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Yamamoto Y, Shirai Y, Sonehara K, Namba S, Ojima T, Yamamoto K, Edahiro R, Suzuki K, Kanai A, Oda Y, Suzuki Y, Morisaki T, Narita A, Takeda Y, Tamiya G, Yamamoto M, Matsuda K, Kumanogoh A, Yamauchi T, Kadowaki T, Okada Y. Dissecting cross-population polygenic heterogeneity across respiratory and cardiometabolic diseases. Nat Commun 2025; 16:3765. [PMID: 40295474 PMCID: PMC12037804 DOI: 10.1038/s41467-025-58149-y] [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: 01/28/2024] [Accepted: 03/11/2025] [Indexed: 04/30/2025] Open
Abstract
Biological mechanisms underlying multimorbidity remain elusive. To dissect the polygenic heterogeneity of multimorbidity in twelve complex traits across populations, we leveraged biobank resources of genome-wide association studies (GWAS) for 232,987 East Asian individuals (the 1st and 2nd cohorts of BioBank Japan) and 751,051 European individuals (UK Biobank and FinnGen). Cross-trait analyses of respiratory and cardiometabolic diseases, rheumatoid arthritis, and smoking identified negative genetic correlations between respiratory and cardiometabolic diseases in East Asian individuals, opposite from the positive associations in European individuals. Associating genome-wide polygenic risk scores (PRS) with 325 blood metabolome and 2917 proteome biomarkers supported the negative cross-trait genetic correlations in East Asian individuals. Bayesian pathway PRS analysis revealed a negative association between asthma and dyslipidemia in a gene set of peroxisome proliferator-activated receptors. The pathway suggested heterogeneity of cell type specificity in the enrichment analysis of the lung single-cell RNA-sequencing dataset. Our study highlights the heterogeneous pleiotropy of immunometabolic dysfunction in multimorbidity.
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Affiliation(s)
- Yuji Yamamoto
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Yuya Shirai
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Kyuto Sonehara
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shinichi Namba
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Takafumi Ojima
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Kenichi Yamamoto
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Department of Pediatrics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Ryuya Edahiro
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Ken Suzuki
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Akinori Kanai
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
| | - Yoshiya Oda
- Department of Lipidomics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yutaka Suzuki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
| | - Takayuki Morisaki
- Division of Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Akira Narita
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Yoshito Takeda
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Gen Tamiya
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Masayuki Yamamoto
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Atsushi Kumanogoh
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Suita, Japan
- Center for Infectious Diseases for Education and Research (CiDER), Osaka University, Suita, Japan
- Japan Agency for Medical Research and Development-Core Research for Evolutional Science and Technology (AMED-CREST), Tokyo, Japan
- Center for Advanced Modalities and DDS (CAMaD), Osaka University, Suita, Japan
| | - Toshimasa Yamauchi
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
- Japan Agency for Medical Research and Development-Core Research for Evolutional Science and Technology (AMED-CREST), Tokyo, Japan.
| | - Takashi Kadowaki
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
- Toranomon Hospital, Tokyo, Japan.
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan.
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
- Center for Infectious Diseases for Education and Research (CiDER), Osaka University, Suita, Japan.
- Center for Advanced Modalities and DDS (CAMaD), Osaka University, Suita, Japan.
- Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Suita, Japan.
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Wan C, Sun S, Han Y, Du Y, Li X, Zhang L, Yang Y, Hao J, Wu Y. Integrating lipid metabolomics, serum medicinal chemistry, network pharmacology and experimental validation to explore the mechanism of Sanmiao wan in the treatment of rheumatoid arthritis. JOURNAL OF ETHNOPHARMACOLOGY 2025; 340:119295. [PMID: 39733801 DOI: 10.1016/j.jep.2024.119295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2024] [Revised: 12/18/2024] [Accepted: 12/26/2024] [Indexed: 12/31/2024]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Rheumatoid arthritis (RA) is a common autoimmune disease with a high clinical morbidity and leads to persistent chronic inflammation. Sanmiao wan is a classic formula for the treatment of RA, and the results of clinical and experimental studies have shown its therapeutic effect on RA. However, its mechanism of action remains unclear. AIM OF THE STUDY The aim of this study was to evaluate the effect of Sanmiao wan on RA rats and to further explore its protective mechanism. MATERIALS AND METHODS Research was conducted using RA models induced by Freund's adjuvant complete, and the degree of arthritis, bone destruction, histopathological and clinical chemical indexes of RA model rats were used to evaluate the animal model and the therapeutic effect of Sanmiao wan. A combination of lipid metabolomics, serum medicinal chemistry, network pharmacology, molecular docking and experimental validation was used to systematically elucidate the potential mechanism of action of Sanmiao wan in the treatment of RA. RESULT Pharmacodynamic results showed that Sanmiao reduced joint swelling and improved immunity, and the results of non-targeted lipid metabolomics showed a total of 6 lipid core markers, which were hypothesised to play a therapeutic role in RA by modulating the glycerophospholipid metabolism and sphingolipid metabolism pathways. Using serum medicinal chemistry, we identified 19 blood components and predicted the targets related to RA, and combined with network pharmacology, we screened a total of 59 components and disease-cross-cutting targets, and the enrichment analysis and network pharmacology and KEGG results indicated that the core targets were TNF, IL6, MMP3, and the key metabolic pathways were TNF signaling pathway, lipid and The key metabolic pathways are TNF signaling pathway, lipid and atherosclerosis, rheumatoid arthritis, IL-17 signaling pathway and sphingolipid signaling pathway, etc. It was verified by molecular docking and ELISA experiments that palmatine, cyasterone, atractylenolide I, atractylenolide III, wogonoside, wogonin, phellodendrine, and berberine in Sanmiao could reduce the activity of these targets, thereby inhibiting the expression of inflammatory factors TNF-α, IL6, IL17, RF, MMP3, STAT3. CONCLUSIONS Sanmiao has a good therapeutic effect on RA, and for the first time, it was found that its potential mechanism of action may be to treat RA by decreasing the activities of TNF, IL6, MMP3 and modulating glycerophospholipid metabolism and sphingolipid metabolism.It provides a solid basis for the clinical application of Sanmiao wan.
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Affiliation(s)
- Chunlei Wan
- Mudanjiang Normal University, Mudanjiang, 157011, China.
| | - Siyu Sun
- Mudanjiang Normal University, Mudanjiang, 157011, China
| | - Yuxing Han
- Mudanjiang Normal University, Mudanjiang, 157011, China
| | - Yuqing Du
- Mudanjiang Normal University, Mudanjiang, 157011, China
| | - Xueying Li
- Mudanjiang Normal University, Mudanjiang, 157011, China
| | - Lei Zhang
- Mudanjiang Normal University, Mudanjiang, 157011, China
| | - Yue Yang
- Mudanjiang Normal University, Mudanjiang, 157011, China
| | - Jingwei Hao
- Mudanjiang Normal University, Mudanjiang, 157011, China
| | - Yuqi Wu
- Mudanjiang Normal University, Mudanjiang, 157011, China
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Luo X, Luo B, Fei L, Zhang Q, Liang X, Chen Y, Zhou X. MS4A superfamily molecules in tumors, Alzheimer's and autoimmune diseases. Front Immunol 2024; 15:1481494. [PMID: 39717774 PMCID: PMC11663944 DOI: 10.3389/fimmu.2024.1481494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2024] [Accepted: 11/12/2024] [Indexed: 12/25/2024] Open
Abstract
MS4A (membrane-spanning 4-domain, subfamily A) molecules are categorized into tetraspanins, which possess four-transmembrane structures. To date, eighteen MS4A members have been identified in humans, whereas twenty-three different molecules have been identified in mice. MS4A proteins are selectively expressed on the surfaces of various immune cells, such as B cells (MS4A1), mast cells (MS4A2), macrophages (MS4A4A), Foxp3+CD4+ regulatory T cells (MS4A4B), and type 3 innate lymphoid cells (TMEM176A and TMEM176B). Early research confirmed that most MS4A molecules function as ion channels that regulate the transport of calcium ions. Recent studies have revealed that some MS4A proteins also function as chaperones that interact with various immune molecules, such as pattern recognition receptors and/or immunoglobulin receptors, to form immune complexes and transmit downstream signals, leading to cell activation, growth, and development. Evidence from preclinical animal models and human genetic studies suggests that the MS4A superfamily plays critical roles in the pathogenesis of various diseases, including cancer, infection, allergies, neurodegenerative diseases and autoimmune diseases. We review recent progress in this field and focus on elucidating the molecular mechanisms by which different MS4A molecules regulate the progression of tumors, Alzheimer's disease, and autoimmune diseases. Therefore, in-depth research into MS4A superfamily members may clarify their ability to act as candidate biomarkers and therapeutic targets for these diseases. Eighteen distinct members of the MS4A (membrane-spanning four-domain subfamily A) superfamily of four-transmembrane proteins have been identified in humans, whereas the MS4A genes are translated into twenty-three different molecules in mice. These proteins are selectively expressed on the surface of various immune cells, such as B cells (MS4A1), macrophages (MS4A4A), mast cells (MS4A2), Foxp3+CD4+ regulatory T cells (MS4A4B), type 3 innate lymphoid cells (TMEM176A and TMEM176B) and colonic epithelial cells (MS4A12). Functionally, most MS4A molecules function as ion channels that regulate the flow of calcium ions [Ca2+] across cell membranes. Recent studies have revealed that some MS4A proteins also act as molecular chaperones and interact with various types of immune receptors, including pattern recognition receptors (PRRs) and immunoglobulin receptors (IgRs), to form signaling complexes, thereby modulating intracellular signaling and cellular activity. Evidence from preclinical animal models and human genetic studies suggests that MS4A proteins play critical roles in various diseases (2). Therefore, we reviewed the recent progress in understanding the role of the MS4A superfamily in diseases, particularly in elucidating its function as a candidate biomarker and therapeutic target for cancer.
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Affiliation(s)
- Xuejiao Luo
- Department of Dermatology, The Affiliated Hospital of the Non-Commissioned Officer (NCO) School, The Army Medical University, Shijiazhuang, Hebei, China
| | - Bin Luo
- Institute of Immunology, Department of Basic Medicine, The Army Military Medical University, Chongqing, China
| | - Lei Fei
- Institute of Immunology, Department of Basic Medicine, The Army Military Medical University, Chongqing, China
| | - Qinggao Zhang
- Chronic Disease Research Center, Medical College, Dalian University, Dalian, Liaoning, China
| | - Xinyu Liang
- Department of Otolaryngology, The Second Affiliated Hospital of the Army Military Medical University, Chongqing, China
| | - Yongwen Chen
- Institute of Immunology, Department of Basic Medicine, The Army Military Medical University, Chongqing, China
- Chronic Disease Research Center, Medical College, Dalian University, Dalian, Liaoning, China
| | - Xueqin Zhou
- Department of Otolaryngology, The Second Affiliated Hospital of the Army Military Medical University, Chongqing, China
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Fernández-Gallego N, Castillo-González R, Moreno-Serna L, García-Cívico AJ, Sánchez-Martínez E, López-Sanz C, Fontes AL, Pimentel LL, Gradillas A, Obeso D, Neuhaus R, Ramírez-Huesca M, Ruiz-Fernández I, Nuñez-Borque E, Carrasco YR, Ibáñez B, Martín P, Blanco C, Barbas C, Barber D, Rodríguez-Alcalá LM, Villaseñor A, Esteban V, Sánchez-Madrid F, Jiménez-Saiz R. Allergic inflammation triggers dyslipidemia via IgG signalling. Allergy 2024; 79:2680-2699. [PMID: 38864116 DOI: 10.1111/all.16187] [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: 09/14/2023] [Revised: 05/02/2024] [Accepted: 05/04/2024] [Indexed: 06/13/2024]
Abstract
BACKGROUND Allergic diseases begin early in life and are often chronic, thus creating an inflammatory environment that may precede or exacerbate other pathologies. In this regard, allergy has been associated to metabolic disorders and with a higher risk of cardiovascular disease, but the underlying mechanisms remain incompletely understood. METHODS We used a murine model of allergy and atherosclerosis, different diets and sensitization methods, and cell-depleting strategies to ascertain the contribution of acute and late phase inflammation to dyslipidemia. Untargeted lipidomic analyses were applied to define the lipid fingerprint of allergic inflammation at different phases of allergic pathology. Expression of genes related to lipid metabolism was assessed in liver and adipose tissue at different times post-allergen challenge. Also, changes in serum triglycerides (TGs) were evaluated in a group of 59 patients ≥14 days after the onset of an allergic reaction. RESULTS We found that allergic inflammation induces a unique lipid signature that is characterized by increased serum TGs and changes in the expression of genes related to lipid metabolism in liver and adipose tissue. Alterations in blood TGs following an allergic reaction are independent of T-cell-driven late phase inflammation. On the contrary, the IgG-mediated alternative pathway of anaphylaxis is sufficient to induce a TG increase and a unique lipid profile. Lastly, we demonstrated an increase in serum TGs in 59 patients after undergoing an allergic reaction. CONCLUSION Overall, this study reveals that IgG-mediated allergic inflammation regulates lipid metabolism.
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Affiliation(s)
- Nieves Fernández-Gallego
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
- Department of Immunology, Instituto de Investigación Sanitaria Hospital Universitario de La Princesa (IIS-Princesa), Universidad Autónoma de Madrid (UAM), Madrid, Spain
| | - Raquel Castillo-González
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
- Department of Immunology, Ophthalmology and Ear, Nose and Throat (ENT), Universidad Complutense de Madrid, Madrid, Spain
| | - Lucía Moreno-Serna
- Department of Immunology, Instituto de Investigación Sanitaria Hospital Universitario de La Princesa (IIS-Princesa), Universidad Autónoma de Madrid (UAM), Madrid, Spain
| | - Antonio J García-Cívico
- Department of Basic Medical Sciences, Faculty of Medicine, Instituto de Medicina Molecular Aplicada (IMMA), Universidad San Pablo-CEU, CEU Universities, Madrid, Spain
- Centro de Metabolómica y Bioanálisis (CEMBIO), Faculty of Pharmacy, Universidad San Pablo-CEU, CEU Universities, Madrid, Spain
| | - Elisa Sánchez-Martínez
- Department of Immunology, Instituto de Investigación Sanitaria Hospital Universitario de La Princesa (IIS-Princesa), Universidad Autónoma de Madrid (UAM), Madrid, Spain
| | - Celia López-Sanz
- Department of Immunology, Instituto de Investigación Sanitaria Hospital Universitario de La Princesa (IIS-Princesa), Universidad Autónoma de Madrid (UAM), Madrid, Spain
| | - Ana Luiza Fontes
- CBQF-Centro de Biotecnologia e Química Fina-Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Porto, Portugal
| | - Lígia L Pimentel
- CBQF-Centro de Biotecnologia e Química Fina-Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Porto, Portugal
| | - Ana Gradillas
- Centro de Metabolómica y Bioanálisis (CEMBIO), Faculty of Pharmacy, Universidad San Pablo-CEU, CEU Universities, Madrid, Spain
| | - David Obeso
- Department of Basic Medical Sciences, Faculty of Medicine, Instituto de Medicina Molecular Aplicada (IMMA), Universidad San Pablo-CEU, CEU Universities, Madrid, Spain
- Centro de Metabolómica y Bioanálisis (CEMBIO), Faculty of Pharmacy, Universidad San Pablo-CEU, CEU Universities, Madrid, Spain
| | - René Neuhaus
- Department of Basic Medical Sciences, Faculty of Medicine, Instituto de Medicina Molecular Aplicada (IMMA), Universidad San Pablo-CEU, CEU Universities, Madrid, Spain
- Centro de Metabolómica y Bioanálisis (CEMBIO), Faculty of Pharmacy, Universidad San Pablo-CEU, CEU Universities, Madrid, Spain
| | | | | | - Emilio Nuñez-Borque
- Department of Allergy and Immunology, Instituto de Investigación Sanitaria Fundación Jiménez Díaz (IIS-FJD), Universidad Autónoma de Madrid (UAM), Madrid, Spain
| | - Yolanda R Carrasco
- Department of Immunology and Oncology, Centro Nacional de Biotecnología (CNB)-CSIC, Madrid, Spain
| | - Borja Ibáñez
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
- Department of Cardiology, Instituto de Investigación Sanitaria Fundación Jiménez Díaz (IIS-FJD), Madrid, Spain
- CIBER de Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III, Madrid, Spain
| | - Pilar Martín
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
- CIBER de Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III, Madrid, Spain
| | - Carlos Blanco
- Department of Allergy, Instituto de Investigación Sanitaria Hospital Universitario de La Princesa (IIS-Princesa), Madrid, Spain
| | - Coral Barbas
- Centro de Metabolómica y Bioanálisis (CEMBIO), Faculty of Pharmacy, Universidad San Pablo-CEU, CEU Universities, Madrid, Spain
| | - Domingo Barber
- Department of Basic Medical Sciences, Faculty of Medicine, Instituto de Medicina Molecular Aplicada (IMMA), Universidad San Pablo-CEU, CEU Universities, Madrid, Spain
| | - Luis M Rodríguez-Alcalá
- CBQF-Centro de Biotecnologia e Química Fina-Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Porto, Portugal
| | - Alma Villaseñor
- Department of Basic Medical Sciences, Faculty of Medicine, Instituto de Medicina Molecular Aplicada (IMMA), Universidad San Pablo-CEU, CEU Universities, Madrid, Spain
- Centro de Metabolómica y Bioanálisis (CEMBIO), Faculty of Pharmacy, Universidad San Pablo-CEU, CEU Universities, Madrid, Spain
| | - Vanesa Esteban
- Department of Allergy and Immunology, Instituto de Investigación Sanitaria Fundación Jiménez Díaz (IIS-FJD), Universidad Autónoma de Madrid (UAM), Madrid, Spain
- Faculty of Medicine and Biomedicine, Universidad Alfonso X El Sabio, Madrid, Spain
| | - Francisco Sánchez-Madrid
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
- Department of Immunology, Instituto de Investigación Sanitaria Hospital Universitario de La Princesa (IIS-Princesa), Universidad Autónoma de Madrid (UAM), Madrid, Spain
- CIBER de Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III, Madrid, Spain
| | - Rodrigo Jiménez-Saiz
- Department of Immunology, Instituto de Investigación Sanitaria Hospital Universitario de La Princesa (IIS-Princesa), Universidad Autónoma de Madrid (UAM), Madrid, Spain
- Department of Immunology and Oncology, Centro Nacional de Biotecnología (CNB)-CSIC, Madrid, Spain
- Department of Medicine, McMaster Immunology Research Centre (MIRC), Schroeder Allergy and Immunology Research Institute (SAIRI), McMaster University, Hamilton, Ontario, Canada
- Faculty of Experimental Sciences, Universidad Francisco de Vitoria (UFV), Madrid, Spain
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Acharya S, Liao S, Jung WJ, Kang YS, Moghaddam VA, Feitosa MF, Wojczynski MK, Lin S, Anema JA, Schwander K, Connell JO, Province MA, Brent MR. A methodology for gene level omics-WAS integration identifies genes influencing traits associated with cardiovascular risks: the Long Life Family Study. Hum Genet 2024; 143:1241-1252. [PMID: 39276247 PMCID: PMC11485042 DOI: 10.1007/s00439-024-02701-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 08/15/2024] [Indexed: 09/16/2024]
Abstract
The Long Life Family Study (LLFS) enrolled 4953 participants in 539 pedigrees displaying exceptional longevity. To identify genetic mechanisms that affect cardiovascular risks in the LLFS population, we developed a multi-omics integration pipeline and applied it to 11 traits associated with cardiovascular risks. Using our pipeline, we aggregated gene-level statistics from rare-variant analysis, GWAS, and gene expression-trait association by Correlated Meta-Analysis (CMA). Across all traits, CMA identified 64 significant genes after Bonferroni correction (p ≤ 2.8 × 10-7), 29 of which replicated in the Framingham Heart Study (FHS) cohort. Notably, 20 of the 29 replicated genes do not have a previously known trait-associated variant in the GWAS Catalog within 50 kb. Thirteen modules in Protein-Protein Interaction (PPI) networks are significantly enriched in genes with low meta-analysis p-values for at least one trait, three of which are replicated in the FHS cohort. The functional annotation of genes in these modules showed a significant over-representation of trait-related biological processes including sterol transport, protein-lipid complex remodeling, and immune response regulation. Among major findings, our results suggest a role of triglyceride-associated and mast-cell functional genes FCER1A, MS4A2, GATA2, HDC, and HRH4 in atherosclerosis risks. Our findings also suggest that lower expression of ATG2A, a gene we found to be associated with BMI, may be both a cause and consequence of obesity. Finally, our results suggest that ENPP3 may play an intermediary role in triglyceride-induced inflammation. Our pipeline is freely available and implemented in the Nextflow workflow language, making it easily runnable on any compute platform ( https://nf-co.re/omicsgenetraitassociation ).
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Affiliation(s)
- Sandeep Acharya
- Division of Computational and Data Sciences, Washington University, St Louis, MO, USA
| | - Shu Liao
- Department of Computer Science and Engineering, Washington University, St Louis, MO, USA
| | - Wooseok J Jung
- Department of Computer Science and Engineering, Washington University, St Louis, MO, USA
| | - Yu S Kang
- Department of Computer Science and Engineering, Washington University, St Louis, MO, USA
| | - Vaha Akbary Moghaddam
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA
| | - Mary F Feitosa
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA
| | - Mary K Wojczynski
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA
| | - Shiow Lin
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA
| | - Jason A Anema
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA
| | - Karen Schwander
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA
| | - Jeff O Connell
- Department of Medicine, University of Maryland, Baltimore, MD, USA
| | - Michael A Province
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA
| | - Michael R Brent
- Department of Computer Science and Engineering, Washington University, St Louis, MO, USA.
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Qin M, Chen L, Hou X, Wu W, Liu Y, Pan Y, Zhang M, Tan Z, Huang D. Ultra-High-Performance Liquid Chromatography-High-Definition Mass Spectrometry-Based Metabolomics to Reveal the Potential Anti-Arthritic Effects of Illicium verum in Cultured Fibroblast-like Synoviocytes Derived from Rheumatoid Arthritis. Metabolites 2024; 14:517. [PMID: 39452898 PMCID: PMC11509614 DOI: 10.3390/metabo14100517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2024] [Revised: 09/23/2024] [Accepted: 09/23/2024] [Indexed: 10/26/2024] Open
Abstract
BACKGROUND Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease. The fruits of Illicium verum, which is a medicinal and edible resource, have been shown to have anti-inflammatory properties. METHODS In this study, we investigated the effects of I. verum extracts (IVEs) on human RA fibroblasts-like synoviocytes (RA-FLS) by using a sensitive and selective ultra-high-performance liquid chromatography with high-definition mass spectrometry (UPLC-HDMS) method. We subsequently analyzed the metabolites produced after the incubation of cultured RA-FLS with IVEs. RESULTS IVEs inhibited the proliferation and suppressed the migration of RA-FLS, and reduced the levels of inflammatory factors including TNF-α and IL-6. Twenty differential metabolites responsible for the effects of IVEs were screened and annotated based on the UPLC-HDMS data by using a cell metabolomics approach. DISCUSSION Our findings suggest that treating RA-FLS with IVEs can regulate lipid and amino acid metabolism, indicating that this extract has the potential to modify the metabolic pathways that cause inflammation in RA. CONCLUSIONS This might lead to novel therapeutic strategies for managing patients with RA.
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Affiliation(s)
| | | | | | | | | | - Yu Pan
- National Engineering Research Center of Southwest Endangered Medicinal Resources Development, Guangxi Botanical Garden of Medicinal Plants, 189 Changgang Road, Nanning 530023, China (W.W.); (Z.T.)
| | | | | | - Danna Huang
- National Engineering Research Center of Southwest Endangered Medicinal Resources Development, Guangxi Botanical Garden of Medicinal Plants, 189 Changgang Road, Nanning 530023, China (W.W.); (Z.T.)
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Li T, Dou Y, Ji J, Chen H, Zhu S, Wang M, Xiong Y, Wang Z, Shan J, Qian K, An L, Lin L, Wang S, Dai Q. Lipidomics reveals the serum profiles of pediatric allergic rhinitis and its severity. Biomed Chromatogr 2024; 38:e5927. [PMID: 38866427 DOI: 10.1002/bmc.5927] [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: 04/01/2024] [Revised: 05/06/2024] [Accepted: 05/21/2024] [Indexed: 06/14/2024]
Abstract
Allergic rhinitis (AR) is a prevalent upper airway chronic inflammatory disease in children worldwide. The role of bioactive lipids in the regulation of AR has been recognized, but the underlying serum lipidomic basis of its pathology remains unclear. We utilized ultra-performance liquid chromatography (UPLC)-Q-Exactive Orbitrap/mass spectrometry (MS) to investigate the serum lipidomic profiles of children with AR. The lipidomic analysis identified 42 lipids that were differentially expressed (p < 0.05, fold change > 2) between the AR (n = 75) and normal control groups (n = 44). Specifically, the serum levels of diacylglycerol (DG), triacylglycerol (TG), fatty acid (FA), lysophosphatidylcholine (LPC), lysophosphatidylethanolamine, phosphatidyl-ethanolamine, and cardiolipins were significantly higher in the AR group. The diagnostic potential of the identified lipids was further evaluated using receiver operating characteristic curve analysis. The analysis revealed that five lipids, including FA 30:7, LPC O-18:1, LPC 18:0, LPC 16:0, and DG 34:0, had area under the curve values greater than 0.9 (p < 0.05). Furthermore, serum levels of IgE and IL-33, markers of AR severity, were found to have a significant positive correlation (p < 0.05) with DGs, LPCs, TGs, and FAs in AR patients. This study revealed the lipid disorders associated with AR and its severity, providing new insights into the pathological process of AR.
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Affiliation(s)
- Tao Li
- Department of Pediatrics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Key Laboratory of Children's Health and Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Yuzhu Dou
- Department of Pediatrics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Key Laboratory of Children's Health and Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Jianjian Ji
- Jiangsu Key Laboratory of Children's Health and Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Hui Chen
- Department of Pediatrics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Key Laboratory of Children's Health and Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Shaoyun Zhu
- Department of Pediatrics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Key Laboratory of Children's Health and Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Min Wang
- Department of Traditional Chinese Medicine, Wuxi Traditional Chinese Medicine Hospital, Wuxi, China
| | - Yingcai Xiong
- Jiangsu Key Laboratory of Children's Health and Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Zhao Wang
- Department of Pediatrics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Key Laboratory of Children's Health and Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Jinjun Shan
- Jiangsu Key Laboratory of Children's Health and Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | | | - Li An
- Jiangsu Key Laboratory of Children's Health and Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Lili Lin
- Department of Pediatrics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Shouchuan Wang
- Department of Pediatrics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Key Laboratory of Children's Health and Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Qigang Dai
- Department of Pediatrics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Key Laboratory of Children's Health and Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, China
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Atehortua L, Sean Davidson W, Chougnet CA. Interactions Between HDL and CD4+ T Cells: A Novel Understanding of HDL Anti-Inflammatory Properties. Arterioscler Thromb Vasc Biol 2024; 44:1191-1201. [PMID: 38660807 PMCID: PMC11111342 DOI: 10.1161/atvbaha.124.320851] [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] [Indexed: 04/26/2024]
Abstract
Several studies in animal models and human cohorts have recently suggested that HDLs (high-density lipoproteins) not only modulate innate immune responses but also adaptative immune responses, particularly CD4+ T cells. CD4+ T cells are central effectors and regulators of the adaptive immune system, and any alterations in their homeostasis contribute to the pathogenesis of cardiovascular diseases, autoimmunity, and inflammatory diseases. In this review, we focus on how HDLs and their components affect CD4+ T-cell homeostasis by modulating cholesterol efflux, immune synapsis, proliferation, differentiation, oxidative stress, and apoptosis. While the effects of apoB-containing lipoproteins on T cells have been relatively well established, this review focuses specifically on new connections between HDL and CD4+ T cells. We present a model where HDL may modulate T cells through both direct and indirect mechanisms.
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Affiliation(s)
- Laura Atehortua
- Division of Immunobiology, Cincinnati Children’s Hospital Research Foundation, University of Cincinnati College of Medicine, Cincinnati, OH
| | - W. Sean Davidson
- Division of Experimental Pathology, Department of Pathology and Laboratory Medicine, University of Cincinnati, Cincinnati, OH
| | - Claire A. Chougnet
- Division of Immunobiology, Cincinnati Children’s Hospital Research Foundation, University of Cincinnati College of Medicine, Cincinnati, OH
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Acharya S, Liao S, Jung WJ, Kang YS, Moghaddam VA, Feitosa M, Wojczynski M, Lin S, Anema JA, Schwander K, Connell JO, Province M, Brent MR. Multi-omics Integration Identifies Genes Influencing Traits Associated with Cardiovascular Risks: The Long Life Family Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.04.24303657. [PMID: 38496585 PMCID: PMC10942516 DOI: 10.1101/2024.03.04.24303657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
The Long Life Family Study (LLFS) enrolled 4,953 participants in 539 pedigrees displaying exceptional longevity. To identify genetic mechanisms that affect cardiovascular risks in the LLFS population, we developed a multi-omics integration pipeline and applied it to 11 traits associated with cardiovascular risks. Using our pipeline, we aggregated gene-level statistics from rare-variant analysis, GWAS, and gene expression-trait association by Correlated Meta-Analysis (CMA). Across all traits, CMA identified 64 significant genes after Bonferroni correction (p ≤ 2.8×10-7), 29 of which replicated in the Framingham Heart Study (FHS) cohort. Notably, 20 of the 29 replicated genes do not have a previously known trait-associated variant in the GWAS Catalog within 50 kb. Thirteen modules in Protein-Protein Interaction (PPI) networks are significantly enriched in genes with low meta-analysis p-values for at least one trait, three of which are replicated in the FHS cohort. The functional annotation of genes in these modules showed a significant over-representation of trait-related biological processes including sterol transport, protein-lipid complex remodeling, and immune response regulation. Among major findings, our results suggest a role of triglyceride-associated and mast-cell functional genes FCER1A, MS4A2, GATA2, HDC, and HRH4 in atherosclerosis risks. Our findings also suggest that lower expression of ATG2A, a gene we found to be associated with BMI, may be both a cause and consequence of obesity. Finally, our results suggest that ENPP3 may play an intermediary role in triglyceride-induced inflammation. Our pipeline is freely available and implemented in the Nextflow workflow language, making it easily runnable on any compute platform (https://nf-co.re/omicsgenetraitassociation).
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Affiliation(s)
- Sandeep Acharya
- Division of Computational and Data Sciences, Washington University, St Louis, MO
| | - Shu Liao
- Department of Computer Science and Engineering, Washington University, St Louis, MO
| | - Wooseok J Jung
- Department of Computer Science and Engineering, Washington University, St Louis, MO
| | - Yu S Kang
- Department of Computer Science and Engineering, Washington University, St Louis, MO
| | - Vaha A Moghaddam
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO
| | - Mary Feitosa
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO
| | - Mary Wojczynski
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO
| | - Shiow Lin
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO
| | - Jason A Anema
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO
| | - Karen Schwander
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO
| | - Jeff O Connell
- Department of Medicine, University of Maryland, Baltimore, MD
| | - Mike Province
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO
| | - Michael R Brent
- Department of Computer Science and Engineering, Washington University, St Louis, MO
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