12/13/2024] Open
Abstract
Purpose Despite recent findings suggesting an altered gut microbiota in those suffering from insomnia disorder (ID), research into the gut microbiota, oral microbiota, serum metabolites, and their interactions in patients with ID is sparse. Patients and Methods We collected a total of 114 fecal samples, 133 oral cavity samples and 20 serum samples to characterize the gut microbiota, oral microbiota and serum metabolites in a cohort of 76 ID patients (IDs) and 59 well-matched healthy controls (HCs). We assessed the microbiota as potentially biomarkers for ID for ID by 16S rDNA sequencing and elucidated the interactions involving gut microbiota, oral microbiota and serum metabolites in ID in conjunction with untargeted metabolomics. Results Gut and oral microbiota of IDs were dysbiotic. Gut and oral microbial biomarkers could be used to differentiate IDs from HCs. Eleven significantly altered serum metabolites, including adenosine, phenol, and phenol sulfate, differed significantly between groups. In multi-omics analyses, adenosine showed a positive correlation with genus_Lachnospira (p=0.029) and total sleep time (p=0.016). Additionally, phenol and phenol sulphate had a negative correlation with genus_Coprococcus (p=0.0059; p=0.0059) and a positive correlation with Pittsburgh Sleep Quality Index (p=0.006; p=0.006) and Insomnia Severity Index (p=0.021; p=0.021). Conclusion Microbiota and serum metabolite changes in IDs are strongly correlated with clinical parameters, implying mechanistic links between altered bacteria, serum metabolites and ID. This study offers novel perspective into the interaction among gut microbiota, oral microbiota, and serum metabolites for ID.
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Affiliation(s)
- Weifeng Lin
- Department of Neurology, The Tenth Affiliated Hospital, Southern Medical University (Dongguan People’s Hospital), Dongguan, Guangdong, 523000, People’s Republic of China
- Department of Psychiatry, Sleep Medicine Center, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, 510632, People’s Republic of China
| | - Yifan Yang
- Sleep Medicine Center, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, Guangdong, 510120, People’s Republic of China
| | - Yurong Zhu
- Department of Pathology, The Tenth Affiliated Hospital, Southern Medical University (Dongguan People’s Hospital), Dongguan, Guangdong, 523000, People’s Republic of China
| | - Rong Pan
- Department of Psychology, The Third People’s Hospital of Zhaoqing, Zhaoqing, Guangdong Province, 526060, People’s Republic of China
| | - Chaonan Liu
- Department of Psychiatry, Sleep Medicine Center, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, 510632, People’s Republic of China
| | - Jiyang Pan
- Department of Psychiatry, Sleep Medicine Center, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, 510632, People’s Republic of China
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Sun T, Sun D, Kuang J, Chao X, Guo Y, Li M, Chen T. A cross-omics data analysis strategy for metabolite-microbe pair identification. Proteomics 2024; 24:e2400035. [PMID: 38994817 DOI: 10.1002/pmic.202400035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 06/22/2024] [Accepted: 07/02/2024] [Indexed: 07/13/2024]
Abstract
Given the pivotal roles of metabolomics and microbiomics, numerous data mining approaches aim to uncover their intricate connections. However, the complex many-to-many associations between metabolome-microbiome profiles yield numerous statistically significant but biologically unvalidated candidates. To address these challenges, we introduce BiOFI, a strategic framework for identifying metabolome-microbiome correlation pairs (Bi-Omics). BiOFI employs a comprehensive scoring system, incorporating intergroup differences, effects on feature correlation networks, and organism abundance. Meanwhile, it establishes a built-in database of metabolite-microbe-KEGG functional pathway linking relationships. Furthermore, BiOFI can rank related feature pairs by combining importance scores and correlation strength. Validation on a dataset of cesarean-section infants confirms the strategy's validity and interpretability. The BiOFI R package is freely accessible at https://github.com/chentianlu/BiOFI.
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Affiliation(s)
- Tao Sun
- Center for Translational Medicine, The Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dongnan Sun
- Center for Translational Medicine, The Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Junliang Kuang
- Center for Translational Medicine, The Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaowen Chao
- Center for Translational Medicine, The Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yihan Guo
- School of Medicine, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Mengci Li
- Center for Translational Medicine, The Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tianlu Chen
- Center for Translational Medicine, The Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Liu C, Du MX, Xie LS, Wang WZ, Chen BS, Yun CY, Sun XW, Luo X, Jiang Y, Wang K, Jiang MZ, Qiao SS, Sun M, Cui BJ, Huang HJ, Qu SP, Li CK, Wu D, Wang LS, Jiang C, Liu HW, Liu SJ. Gut commensal Christensenella minuta modulates host metabolism via acylated secondary bile acids. Nat Microbiol 2024; 9:434-450. [PMID: 38233647 DOI: 10.1038/s41564-023-01570-0] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 11/29/2023] [Indexed: 01/19/2024]
Abstract
A strong correlation between gut microbes and host health has been observed in numerous gut metagenomic cohort studies. However, the underlying mechanisms governing host-microbe interactions in the gut remain largely unknown. Here we report that the gut commensal Christensenella minuta modulates host metabolism by generating a previously undescribed class of secondary bile acids with 3-O-acylation substitution that inhibit the intestinal farnesoid X receptor. Administration of C. minuta alleviated features of metabolic disease in high fat diet-induced obese mice associated with a significant increase in these acylated bile acids, which we refer to as 3-O-acyl-cholic acids. Specific knockout of intestinal farnesoid X receptor in mice counteracted the beneficial effects observed in their wild-type counterparts. Finally, we showed that 3-O-acyl-CAs were prevalent in healthy humans but significantly depleted in patients with type 2 diabetes. Our findings indicate a role for C. minuta and acylated bile acids in metabolic diseases.
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Affiliation(s)
- Chang Liu
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, P. R. China
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, P. R. China
| | - Meng-Xuan Du
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, P. R. China
| | - Li-Sheng Xie
- College of Life Science, Hebei University, Baoding, P. R. China
| | - Wen-Zhao Wang
- State Key Laboratory of Mycology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, P. R. China
| | - Bao-Song Chen
- State Key Laboratory of Mycology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, P. R. China
| | - Chu-Yu Yun
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, P. R. China
| | - Xin-Wei Sun
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, P. R. China
| | - Xi Luo
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, P. R. China
| | - Yu Jiang
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, P. R. China
| | - Kai Wang
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, P. R. China
| | - Min-Zhi Jiang
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, P. R. China
| | - Shan-Shan Qiao
- State Key Laboratory of Mycology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, P. R. China
| | - Min Sun
- The Second Hospital of Shandong University, Jinan, P. R. China
| | - Bao-Juan Cui
- The Second Hospital of Shandong University, Jinan, P. R. China
| | - Hao-Jie Huang
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, P. R. China
| | | | | | - Dalei Wu
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, P. R. China
| | - Lu-Shan Wang
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, P. R. China
| | - Changtao Jiang
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, P. R. China.
- Center of Basic Medical Research, Institute of Medical Innovation and Research, Third Hospital, Peking University, Beijing, P. R. China.
| | - Hong-Wei Liu
- State Key Laboratory of Mycology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, P. R. China.
| | - Shuang-Jiang Liu
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, P. R. China.
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, P. R. China.
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Accurate Prediction of Anti-hypertensive Peptides Based on Convolutional Neural Network and Gated Recurrent unit. Interdiscip Sci 2022; 14:879-894. [PMID: 35474167 DOI: 10.1007/s12539-022-00521-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 03/30/2022] [Accepted: 04/06/2022] [Indexed: 12/30/2022]
Abstract
Hypertension (HT) is a general disease, and also one of the most ordinary and major causes of cardiovascular disease. Some diseases are caused by high blood pressure, including impairment of heart and kidney function, cerebral hemorrhage and myocardial infarction. Due to the limitations of laboratory methods, bioactive peptides for the treatment of HT need a long time to be identified. Therefore, it is of great immediate significance for the identification of anti-hypertensive peptides (AHTPs). With the prevalence of machine learning, it is suggested to use it as a supplementary method for AHTPs classification. Therefore, we develop a new model to identify AHTPs based on multiple features and deep learning. And the deep model is constructed by combining a convolutional neural network (CNN) and a gated recurrent unit (GRU). The unique convolution structure is used to reduce the feature dimension and running time. The data processed by CNN is input into the recurrent structure GRU, and important information is filtered out through the reset gate and update gate. Finally, the output layer adopts Sigmoid activation function. Firstly, we use Kmer, the deviation between the dipeptide frequency and the expected mean (DDE), encoding based on grouped weight (EBGW), enhanced grouped amino acid composition (EGAAC) and dipeptide binary profile and frequency (DBPF) to extract features. For Kmer, DDE, EBGW and EGAAC, it is widely used in the field of protein research. DBPF is a new feature representation method designed by us. It corresponds dipeptides to binary numbers, and finally obtains a binary coding file and a frequency file. Then these features are spliced together and input into our proposed model for prediction and analysis. After a tenfold cross-validation test, this model has a better competitive advantage than the previous methods, and the accuracy is 96.23% and 99.10%, respectively. From the results, compared with the previous methods, it has been greatly improved. It shows that the combination of convolution calculation and recurrent structure has a positive impact on the classification of AHTPs. The results show that this method is a feasible, efficient and competitive sequence analysis tool for AHTPs. Meanwhile, we design a friendly online prediction tool and it is freely accessible at http://ahtps.zhanglab.site/ .
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Luo Z, Ma L, Zhou T, Huang Y, Zhang L, Du Z, Yong K, Yao X, Shen L, Yu S, Shi X, Cao S. Beta-Glucan Alters Gut Microbiota and Plasma Metabolites in Pre-Weaning Dairy Calves. Metabolites 2022; 12:687. [PMID: 35893252 PMCID: PMC9332571 DOI: 10.3390/metabo12080687] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 07/21/2022] [Accepted: 07/22/2022] [Indexed: 02/01/2023] Open
Abstract
The present study aims to evaluate the alterations in gut microbiome and plasma metabolites of dairy calves with β-glucan (BG) supplementation. Fourteen healthy newborn dairy calves with similar body weight were randomly divided into control (n = 7) and BG (n = 7) groups. All the calves were fed on the basal diet, while calves in the BG group were supplemented with oat BG on d 8 for 14 days. Serum markers, fecal microbiome, and plasma metabolites at d 21 were analyzed. The calves were weaned on d 60 and weighed. The mean weaning weight of the BG group was 4.29 kg heavier than that of the control group. Compared with the control group, the levels of serum globulin, albumin, and superoxide dismutase were increased in the BG group. Oat BG intake increased the gut microbiota richness and decreased the Firmicutes-to-Bacteroidetes ratio. Changes in serum markers were found to be correlated with the plasma metabolites, including sphingosine, trehalose, and 3-methoxy-4-hydroxyphenylglycol sulfate, and gut microbiota such as Ruminococcaceae_NK4A214, Alistipes, and Bacteroides. Overall, these results suggest that the BG promotes growth and health of pre-weaning dairy calves by affecting the interaction between the host and gut microbiota.
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Affiliation(s)
- Zhengzhong Luo
- Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, Chengdu University, Chengdu 610106, China;
- Department of Clinical Veterinary Medicine, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, China; (L.M.); (T.Z.); (L.Z.); (Z.D.); (X.Y.); (L.S.); (S.Y.)
| | - Li Ma
- Department of Clinical Veterinary Medicine, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, China; (L.M.); (T.Z.); (L.Z.); (Z.D.); (X.Y.); (L.S.); (S.Y.)
| | - Tao Zhou
- Department of Clinical Veterinary Medicine, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, China; (L.M.); (T.Z.); (L.Z.); (Z.D.); (X.Y.); (L.S.); (S.Y.)
| | - Yixin Huang
- Institute of Biodiversity, Animal Health & Comparative Medicine, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow G61 1QH, UK;
| | - Liben Zhang
- Department of Clinical Veterinary Medicine, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, China; (L.M.); (T.Z.); (L.Z.); (Z.D.); (X.Y.); (L.S.); (S.Y.)
| | - Zhenlong Du
- Department of Clinical Veterinary Medicine, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, China; (L.M.); (T.Z.); (L.Z.); (Z.D.); (X.Y.); (L.S.); (S.Y.)
| | - Kang Yong
- Department of Animal Science and Technology, Chongqing Three Gorges Vocational College, Chongqing 404100, China;
| | - Xueping Yao
- Department of Clinical Veterinary Medicine, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, China; (L.M.); (T.Z.); (L.Z.); (Z.D.); (X.Y.); (L.S.); (S.Y.)
| | - Liuhong Shen
- Department of Clinical Veterinary Medicine, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, China; (L.M.); (T.Z.); (L.Z.); (Z.D.); (X.Y.); (L.S.); (S.Y.)
| | - Shumin Yu
- Department of Clinical Veterinary Medicine, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, China; (L.M.); (T.Z.); (L.Z.); (Z.D.); (X.Y.); (L.S.); (S.Y.)
| | - Xiaodong Shi
- Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, Chengdu University, Chengdu 610106, China;
| | - Suizhong Cao
- Department of Clinical Veterinary Medicine, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, China; (L.M.); (T.Z.); (L.Z.); (Z.D.); (X.Y.); (L.S.); (S.Y.)
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