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Yu W, Yin G, Chen S, Zhang X, Meng D, Wang L, Liu H, Jiang W, Sun Y, Zhang F. Diosgenin attenuates metabolic-associated fatty liver disease through the hepatic NLRP3 inflammasome-dependent signaling pathway. Int Immunopharmacol 2024; 138:112581. [PMID: 38944952 DOI: 10.1016/j.intimp.2024.112581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Revised: 06/05/2024] [Accepted: 06/25/2024] [Indexed: 07/02/2024]
Abstract
Metabolic-associated fatty liver disease (MAFLD) is one of the most common liver diseases worldwide; however, its pathogenesis and treatment methods have not been perfected. NOD-like receptor thermal protein domain-associated protein 3 (NLRP3) is a promising therapeutic target for MAFLD. Diosgenin (DG) is a natural compound that was identified in a traditional Chinese herbal medicine, which has pharmacological effects, such as anti-inflammatory, antioxidant, hepatoprotective, and hypolipidemic activities. In this study, we examined the effects and molecular mechanisms of DG on MAFLD in vitro and in vivo. We established a rat model by administering a high-fat diet (HFD). We also generated an in vitro MAFLD model by treating HepG2 cells with free fatty acids (FFAs). The results indicated that DG attenuated lipid accumulation and liver injury in both in vitro and in vivo models. DG downregulated the expression of NLRP3, apoptosis-associated speckle-like protein (ASC), cysteinyl aspartate specific proteinase-1 (caspase-1), gasdermin D (GSDMD), GSDMD-n, and interleukin-1β (IL-1β). In addition, we silenced and overexpressed NLRP3 in vitro to determine the effects of DG on antiMAFLD. Silencing NLRP3 enhanced the effect of DG on the treatment of MAFLD, whereas NLRP3 overexpression reversed its beneficial effects. Taken together, the results show that DG has a favorable effect on attenuating MAFLD through the hepatic NLRP3 inflammasome-dependent signaling pathway. DG represents a natural NLRP3 inhibitor for the MAFLD treatment.
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Affiliation(s)
- Wenfei Yu
- Shandong University of Traditional Chinese Medicine, Jinan 250013, People's Republic of China
| | - Guoliang Yin
- Shandong University of Traditional Chinese Medicine, Jinan 250013, People's Republic of China
| | - Suwen Chen
- Shandong University of Traditional Chinese Medicine, Jinan 250013, People's Republic of China
| | - Xin Zhang
- Shandong University of Traditional Chinese Medicine, Jinan 250013, People's Republic of China
| | - Decheng Meng
- Shandong University of Traditional Chinese Medicine, Jinan 250013, People's Republic of China
| | - Linya Wang
- Shandong University of Traditional Chinese Medicine, Jinan 250013, People's Republic of China
| | - Hongshuai Liu
- Shandong University of Traditional Chinese Medicine, Jinan 250013, People's Republic of China
| | - Wenying Jiang
- Shandong University of Traditional Chinese Medicine, Jinan 250013, People's Republic of China
| | - Yuqing Sun
- Shandong University of Traditional Chinese Medicine, Jinan 250013, People's Republic of China
| | - Fengxia Zhang
- Department of Neurology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250011, People's Republic of China.
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Andersson D, Kebede FT, Escobar M, Österlund T, Ståhlberg A. Principles of digital sequencing using unique molecular identifiers. Mol Aspects Med 2024; 96:101253. [PMID: 38367531 DOI: 10.1016/j.mam.2024.101253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 01/26/2024] [Accepted: 02/03/2024] [Indexed: 02/19/2024]
Abstract
Massively parallel sequencing technologies have long been used in both basic research and clinical routine. The recent introduction of digital sequencing has made previously challenging applications possible by significantly improving sensitivity and specificity to now allow detection of rare sequence variants, even at single molecule level. Digital sequencing utilizes unique molecular identifiers (UMIs) to minimize sequencing-induced errors and quantification biases. Here, we discuss the principles of UMIs and how they are used in digital sequencing. We outline the properties of different UMI types and the consequences of various UMI approaches in relation to experimental protocols and bioinformatics. Finally, we describe how digital sequencing can be applied in specific research fields, focusing on cancer management where it can be used in screening of asymptomatic individuals, diagnosis, treatment prediction, prognostication, monitoring treatment efficacy and early detection of treatment resistance as well as relapse.
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Affiliation(s)
- Daniel Andersson
- Sahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, 413 90, Gothenburg, Sweden
| | - Firaol Tamiru Kebede
- Sahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, 413 90, Gothenburg, Sweden
| | - Mandy Escobar
- Sahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, 413 90, Gothenburg, Sweden
| | - Tobias Österlund
- Sahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, 413 90, Gothenburg, Sweden; Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, 413 90, Gothenburg, Sweden; Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden
| | - Anders Ståhlberg
- Sahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, 413 90, Gothenburg, Sweden; Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, 413 90, Gothenburg, Sweden; Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden.
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3
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Wang J, Gao Y, Wang F, Zeng S, Li J, Miao H, Wang T, Zeng J, Baptista-Hon D, Monteiro O, Guan T, Cheng L, Lu Y, Luo Z, Li M, Zhu JK, Nie S, Zhang K, Zhou Y. Accurate estimation of biological age and its application in disease prediction using a multimodal image Transformer system. Proc Natl Acad Sci U S A 2024; 121:e2308812120. [PMID: 38190540 PMCID: PMC10801873 DOI: 10.1073/pnas.2308812120] [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: 06/09/2023] [Accepted: 10/12/2023] [Indexed: 01/10/2024] Open
Abstract
Aging in an individual refers to the temporal change, mostly decline, in the body's ability to meet physiological demands. Biological age (BA) is a biomarker of chronological aging and can be used to stratify populations to predict certain age-related chronic diseases. BA can be predicted from biomedical features such as brain MRI, retinal, or facial images, but the inherent heterogeneity in the aging process limits the usefulness of BA predicted from individual body systems. In this paper, we developed a multimodal Transformer-based architecture with cross-attention which was able to combine facial, tongue, and retinal images to estimate BA. We trained our model using facial, tongue, and retinal images from 11,223 healthy subjects and demonstrated that using a fusion of the three image modalities achieved the most accurate BA predictions. We validated our approach on a test population of 2,840 individuals with six chronic diseases and obtained significant difference between chronological age and BA (AgeDiff) than that of healthy subjects. We showed that AgeDiff has the potential to be utilized as a standalone biomarker or conjunctively alongside other known factors for risk stratification and progression prediction of chronic diseases. Our results therefore highlight the feasibility of using multimodal images to estimate and interrogate the aging process.
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Affiliation(s)
- Jinzhuo Wang
- Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Beijing100871, China
| | - Yuanxu Gao
- Macau Institute for AI in Medicine and Zhuhai People’s Hospital and the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology, Macau999087, China
| | - Fangfei Wang
- Macau Institute for AI in Medicine and Zhuhai People’s Hospital and the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology, Macau999087, China
- Guangzhou National Laboratory, Guangzhou510005, China
| | - Simiao Zeng
- Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou510623, China
| | - Jiahui Li
- Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou510623, China
| | - Hanpei Miao
- Dongguan People’s Hospital, Southern Medical University, Dongguan523059, China
| | - Taorui Wang
- Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou510623, China
| | - Jin Zeng
- Guangzhou National Laboratory, Guangzhou510005, China
| | - Daniel Baptista-Hon
- Macau Institute for AI in Medicine and Zhuhai People’s Hospital and the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology, Macau999087, China
| | - Olivia Monteiro
- Macau Institute for AI in Medicine and Zhuhai People’s Hospital and the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology, Macau999087, China
| | - Taihua Guan
- Guangzhou National Laboratory, Guangzhou510005, China
| | - Linling Cheng
- Macau Institute for AI in Medicine and Zhuhai People’s Hospital and the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology, Macau999087, China
| | - Yuxing Lu
- Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Beijing100871, China
| | - Zhengchao Luo
- Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Beijing100871, China
| | - Ming Li
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou325027, China
| | - Jian-kang Zhu
- Institute of Advanced Biotechnology and School of Life Sciences, Southern University of Science and Technology, Shenzhen518055, China
| | - Sheng Nie
- National Clinical Research Center for Kidney Diseases, State Key Laboratory for Organ Failure Research, Nanfang Hospital, Southern Medical University, Guangzhou510515, China
| | - Kang Zhang
- Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Beijing100871, China
- Macau Institute for AI in Medicine and Zhuhai People’s Hospital and the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology, Macau999087, China
- Guangzhou National Laboratory, Guangzhou510005, China
- Dongguan People’s Hospital, Southern Medical University, Dongguan523059, China
| | - Yong Zhou
- Clinical Research Institute, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai201620, China
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Niu C, Tu Y, Jin Q, Chen Z, Yuan K, Wang M, Zhang P, Luo J, Li H, Yang Y, Liu X, Mao M, Dong T, Tan W, Hu X, Pan Y, Hou L, Ma R, Huang Z. Mapping the human oral and gut fungal microbiota in patients with metabolic dysfunction-associated fatty liver disease. Front Cell Infect Microbiol 2023; 13:1157368. [PMID: 37180439 PMCID: PMC10170973 DOI: 10.3389/fcimb.2023.1157368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 03/24/2023] [Indexed: 05/16/2023] Open
Abstract
Metabolic dysfunction-associated fatty liver disease (MAFLD) is a phenotype of liver diseases associated with metabolic syndrome. The pathogenesis MAFLD remains unclear. The liver maintains is located near the intestine and is physiologically interdependent with the intestine via metabolic exchange and microbial transmission, underpinning the recently proposed "oral-gut-liver axis" concept. However, little is known about the roles of commensal fungi in the disease development. This study aimed to characterize the alterations of oral and gut mycobiota and their roles in MAFLD. Twenty-one MAFLD participants and 20 healthy controls were enrolled. Metagenomics analyses of saliva, supragingival plaques, and feces revealed significant alterations in the gut fungal composition of MAFLD patients. Although no statistical difference was evident in the oral mycobiome diversity within MAFLD and healthy group, significantly decreased diversities were observed in fecal samples of MAFLD patients. The relative abundance of one salivary species, five supragingival species, and seven fecal species was significantly altered in MAFLD patients. Twenty-two salivary, 23 supragingival, and 22 fecal species were associated with clinical parameters. Concerning the different functions of fungal species, pathways involved in metabolic pathways, biosynthesis of secondary metabolites, microbial metabolism in diverse environments, and carbon metabolism were abundant both in the oral and gut mycobiomes. Moreover, different fungal contributions in core functions were observed between MAFLD patients and the healthy controls, especially in the supragingival plaque and fecal samples. Finally, correlation analysis between oral/gut mycobiome and clinical parameters identified correlations of certain fungal species in both oral and gut niches. Particularly, Mucor ambiguus, which was abundant both in saliva and feces, was positively correlated with body mass index, total cholesterol, low-density lipoprotein, alanine aminotransferase, and aspartate aminotransferase, providing evidence of a possible "oral-gut-liver" axis. The findings illustrate the potential correlation between core mycobiome and the development of MAFLD and could propose potential therapeutic strategies.
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Affiliation(s)
- Chenguang Niu
- Department of Endodontics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- National Center for Stomatology, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Ye Tu
- Department of Endodontics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- National Center for Stomatology, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Qiaoqiao Jin
- Department of Endodontics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- National Center for Stomatology, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Zhanyi Chen
- Department of Endodontics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- National Center for Stomatology, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Keyong Yuan
- Department of Endodontics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- National Center for Stomatology, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Min Wang
- Institute of Stomatology, School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, China
- Department of Endodontics, School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, China
| | - Pengfei Zhang
- Department of Endodontics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- National Center for Stomatology, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Junyuan Luo
- Department of Endodontics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- National Center for Stomatology, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Hao Li
- Department of Endodontics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- National Center for Stomatology, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Yueyi Yang
- Department of Endodontics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- National Center for Stomatology, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Xiaoyu Liu
- Department of Endodontics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- National Center for Stomatology, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Mengying Mao
- Department of Endodontics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- National Center for Stomatology, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Ting Dong
- Department of Endodontics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- National Center for Stomatology, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Wenduo Tan
- Department of Endodontics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- National Center for Stomatology, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Xuchen Hu
- Department of Endodontics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- National Center for Stomatology, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Yihuai Pan
- Institute of Stomatology, School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, China
- Department of Endodontics, School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, China
| | - Lili Hou
- Department of Nursing, Shanghai Ninth People’s Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Rui Ma
- Department of Endodontics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- National Center for Stomatology, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
- *Correspondence: Zhengwei Huang, ; Rui Ma,
| | - Zhengwei Huang
- Department of Endodontics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- National Center for Stomatology, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
- *Correspondence: Zhengwei Huang, ; Rui Ma,
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