1
|
Chuang HC, Lan KY, Hsu PM, Chen MH, Chen YM, Yen JH, Liao BY, Tan TH. UHRF1P contributes to IL-17A-mediated systemic lupus erythematosus via UHRF1-MAP4K3 axis. J Autoimmun 2024; 146:103221. [PMID: 38643728 DOI: 10.1016/j.jaut.2024.103221] [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: 02/06/2024] [Revised: 03/14/2024] [Accepted: 04/01/2024] [Indexed: 04/23/2024]
Abstract
Inflammatory T cells contribute to the pathogenesis of autoimmune diseases such as systemic lupus erythematosus (SLE). Analysis of the T-cell transcriptomics data of two independent SLE patient cohorts by three machine learning models revealed the pseudogene UHRF1P as a novel SLE biomarker. The pseudogene-encoded UHRF1P protein was overexpressed in peripheral blood T cells of SLE patients. The UHRF1P protein lacks the amino-terminus of its parental UHRF1 protein, resulting in missing the proteasome-binding ubiquitin-like (Ubl) domain of UHRF1. T-cell-specific UHRF1P transgenic mice manifested the induction of IL-17A and autoimmune inflammation. Mechanistically, UHFR1P prevented UHRF1-induced Lys48-linked ubiquitination and degradation of MAP4K3 (GLK), which is a kinase known to induce IL-17A. Consistently, IL-17A induction and autoimmune phenotypes of UHRF1P transgenic mice were obliterated by MAP4K3 knockout. Collectively, UHRF1P overexpression in T cells inhibits the E3 ligase function of its parental UHRF1 and induces autoimmune diseases.
Collapse
Affiliation(s)
- Huai-Chia Chuang
- Immunology Research Center, National Health Research Institutes, Zhunan, Taiwan
| | - Kuei-Yuan Lan
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Pu-Ming Hsu
- Immunology Research Center, National Health Research Institutes, Zhunan, Taiwan
| | - Ming-Han Chen
- Division of Allergy, Immunology, and Rheumatology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yi-Ming Chen
- Division of Allergy, Immunology, and Rheumatology, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Jeng-Hsien Yen
- Division of Rheumatology, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
| | - Ben-Yang Liao
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan.
| | - Tse-Hua Tan
- Immunology Research Center, National Health Research Institutes, Zhunan, Taiwan.
| |
Collapse
|
2
|
Zhu X, Chen Y, Yin Z, Zhang Y, Shen Y, Dai D, Lin X, Zou LH, Shen N, Ye Z, Ding H, Hou G. Novel potential lncRNA biomarker in B cells indicates essential pathogenic pathway activation in patients with SLE. Lupus Sci Med 2024; 11:e001065. [PMID: 38599668 PMCID: PMC11015226 DOI: 10.1136/lupus-2023-001065] [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: 09/28/2023] [Accepted: 04/01/2024] [Indexed: 04/12/2024]
Abstract
OBJECTIVES Systemic lupus erythematosus (SLE) is a highly heterogeneous disease, and B cell abnormalities play a central role in the pathogenesis of SLE. Long non-coding RNAs (lncRNAs) have also been implicated in the pathogenesis of SLE. The expression of lncRNAs is finely regulated and cell-type dependent, so we aimed to identify B cell-expressing lncRNAs as biomarkers for SLE, and to explore their ability to reflect the status of SLE critical pathway and disease activity. METHODS Weighted gene coexpression network analysis (WGCNA) was used to cluster B cell-expressing genes of patients with SLE into different gene modules and relate them to clinical features. Based on the results of WGCNA, candidate lncRNA levels were further explored in public bulk and single-cell RNA-sequencing data. In another independent cohort, the levels of the candidate were detected by RT-qPCR and the correlation with disease activity was analysed. RESULTS WGCNA analysis revealed one gene module significantly correlated with clinical features, which was enriched in type I interferon (IFN) pathway. Among non-coding genes in this module, lncRNA RP11-273G15.2 was differentially expressed in all five subsets of B cells from patients with SLE compared with healthy controls and other autoimmune diseases. RT-qPCR validated that RP11-273G15.2 was highly expressed in SLE B cells and positively correlated with IFN scores (r=0.7329, p<0.0001) and disease activity (r=0.4710, p=0.0005). CONCLUSION RP11-273G15.2 could act as a diagnostic and disease activity monitoring biomarker for SLE, which might have the potential to guide clinical management.
Collapse
Affiliation(s)
- Xinyi Zhu
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, China
| | - Yashuo Chen
- Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, Guangdong, China
| | - Zhihua Yin
- Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, Guangdong, China
| | - Yutong Zhang
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, China
| | - Yiwei Shen
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, China
| | - Dai Dai
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, China
| | - Xiaojing Lin
- Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, Guangdong, China
| | - Ling-Hua Zou
- Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, Guangdong, China
| | - Nan Shen
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, China
| | - Zhizhong Ye
- Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, Guangdong, China
| | - Huihua Ding
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, China
| | - Guojun Hou
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, China
| |
Collapse
|
3
|
Zhan K, Buhler KA, Chen IY, Fritzler MJ, Choi MY. Systemic lupus in the era of machine learning medicine. Lupus Sci Med 2024; 11:e001140. [PMID: 38443092 PMCID: PMC11146397 DOI: 10.1136/lupus-2023-001140] [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: 12/29/2023] [Accepted: 01/26/2024] [Indexed: 03/07/2024]
Abstract
Artificial intelligence and machine learning applications are emerging as transformative technologies in medicine. With greater access to a diverse range of big datasets, researchers are turning to these powerful techniques for data analysis. Machine learning can reveal patterns and interactions between variables in large and complex datasets more accurately and efficiently than traditional statistical methods. Machine learning approaches open new possibilities for studying SLE, a multifactorial, highly heterogeneous and complex disease. Here, we discuss how machine learning methods are rapidly being integrated into the field of SLE research. Recent reports have focused on building prediction models and/or identifying novel biomarkers using both supervised and unsupervised techniques for understanding disease pathogenesis, early diagnosis and prognosis of disease. In this review, we will provide an overview of machine learning techniques to discuss current gaps, challenges and opportunities for SLE studies. External validation of most prediction models is still needed before clinical adoption. Utilisation of deep learning models, access to alternative sources of health data and increased awareness of the ethics, governance and regulations surrounding the use of artificial intelligence in medicine will help propel this exciting field forward.
Collapse
Affiliation(s)
- Kevin Zhan
- University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
| | - Katherine A Buhler
- University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
| | - Irene Y Chen
- Computational Precision Health, University of California Berkeley and University of California San Francisco, Berkeley, California, USA
- Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, California, USA
| | - Marvin J Fritzler
- University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
| | - May Y Choi
- University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
- McCaig Institute for Bone and Joint Health, Calgary, Alberta, Canada
| |
Collapse
|
4
|
Parodis I, Lanata C, Nikolopoulos D, Blazer A, Yazdany J. Reframing health disparities in SLE: A critical reassessment of racial and ethnic differences in lupus disease outcomes. Best Pract Res Clin Rheumatol 2023; 37:101894. [PMID: 38057256 DOI: 10.1016/j.berh.2023.101894] [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: 10/09/2023] [Accepted: 10/27/2023] [Indexed: 12/08/2023]
Abstract
Health disparities in the prevalence and outcomes of systemic lupus erythematosus (SLE) are well documented across racial and ethnic groups. Similar to other chronic diseases, differences in disease severity among individuals with SLE are likely influenced by both genetic predisposition and multiple social determinants of health. However, research in SLE that jointly examines the genetic and environmental contributions to the disease course is limited, resulting in an incomplete understanding of the biologic and social mechanisms that underly health disparities. While research on health disparities can reveal inequalities and inform resource allocation to improve outcomes, research that relies on racial and ethnic categories to describe diverse groups of people can pose challenges. Additionally, results from research comparing outcomes across socially constructed groups without considering other contributing factors can be misleading. We herein comprehensively examine existing literature on health disparities in SLE, including both clinical studies that examine the relationship between self-reported race and ethnicity and disease outcomes and studies that explore the relationships between genomics and lupus outcomes. Having surveyed this body of research, we propose a framework for research examining health disparities in SLE, including ways to mitigate bias in future studies.
Collapse
Affiliation(s)
- Ioannis Parodis
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden; Department of Gastroenterology, Dermatology and Rheumatology, Karolinska University Hospital, Stockholm, Sweden; Department of Rheumatology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
| | - Cristina Lanata
- Genomics of Autoimmune Rheumatic Disease Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Dionysis Nikolopoulos
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden; Department of Gastroenterology, Dermatology and Rheumatology, Karolinska University Hospital, Stockholm, Sweden
| | - Ashira Blazer
- Division of Rheumatology, Department of Medicine, Hospital for Special, Surgery, New York, NY, USA
| | - Jinoos Yazdany
- Division of Rheumatology, University of California, San Francisco, USA
| |
Collapse
|
5
|
Cutts Z, Patterson S, Maliskova L, Taylor KE, Ye C, Dall'Era M, Yazdany J, Criswell L, Fragiadakis GK, Langelier C, Capra JA, Sirota M, Lanata CM. Cell-Specific Transposable Element Gene Expression Analysis Identifies Associations with Systemic Lupus Erythematosus Phenotypes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.27.567477. [PMID: 38076936 PMCID: PMC10705239 DOI: 10.1101/2023.11.27.567477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2024]
Abstract
There is an established yet unexplained link between interferon (IFN) and systemic lupus erythematosus (SLE). The expression of sequences derived from transposable elements (TEs) may contribute to production of type I IFNs and generation of autoantibodies. We profiled cell-sorted RNA-seq data (CD4+ T cells, CD14+ monocytes, CD19+ B cells, and NK cells) from PBMCs of 120 SLE patients and quantified TE expression identifying 27,135 TEs. We tested for differential TE expression across 10 SLE phenotypes including autoantibody production and disease activity and discovered 731 differentially expressed (DE) TEs whose effects were mostly cell-specific and phenotype-specific. DE TEs were enriched for specific families and viral genes encoded in TE sequences. Increased expression of DE TEs was associated with genes involved in antiviral activity such as LY6E, ISG15, TRIM22 and pathways such as interferon signaling. These findings suggest that expression of TEs contributes to activation of SLE-related mechanisms in a cell-specific manner, which can impact disease diagnostics and therapeutics.
Collapse
|
6
|
Hubbard EL, Bachali P, Kingsmore KM, He Y, Catalina MD, Grammer AC, Lipsky PE. Analysis of transcriptomic features reveals molecular endotypes of SLE with clinical implications. Genome Med 2023; 15:84. [PMID: 37845772 PMCID: PMC10578040 DOI: 10.1186/s13073-023-01237-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 09/25/2023] [Indexed: 10/18/2023] Open
Abstract
BACKGROUND Systemic lupus erythematosus (SLE) is known to be clinically heterogeneous. Previous efforts to characterize subsets of SLE patients based on gene expression analysis have not been reproduced because of small sample sizes or technical problems. The aim of this study was to develop a robust patient stratification system using gene expression profiling to characterize individual lupus patients. METHODS We employed gene set variation analysis (GSVA) of informative gene modules to identify molecular endotypes of SLE patients, machine learning (ML) to classify individual patients into molecular subsets, and logistic regression to develop a composite metric estimating the scope of immunologic perturbations. SHapley Additive ExPlanations (SHAP) revealed the impact of specific features on patient sub-setting. RESULTS Using five datasets comprising 2183 patients, eight SLE endotypes were identified. Expanded analysis of 3166 samples in 17 datasets revealed that each endotype had unique gene enrichment patterns, but not all endotypes were observed in all datasets. ML algorithms trained on 2183 patients and tested on 983 patients not used to develop the model demonstrated effective classification into one of eight endotypes. SHAP indicated a unique array of features influential in sorting individual samples into each of the endotypes. A composite molecular score was calculated for each patient and significantly correlated with standard laboratory measures. Significant differences in clinical characteristics were associated with different endotypes, with those with the least perturbed transcriptional profile manifesting lower disease severity. The more abnormal endotypes were significantly more likely to experience a severe flare over the subsequent 52 weeks while on standard-of-care medication and specific endotypes were more likely to be clinical responders to the investigational product tested in one clinical trial analyzed (tabalumab). CONCLUSIONS Transcriptomic profiling and ML reproducibly separated lupus patients into molecular endotypes with significant differences in clinical features, outcomes, and responsiveness to therapy. Our classification approach using a composite scoring system based on underlying molecular abnormalities has both staging and prognostic relevance.
Collapse
Affiliation(s)
- Erika L Hubbard
- AMPEL BioSolutions, LLC, 250 W. Main St. #300, Charlottesville, VA, 22902, USA.
- RILITE Research Institute, Charlottesville, VA, 22902, USA.
| | - Prathyusha Bachali
- AMPEL BioSolutions, LLC, 250 W. Main St. #300, Charlottesville, VA, 22902, USA
- RILITE Research Institute, Charlottesville, VA, 22902, USA
| | - Kathryn M Kingsmore
- AMPEL BioSolutions, LLC, 250 W. Main St. #300, Charlottesville, VA, 22902, USA
- RILITE Research Institute, Charlottesville, VA, 22902, USA
| | - Yisha He
- Altria, Richmond, VA, 23230, USA
| | | | - Amrie C Grammer
- AMPEL BioSolutions, LLC, 250 W. Main St. #300, Charlottesville, VA, 22902, USA
- RILITE Research Institute, Charlottesville, VA, 22902, USA
| | - Peter E Lipsky
- AMPEL BioSolutions, LLC, 250 W. Main St. #300, Charlottesville, VA, 22902, USA
- RILITE Research Institute, Charlottesville, VA, 22902, USA
| |
Collapse
|
7
|
Andreoletti G, Romano O, Chou HJ, Sefid-Dashti MJ, Grilli A, Chen C, Lakshman N, Purushothaman P, Varfaj F, Mavilio F, Bicciato S, Urbinati F. High-throughput transcriptome analyses from ASPIRO, a phase 1/2/3 study of gene replacement therapy for X-linked myotubular myopathy. Am J Hum Genet 2023; 110:1648-1660. [PMID: 37673065 PMCID: PMC10577074 DOI: 10.1016/j.ajhg.2023.08.008] [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: 03/28/2023] [Revised: 08/09/2023] [Accepted: 08/11/2023] [Indexed: 09/08/2023] Open
Abstract
X-linked myotubular myopathy (XLMTM) is a severe congenital disease characterized by profound muscle weakness, respiratory failure, and early death. No approved therapy for XLMTM is currently available. Adeno-associated virus (AAV)-mediated gene replacement therapy has shown promise as an investigational therapeutic strategy. We aimed to characterize the transcriptomic changes in muscle biopsies of individuals with XLMTM who received resamirigene bilparvovec (AT132; rAAV8-Des-hMTM1) in the ASPIRO clinical trial and to identify potential biomarkers that correlate with therapeutic outcome. We leveraged RNA-sequencing data from the muscle biopsies of 15 study participants and applied differential expression analysis, gene co-expression analysis, and machine learning to characterize the transcriptomic changes at baseline (pre-dose) and at 24 and 48 weeks after resamirigene bilparvovec dosing. As expected, MTM1 expression levels were significantly increased after dosing (p < 0.0001). Differential expression analysis identified upregulated genes after dosing that were enriched in several pathways, including lipid metabolism and inflammatory response pathways, and downregulated genes were enriched in cell-cell adhesion and muscle development pathways. Genes involved in inflammatory and immune pathways were differentially expressed between participants exhibiting ventilator support reduction of either greater or less than 6 h/day after gene therapy compared to pre-dosing. Co-expression analysis identified similarly regulated genes, which were grouped into modules. Finally, the machine learning model identified five genes, including MTM1, as potential RNA biomarkers to monitor the progress of AAV gene replacement therapy. These findings further extend our understanding of AAV-mediated gene therapy in individuals with XLMTM at the transcriptomic level.
Collapse
Affiliation(s)
- Gaia Andreoletti
- Astellas Gene Therapies (formerly Audentes Therapeutics), San Francisco, CA, USA.
| | - Oriana Romano
- Department of Life Sciences, University of Modena and Reggio Emilia, 41125 Modena, Italy
| | - Hsin-Jung Chou
- Astellas Gene Therapies (formerly Audentes Therapeutics), San Francisco, CA, USA
| | | | - Andrea Grilli
- Department of Life Sciences, University of Modena and Reggio Emilia, 41125 Modena, Italy
| | - Clarice Chen
- Astellas Gene Therapies (formerly Audentes Therapeutics), San Francisco, CA, USA; Tox and Text Solutions, LLC, Anaheim, CA 92807, USA
| | - Neema Lakshman
- Astellas Gene Therapies (formerly Audentes Therapeutics), San Francisco, CA, USA
| | - Pravin Purushothaman
- Astellas Gene Therapies (formerly Audentes Therapeutics), San Francisco, CA, USA
| | - Fatbardha Varfaj
- Astellas Gene Therapies (formerly Audentes Therapeutics), San Francisco, CA, USA
| | - Fulvio Mavilio
- Department of Life Sciences, University of Modena and Reggio Emilia, 41125 Modena, Italy
| | - Silvio Bicciato
- Department of Life Sciences, University of Modena and Reggio Emilia, 41125 Modena, Italy
| | - Fabrizia Urbinati
- Astellas Gene Therapies (formerly Audentes Therapeutics), San Francisco, CA, USA.
| |
Collapse
|
8
|
Wilson JJ, Wei J, Daamen AR, Sears JD, Bechtel E, Mayberry CL, Stafford GA, Bechtold L, Grammer AC, Lipsky PE, Roopenian DC, Chang CH. Glucose oxidation-dependent survival of activated B cells provides a putative novel therapeutic target for lupus treatment. iScience 2023; 26:107487. [PMID: 37636066 PMCID: PMC10448027 DOI: 10.1016/j.isci.2023.107487] [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] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 03/27/2023] [Accepted: 07/24/2023] [Indexed: 08/29/2023] Open
Abstract
Aberrant metabolic demand is observed in immune/inflammatory disorders, yet the role in pathogenesis remains unclear. Here, we discover that in lupus, activated B cells, including germinal center B (GCB) cells, have remarkably high glycolytic requirement for survival over T cell populations, as demonstrated by increased metabolic activity in lupus-activated B cells compared to immunization-induced cells. The augmented reliance on glucose oxidation makes GCB cells vulnerable to mitochondrial ROS-induced oxidative stress and apoptosis. Short-term glycolysis inhibition selectively reduces pathogenic activated B in lupus-prone mice, extending their lifespan, without affecting T follicular helper cells. Particularly, BCMA-expressing GCB cells rely heavily on glucose oxidation. Depleting BCMA-expressing activated B cells with APRIL-based CAR-T cells significantly prolongs the lifespan of mice with severe autoimmune disease. These results reveal that glycolysis-dependent activated B and GCB cells, especially those expressing BCMA, are potentially key lupus mediators, and could be targeted to improve disease outcomes.
Collapse
Affiliation(s)
- John J. Wilson
- The Jackson Laboratory, Bar Harbor, Maine, ME 04609, USA
| | - Jian Wei
- The Jackson Laboratory, Bar Harbor, Maine, ME 04609, USA
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Shandong University, Jinan, Shandong 250012, China
| | - Andrea R. Daamen
- AMPEL BioSolutions and the RILITE Research Institute, Charlottesville, VA 22902, USA
| | - John D. Sears
- The Jackson Laboratory, Bar Harbor, Maine, ME 04609, USA
- Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Elaine Bechtel
- The Jackson Laboratory, Bar Harbor, Maine, ME 04609, USA
| | | | | | | | - Amrie C. Grammer
- AMPEL BioSolutions and the RILITE Research Institute, Charlottesville, VA 22902, USA
| | - Peter E. Lipsky
- AMPEL BioSolutions and the RILITE Research Institute, Charlottesville, VA 22902, USA
| | | | - Chih-Hao Chang
- The Jackson Laboratory, Bar Harbor, Maine, ME 04609, USA
- Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME 04469, USA
- Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA 02111, USA
| |
Collapse
|
9
|
Rector I, Owen KA, Bachali P, Hubbard E, Yazdany J, Dall'era M, Grammer AC, Lipsky PE. Differential regulation of the interferon response in systemic lupus erythematosus distinguishes patients of Asian ancestry. RMD Open 2023; 9:e003475. [PMID: 37709528 PMCID: PMC10503349 DOI: 10.1136/rmdopen-2023-003475] [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: 07/06/2023] [Accepted: 08/28/2023] [Indexed: 09/16/2023] Open
Abstract
OBJECTIVES Type I interferon (IFN) plays a role in the pathogenesis of systemic lupus erythematosus (SLE), but insufficient attention has been directed to the differences in IFN responses between ancestral populations. Here, we explored the expression of the interferon gene signatures (IGSs) in SLE patients of European ancestry (EA) and Asian ancestry (AsA). METHODS We used gene set variation analysis with multiple IGS encompassing the response to both type 1 and type 2 IFN in isolated CD14+ monocytes, CD19+B cells, CD4+T cells and Natural Killer (NK) cells from patients with SLE stratified by self-identified ancestry. The expression of genes upstream of the IGS and influenced by lupus-associated risk alleles was also examined. Lastly, we employed machine learning (ML) models to assess the most important features classifying patients by disease activity. RESULTS AsA patients with SLE exhibited greater enrichment in the IFN core and IFNA2 IGS compared with EA patients in all cell types examined and, in the presence and absence of autoantibodies. Overall, AsA patients with SLE demonstrated higher expression of genes upstream of the IGS than EA counterparts. ML with feature importance analysis indicated that IGS expression in NK cells, anti-dsDNA, complement levels and AsA status contributed to disease activity. CONCLUSIONS AsA patients with SLE exhibited higher IGS than EA patients in all cell types regardless of autoantibody status, with enhanced expression of genetically associated genes upstream of the IGS potentially contributing. AsA, along with the IGS in NK cells, anti-dsDNA and complement, independently influenced SLE disease activity.
Collapse
Affiliation(s)
- Ian Rector
- AMPEL Biosolutions LLC and the RILITE Research Institute, Charlottesville, Virginia, USA
| | | | - Prathyusha Bachali
- AMPEL Biosolutions LLC and the RILITE Research Institute, Charlottesville, Virginia, USA
| | - Erika Hubbard
- AMPEL Biosolutions LLC and the RILITE Research Institute, Charlottesville, Virginia, USA
| | - Jinoos Yazdany
- Medicine/Rheumatology, University of California, San Francisco, California, USA
| | - Maria Dall'era
- Division of Rheumatology, University of California, San Francisco, California, USA
| | - Amrie C Grammer
- AMPEL Biosolutions LLC and the RILITE Research Institute, Charlottesville, Virginia, USA
| | - Peter E Lipsky
- AMPEL Biosolutions LLC and the RILITE Research Institute, Charlottesville, Virginia, USA
| |
Collapse
|
10
|
Baglaenko Y, Wagner C, Bhoj VG, Brodin P, Gershwin ME, Graham D, Invernizzi P, Kidd KK, Korsunsky I, Levy M, Mammen AL, Nizet V, Ramirez-Valle F, Stites EC, Williams MS, Wilson M, Rose NR, Ladd V, Sirota M. Making inroads to precision medicine for the treatment of autoimmune diseases: Harnessing genomic studies to better diagnose and treat complex disorders. CAMBRIDGE PRISMS. PRECISION MEDICINE 2023; 1:e25. [PMID: 38550937 PMCID: PMC10953750 DOI: 10.1017/pcm.2023.14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 04/21/2023] [Accepted: 04/25/2023] [Indexed: 06/13/2024]
Abstract
Precision Medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle. Autoimmune diseases are those in which the body's natural defense system loses discriminating power between its own cells and foreign cells, causing the body to mistakenly attack healthy tissues. These conditions are very heterogeneous in their presentation and therefore difficult to diagnose and treat. Achieving precision medicine in autoimmune diseases has been challenging due to the complex etiologies of these conditions, involving an interplay between genetic, epigenetic, and environmental factors. However, recent technological and computational advances in molecular profiling have helped identify patient subtypes and molecular pathways which can be used to improve diagnostics and therapeutics. This review discusses the current understanding of the disease mechanisms, heterogeneity, and pathogenic autoantigens in autoimmune diseases gained from genomic and transcriptomic studies and highlights how these findings can be applied to better understand disease heterogeneity in the context of disease diagnostics and therapeutics.
Collapse
Affiliation(s)
| | | | | | | | | | - Daniel Graham
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Pietro Invernizzi
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- European Reference Network on Hepatological Diseases (ERN RARE-LIVER), IRCCS Fondazione San Gerardo dei Tintori, Monza, Italy
| | - Kenneth K. Kidd
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA
| | | | - Michael Levy
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrew L. Mammen
- National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, USA
| | - Victor Nizet
- School of Medicine, University of California San Diego, San Diego, CA, USA
| | | | - Edward C. Stites
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA
| | | | - Michael Wilson
- Weill Institute for Neurosciences, Department of Neurology, UCSF, San Francisco, CA, USA
| | - Noel R. Rose
- Autoimmune Association, Clinton Township, MI, USA
| | | | - Marina Sirota
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
- Department of Pediatrics, UCSF, San Francisco, CA, USA
| |
Collapse
|
11
|
Enrico P, Delvecchio G, Turtulici N, Aronica R, Pigoni A, Squarcina L, Villa FM, Perlini C, Rossetti MG, Bellani M, Lasalvia A, Bonetto C, Scocco P, D'Agostino A, Torresani S, Imbesi M, Bellini F, Veronese A, Bocchio-Chiavetto L, Gennarelli M, Balestrieri M, Colombo GI, Finardi A, Ruggeri M, Furlan R, Brambilla P. A machine learning approach on whole blood immunomarkers to identify an inflammation-associated psychosis onset subgroup. Mol Psychiatry 2023; 28:1190-1200. [PMID: 36604602 DOI: 10.1038/s41380-022-01911-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 11/29/2022] [Accepted: 12/07/2022] [Indexed: 01/07/2023]
Abstract
Psychosis onset is a transdiagnostic event that leads to a range of psychiatric disorders, which are currently diagnosed through clinical observation. The integration of multimodal biological data could reveal different subtypes of psychosis onset to target for the personalization of care. In this study, we tested the existence of subgroups of patients affected by first-episode psychosis (FEP) with a possible immunopathogenic basis. To do this, we designed a data-driven unsupervised machine learning model to cluster a sample of 127 FEP patients and 117 healthy controls (HC), based on the peripheral blood expression levels of 12 psychosis-related immune gene transcripts. To validate the model, we applied a resampling strategy based on the half-splitting of the total sample with random allocation of the cases. Further, we performed a post-hoc univariate analysis to verify the clinical, cognitive, and structural brain correlates of the subgroups identified. The model identified and validated two distinct clusters: 1) a FEP cluster characterized by the high expression of inflammatory and immune-activating genes (IL1B, CCR7, IL12A and CXCR3); 2) a cluster consisting of an equal number of FEP and HC subjects, which did not show a relative over or under expression of any immune marker (balanced subgroup). None of the subgroups was related to specific symptoms dimensions or longitudinal diagnosis of affective vs non-affective psychosis. FEP patients included in the balanced immune subgroup showed a thinning of the left supramarginal and superiorfrontal cortex (FDR-adjusted p-values < 0.05). Our results demonstrated the existence of a FEP patients' subgroup identified by a multivariate pattern of immunomarkers involved in inflammatory activation. This evidence may pave the way to sample stratification in clinical studies aiming to develop diagnostic tools and therapies targeting specific immunopathogenic pathways of psychosis.
Collapse
Affiliation(s)
- Paolo Enrico
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.,Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Giuseppe Delvecchio
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Nunzio Turtulici
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Rosario Aronica
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Alessandro Pigoni
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy.,Social and Affective Neuroscience Group, MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Letizia Squarcina
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Filippo M Villa
- Child Psychopathology Unit, Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Lecco, Italy
| | - Cinzia Perlini
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Clinical Psychology, University of Verona, Verona, Italy.,USD Clinical Psychology, Azienda Ospedaliera Universitaria Integrata (AOUI) of Verona, Verona, Italy
| | - Maria G Rossetti
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Marcella Bellani
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy.,UOC of Psychiatry, Azienda Ospedaliera Universitaria Integrata (AOUI) of Verona, Verona, Italy
| | - Antonio Lasalvia
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
| | - Chiara Bonetto
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
| | - Paolo Scocco
- Department of Mental Health, AULSS 6 Euganea, Padua, Italy
| | - Armando D'Agostino
- Department of Health Sciences, San Paolo University Hospital, University of Milan, Milano, Milan, Italy
| | - Stefano Torresani
- Department of Psychiatry, ULSS, Bolzano Suedtiroler Sanitaetbetrieb- Azienda Sanitaria dell'Alto Adige, Bolzano, Italy
| | | | | | | | - Luisella Bocchio-Chiavetto
- Faculty of Psychology, eCampus University, Novedrate, Como, Italy.,Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio, Fatebenefratelli, Brescia, Italy
| | - Massimo Gennarelli
- Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio, Fatebenefratelli, Brescia, Italy.,Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | | | - Gualtiero I Colombo
- Centro Cardiologico Monzino IRCCS, Immunology and Functional Genomics Unit, Milan, Italy
| | - Annamaria Finardi
- Clinical Neuroimmunology Unit, Institute of Experimental Neurology, IRCCS Ospedale San Raffaele, Milano, Italy
| | - Mirella Ruggeri
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy.,UOC of Psychiatry, Azienda Ospedaliera Universitaria Integrata (AOUI) of Verona, Verona, Italy
| | - Roberto Furlan
- Clinical Neuroimmunology Unit, Institute of Experimental Neurology, IRCCS Ospedale San Raffaele, Milano, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy. .,Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy.
| | | |
Collapse
|
12
|
Munguía-Realpozo P, Etchegaray-Morales I, Mendoza-Pinto C, Méndez-Martínez S, Osorio-Peña ÁD, Ayón-Aguilar J, García-Carrasco M. Current state and completeness of reporting clinical prediction models using machine learning in systemic lupus erythematosus: A systematic review. Autoimmun Rev 2023; 22:103294. [PMID: 36791873 DOI: 10.1016/j.autrev.2023.103294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 02/09/2023] [Indexed: 02/17/2023]
Abstract
OBJECTIVE We carried out a systematic review (SR) of adherence in diagnostic and prognostic applications of ML in SLE using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement. METHODS A SR employing five databases was conducted from its inception until December 2021. We identified articles that evaluated the utilization of ML for prognostic and/or diagnostic purposes. This SR was reported based on the PRISMA guidelines. The TRIPOD statement assessed adherence to reporting standards. Assessment for risk of bias was done using PROBAST tool. RESULTS We included 45 studies: 29 (64.4%) diagnostic and 16 (35.5%) prognostic prediction- model studies. Overall, articles adhered by between 17% and 67% (median 43%, IQR 37-49%) to TRIPOD items. Only few articles reported the model's predictive performance (2.3%, 95% CI 0.06-12.0), testing of interaction terms (2.3%, 95% CI 0.06-12.0), flow of participants (50%, 95% CI; 34.6-65.4), blinding of predictors (2.3%, 95% CI 0.06-12.0), handling of missing data (36.4%, 95% CI 22.4-52.2), and appropriate title (20.5%, 95% CI 9.8-35.3). Some items were almost completely reported: the source of data (88.6%, 95% CI 75.4-96.2), eligibility criteria (86.4%, 95% CI 76.2-96.5), and interpretation of findings (88.6%, 95% CI 75.4-96.2). In addition, most of model studies had high risk of bias. CONCLUSIONS The reporting adherence of ML-based model developed for SLE, is currently inadequate. Several items deemed crucial for transparent reporting were not fully reported in studies on ML-based prediction models. REVIEW REGISTRATION PROSPERO ID# CRD42021284881. (Amended to limit the scope).
Collapse
Affiliation(s)
- Pamela Munguía-Realpozo
- Systemic Autoimmune Diseases Research Unit, Specialties Hospital UMAE- CIBIOR, Mexican Institute for Social Security, Puebla, Mexico; Department of Rheumatology, Medicine School, Meritorious Autonomous University of Puebla, Mexico
| | - Ivet Etchegaray-Morales
- Department of Rheumatology, Medicine School, Meritorious Autonomous University of Puebla, Mexico.
| | - Claudia Mendoza-Pinto
- Systemic Autoimmune Diseases Research Unit, Specialties Hospital UMAE- CIBIOR, Mexican Institute for Social Security, Puebla, Mexico; Department of Rheumatology, Medicine School, Meritorious Autonomous University of Puebla, Mexico.
| | | | - Ángel David Osorio-Peña
- Department of Rheumatology, Medicine School, Meritorious Autonomous University of Puebla, Mexico
| | - Jorge Ayón-Aguilar
- Coordination of Health Research, Mexican Social Security Institute, Puebla, Mexico.
| | - Mario García-Carrasco
- Department of Rheumatology, Medicine School, Meritorious Autonomous University of Puebla, Mexico
| |
Collapse
|
13
|
Sasaki T, Bracero S, Keegan J, Chen L, Cao Y, Stevens E, Qu Y, Wang G, Nguyen J, Sparks JA, Holers VM, Alves SE, Lederer JA, Costenbader KH, Rao DA. Longitudinal Immune Cell Profiling in Patients With Early Systemic Lupus Erythematosus. Arthritis Rheumatol 2022; 74:1808-1821. [PMID: 35644031 PMCID: PMC10238884 DOI: 10.1002/art.42248] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 04/29/2022] [Accepted: 05/24/2022] [Indexed: 11/10/2022]
Abstract
OBJECTIVES To investigate the immune cell profiles of patients with systemic lupus erythematosus (SLE), and to identify longitudinal changes in those profiles over time. METHODS We employed mass cytometry with 3 different panels of 38-39 markers (an immunophenotyping panel, a T cell/monocyte panel, and a B cell panel) in cryopreserved peripheral blood mononuclear cells (PBMCs) from 9 patients with early SLE, 15 patients with established SLE, and 14 controls without autoimmune disease. We used machine learning-driven clustering, flow self-organizing maps, and dimensional reduction with t-distributed stochastic neighbor embedding to identify unique cell populations in early SLE and established SLE. We used mass cytometry data of PBMCs from 19 patients with early rheumatoid arthritis (RA) and 23 controls to compare levels of specific cell populations in early RA and SLE. For the 9 patients with early SLE, longitudinal mass cytometry analysis was applied to PBMCs at enrollment, 6 months after enrollment, and 1 year after enrollment. Serum samples were also assayed for 65 cytokines using Luminex multiplex assay, and associations between cell types and cytokines/chemokines were assessed. RESULTS Levels of peripheral helper T cells, follicular helper T (Tfh) cells, and several Ki-67+ proliferating subsets (ICOS+Ki-67+ CD8 T cells, Ki-67+ regulatory T cells, CD19intermediate Ki-67high plasmablasts, and PU.1high Ki-67high monocytes) were increased in patients with early SLE, with more prominent alterations than were seen in patients with early RA. Longitudinal mass cytometry and multiplex serum cytokine assays of samples from patients with early SLE revealed that levels of Tfh cells and CXCL10 had decreased 1 year after enrollment. Levels of CXCL13 were positively correlated with levels of several of the expanded cell populations in early SLE. CONCLUSION Two major helper T cell subsets and unique Ki-67+ proliferating immune cell subsets were expanded in patients in the early phase of SLE, and the immunologic features characteristic of early SLE evolved over time.
Collapse
Affiliation(s)
- Takanori Sasaki
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Sabrina Bracero
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Joshua Keegan
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Lin Chen
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Ye Cao
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Emma Stevens
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Yujie Qu
- Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA
| | - Guoxing Wang
- Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA
| | - Jennifer Nguyen
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Jeffrey A. Sparks
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - V. Michael Holers
- Division of Rheumatology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Stephen E. Alves
- Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA
| | - James A. Lederer
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Deepak A. Rao
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| |
Collapse
|
14
|
Thompson M, Gordon MG, Lu A, Tandon A, Halperin E, Gusev A, Ye CJ, Balliu B, Zaitlen N. Multi-context genetic modeling of transcriptional regulation resolves novel disease loci. Nat Commun 2022; 13:5704. [PMID: 36171194 PMCID: PMC9519579 DOI: 10.1038/s41467-022-33212-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 09/07/2022] [Indexed: 12/01/2022] Open
Abstract
A majority of the variants identified in genome-wide association studies fall in non-coding regions of the genome, indicating their mechanism of impact is mediated via gene expression. Leveraging this hypothesis, transcriptome-wide association studies (TWAS) have assisted in both the interpretation and discovery of additional genes associated with complex traits. However, existing methods for conducting TWAS do not take full advantage of the intra-individual correlation inherently present in multi-context expression studies and do not properly adjust for multiple testing across contexts. We introduce CONTENT-a computationally efficient method with proper cross-context false discovery correction that leverages correlation structure across contexts to improve power and generate context-specific and context-shared components of expression. We apply CONTENT to bulk multi-tissue and single-cell RNA-seq data sets and show that CONTENT leads to a 42% (bulk) and 110% (single cell) increase in the number of genetically predicted genes relative to previous approaches. We find the context-specific component of expression comprises 30% of heritability in tissue-level bulk data and 75% in single-cell data, consistent with cell-type heterogeneity in bulk tissue. In the context of TWAS, CONTENT increases the number of locus-phenotype associations discovered by over 51% relative to previous methods across 22 complex traits.
Collapse
Affiliation(s)
- Mike Thompson
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA.
| | - Mary Grace Gordon
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
- Biological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Andrew Lu
- UCLA-Caltech Medical Scientist Training Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Anchit Tandon
- Department of Mathematics, Indian Institute of Technology Delhi, Hauz Khas, Delhi, India
| | - Eran Halperin
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, University of California Los Angeles, Los Angeles, CA, USA
- Department of Anesthesiology and Perioperative Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Alexander Gusev
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, US
- Division of Genetics, Brigham and Women's Hospital, Boston, MA, US
| | - Chun Jimmie Ye
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
- Chan-Zuckerberg Biohub, San Francisco, CA, USA
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Institute for Computational Health Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Brunilda Balliu
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Noah Zaitlen
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA.
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA.
| |
Collapse
|
15
|
Nakano M, Ota M, Takeshima Y, Iwasaki Y, Hatano H, Nagafuchi Y, Itamiya T, Maeda J, Yoshida R, Yamada S, Nishiwaki A, Takahashi H, Takahashi H, Akutsu Y, Kusuda T, Suetsugu H, Liu L, Kim K, Yin X, Bang SY, Cui Y, Lee HS, Shoda H, Zhang X, Bae SC, Terao C, Yamamoto K, Okamura T, Ishigaki K, Fujio K. Distinct transcriptome architectures underlying lupus establishment and exacerbation. Cell 2022; 185:3375-3389.e21. [PMID: 35998627 DOI: 10.1016/j.cell.2022.07.021] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 05/19/2022] [Accepted: 07/22/2022] [Indexed: 12/13/2022]
Abstract
Systemic lupus erythematosus (SLE) is a complex autoimmune disease involving multiple immune cells. To elucidate SLE pathogenesis, it is essential to understand the dysregulated gene expression pattern linked to various clinical statuses with a high cellular resolution. Here, we conducted a large-scale transcriptome study with 6,386 RNA sequencing data covering 27 immune cell types from 136 SLE and 89 healthy donors. We profiled two distinct cell-type-specific transcriptomic signatures: disease-state and disease-activity signatures, reflecting disease establishment and exacerbation, respectively. We then identified candidate biological processes unique to each signature. This study suggested the clinical value of disease-activity signatures, which were associated with organ involvement and therapeutic responses. However, disease-activity signatures were less enriched around SLE risk variants than disease-state signatures, suggesting that current genetic studies may not well capture clinically vital biology. Together, we identified comprehensive gene signatures of SLE, which will provide essential foundations for future genomic and genetic studies.
Collapse
Affiliation(s)
- Masahiro Nakano
- Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan; Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Mineto Ota
- Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan; Department of Functional Genomics and Immunological Diseases, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan
| | - Yusuke Takeshima
- Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan
| | - Yukiko Iwasaki
- Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan
| | - Hiroaki Hatano
- Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan; Laboratory for Human Immunogenetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Yasuo Nagafuchi
- Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan; Department of Functional Genomics and Immunological Diseases, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan
| | - Takahiro Itamiya
- Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan
| | - Junko Maeda
- Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan
| | - Ryochi Yoshida
- Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan
| | - Saeko Yamada
- Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan
| | - Aya Nishiwaki
- Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan
| | - Haruka Takahashi
- Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan
| | - Hideyuki Takahashi
- Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan
| | - Yuko Akutsu
- Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan
| | - Takeshi Kusuda
- Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan
| | - Hiroyuki Suetsugu
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan; Department of Orthopaedic Surgery, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; Department of Orthopaedic Surgery, Hamanomachi hospital, Fukuoka 810-8539, Japan
| | - Lu Liu
- Department of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui 230032, China; Institute of Dermatology, Anhui Medical University, Hefei, Anhui 230032, China
| | - Kwangwoo Kim
- Department of Biology, Kyung Hee University, Seoul 02447, South Korea; Department of Biomedical and Pharmaceutical Sciences, Kyung Hee University, Seoul 02447, South Korea
| | - Xianyong Yin
- Department of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui 230032, China; Institute of Dermatology, Anhui Medical University, Hefei, Anhui 230032, China; Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Human Phenome Institute, Fudan University, Shanghai 201203, China
| | - So-Young Bang
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul 04763, South Korea; Hanyang University Institute of Bioscience and Biotechnology & Hanyang University Institute for Rheumatology Research, Seoul 04763, South Korea
| | - Yong Cui
- Department of Dermatology, China-Japan Friendship Hospital, Beijing 100029, China
| | - Hye-Soon Lee
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul 04763, South Korea; Hanyang University Institute of Bioscience and Biotechnology & Hanyang University Institute for Rheumatology Research, Seoul 04763, South Korea
| | - Hirofumi Shoda
- Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan
| | - Xuejun Zhang
- Department of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui 230032, China; Institute of Dermatology, Anhui Medical University, Hefei, Anhui 230032, China
| | - Sang-Cheol Bae
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul 04763, South Korea; Hanyang University Institute of Bioscience and Biotechnology & Hanyang University Institute for Rheumatology Research, Seoul 04763, South Korea
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan; Clinical Research Center, Shizuoka General Hospital, Shizuoka 420-8527, Japan; The Department of Applied Genetics, The School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka 422-8529, Japan
| | - Kazuhiko Yamamoto
- Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan; Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Tomohisa Okamura
- Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan; Department of Functional Genomics and Immunological Diseases, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan
| | - Kazuyoshi Ishigaki
- Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan; Laboratory for Human Immunogenetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan.
| | - Keishi Fujio
- Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan.
| |
Collapse
|
16
|
The inflammatory signature in monocytes of Sjögren's syndrome and systemic lupus erythematosus, revealed by the integrated Reactome and drug target analysis. Genes Genomics 2022; 44:1215-1229. [PMID: 36040684 DOI: 10.1007/s13258-022-01308-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 08/10/2022] [Indexed: 11/04/2022]
Abstract
BACKGROUND The innate immune regulation, especially by the type I IFN signature in the CD14+ monocytes, is known to be critical in the pathogenesis of autoimmune Sjögren's syndrome (SjS) and systemic lupus erythematosus (SLE). OBJECTIVE Since patients with one condition can be overlapped with another, this study is to identify shared differentially expressed genes (DEGs) in SjS and SLE compared to healthy controls (HCs) and refine transcriptomic profiles with the integrated Reactome and gene-drug network analysis for an anti-inflammation therapy. METHODS CD14+ monocytes were purified from whole blood of SjS and SLE patients (females, ages from 32 to 62) and subject to bulk RNA-sequencing, followed by data analyses for comparison with HC monocytes (females, ages 30 and 33). Functional categorizations, using Gene Ontology (GO) and the Reactome pathway analysis, were performed and DEGs associated with therapeutic drugs were identified from the Drug Repurposing Hub (DHUB) database. RESULTS The GO analysis revealed that DEGs in the inflammatory response and the cellular response to cytokine were highly enriched in both conditions. A propensity toward M1 macrophage differentiation appears to be prominent in SjS while the Response to Virus was significant in SLE monocytes. Through the Reactome pathway analysis, DEGs in the IFN signaling and the cytokine signaling in immune system were most significantly enriched in both. Upregulation of NGF-induced transcription activity in SjS and the complement cascade activity in SLE were also noted. Multiple anti-inflammatory drugs, such as prostaglandin-endoperoxide synthase and angiotensin-I-converting- enzyme were associated with the DEGs in these conditions. CONCLUSIONS Taken together, our analysis indicates distinct inflammatory transcriptomic profiles shared in SjS and SLE monocytes. Comprehensive characterizations of the data from these conditions will ultimately allow differential diagnosis of each condition and identification of therapeutic targets.
Collapse
|
17
|
Recent advances in the use of machine learning and artificial intelligence to improve diagnosis, predict flares, and enrich clinical trials in lupus. Curr Opin Rheumatol 2022; 34:374-381. [PMID: 36001343 DOI: 10.1097/bor.0000000000000902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Machine learning is a computational tool that is increasingly used for the analysis of medical data and has provided the promise of more personalized care. RECENT FINDINGS The frequency with which machine learning analytics are reported in lupus research is comparable with that of rheumatoid arthritis and cancer, yet the clinical application of these computational tools has yet to be translated into better care. Considerable work has been applied to the development of machine learning models for lupus diagnosis, flare prediction, and classification of disease using histology or other medical images, yet few models have been tested in external datasets and independent centers. Application of machine learning has yet to be reported for lupus clinical trial enrichment and automated identification of eligible patients. Integration of machine learning into lupus clinical care and clinical trials would benefit from collaborative development between clinicians and data scientists. SUMMARY Although the application of machine learning to lupus data is at a nascent stage, initial results suggest a promising future.
Collapse
|
18
|
The promise of precision medicine in rheumatology. Nat Med 2022; 28:1363-1371. [PMID: 35788174 PMCID: PMC9513842 DOI: 10.1038/s41591-022-01880-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 05/23/2022] [Indexed: 01/07/2023]
Abstract
Systemic autoimmune rheumatic diseases (SARDs) exhibit extensive heterogeneity in clinical presentation, disease course, and treatment response. Therefore, precision medicine - whereby treatment is tailored according to the underlying pathogenic mechanisms of an individual patient at a specific time - represents the 'holy grail' in SARD clinical care. Current strategies include treat-to-target therapies and autoantibody testing for patient stratification; however, these are far from optimal. Recent innovations in high-throughput 'omic' technologies are now enabling comprehensive profiling at multiple levels, helping to identify subgroups of patients who may taper off potentially toxic medications or better respond to current molecular targeted therapies. Such advances may help to optimize outcomes and identify new pathways for treatment, but there are many challenges along the path towards clinical translation. In this Review, we discuss recent efforts to dissect cellular and molecular heterogeneity across multiple SARDs and future directions for implementing stratification approaches for SARD treatment in the clinic.
Collapse
|
19
|
Ramos‐Mucci L, Sarmiento P, Little D, Snelling S. Research perspectives-Pipelines to human tendon transcriptomics. J Orthop Res 2022; 40:993-1005. [PMID: 35239195 PMCID: PMC9007907 DOI: 10.1002/jor.25315] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 02/23/2022] [Accepted: 03/01/2022] [Indexed: 02/04/2023]
Abstract
Tendon transcriptomics is a rapidly growing field in musculoskeletal biology. The ultimate aim of many current tendon transcriptomic studies is characterization of in vitro, ex vivo, or in vivo, healthy, and diseased tendon microenvironments to identify the underlying pathways driving human tendon pathology. The transcriptome interfaces between genomic, proteomic, and metabolomic signatures of the tendon cellular niche and the response of this niche to stimuli. Some of the greatest bottlenecks in tendon transcriptomics relate to the availability and quality of human tendon tissue, hence animal tissues are frequently used even though human tissue is most translationally relevant. Here, we review the variability associated with human donor and procurement factors, such as whether the tendon is cadaveric or a clinical remnant, and how these variables affect the quality and relevance of the transcriptomes obtained. Moreover, age, sex, and health demographic variables impact the human tendon transcriptome. Tendons present tissue-specific challenges for cell, nuclei, and RNA extraction that include a dense extracellular matrix, low cellularity, and therefore low RNA yield of variable quality. Consideration of these factors is particularly important for single-cell and single-nuclei resolution transcriptomics due to the necessity for unbiased and representative cell or nuclei populations. Different cell, nuclei, and RNA extraction methods, library preparation, and quality control methods are used by the tendon research community and attention should be paid to these when designing and reporting studies. We discuss the different components and challenges of human tendon transcriptomics, and propose pipelines, quality control, and reporting guidelines for future work in the field.
Collapse
Affiliation(s)
- Lorenzo Ramos‐Mucci
- Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal ScienceUniversity of OxfordOxfordUK
| | - Paula Sarmiento
- Department of Biomedical EngineeringPurdue UniversityWest LafayetteIndianaUSA
| | - Dianne Little
- Department of Biomedical EngineeringPurdue UniversityWest LafayetteIndianaUSA,Department of Basic Medical SciencesPurdue UniversityWest LafayetteIndianaUSA
| | - Sarah Snelling
- Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal ScienceUniversity of OxfordOxfordUK
| |
Collapse
|
20
|
Lee MH, Koh JWH, Ng CH, Lim SHH, Cho J, Lateef A, Mak A, Tay SH. A meta-analysis of clinical manifestations in asian systemic lupus erythematous: The effects of ancestry, ethnicity and gender. Semin Arthritis Rheum 2021; 52:151932. [PMID: 34996626 DOI: 10.1016/j.semarthrit.2021.12.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 11/17/2021] [Accepted: 12/06/2021] [Indexed: 11/17/2022]
Abstract
OBJECTIVES Systemic lupus erythematosus (SLE) in Asians is a unique patient group that has been thought to present with more severe organ involvement in comparison to their non-Asian counterparts. We set out to perform a meta-analysis to compare clinical manifestations between ancestries, with a focus on Southeast Asian ethnicities and gender. MATERIALS AND METHODS A cross-sectional study was performed in conjunction with a meta-analysis to identify differences in prevalences of SLE clinical manifestations. Searches were conducted on Medline for articles comparing between: (i) Asian and non-Asian ancestry; (ii) Southeast Asian ethnicities (Chinese, Malay and Indian); and (iii) male and female Asians. Using random effects model, effect sizes as odd ratios were pooled with DerSimonian and Laird's model. RESULTS A total of 13 articles were identified and pooled together with our study for this meta-analysis. Comparing among patients of Asian with Non-Asian/European ancestries, no significance difference was found in severe organ manifestations such as renal and neurological involvement [odds ratio (OR): 1.398, p= 0.320 and OR: 1.224, p= 0.526 respectively]. There was significantly greater proportion of Asian SLE patients with thrombocytopenia compared to non-Asian SLE. Chinese SLE patients were less likely to have oral ulcers compared to Indian SLE patients. Lastly, Asian male SLE patients had greater incidence of renal involvement and thrombocytopenia compared to Asian female SLE patients. CONCLUSIONS Severe SLE manifestations such as renal and neurological involvement were not significantly more frequent in Asian SLE compared to non-Asian/European SLE in this analysis.
Collapse
Affiliation(s)
- Ming Hui Lee
- Department of Biological Sciences, Faculty of Science, National University of Singapore, Singapore
| | - Jeffery Wei Heng Koh
- Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore
| | - Cheng Han Ng
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Sandy H H Lim
- Division of Rheumatology, Department of Medicine, National University Hospital, Singapore
| | - Jiacai Cho
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Division of Rheumatology, Department of Medicine, National University Hospital, Singapore
| | - Aisha Lateef
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Division of Rheumatology, Department of Medicine, National University Hospital, Singapore; Department of Medicine, Woodlands Health Campus, Singapore
| | - Anselm Mak
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Division of Rheumatology, Department of Medicine, National University Hospital, Singapore
| | - Sen Hee Tay
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Division of Rheumatology, Department of Medicine, National University Hospital, Singapore.
| |
Collapse
|
21
|
Transcriptomics data: pointing the way to subclassification and personalized medicine in systemic lupus erythematosus. Curr Opin Rheumatol 2021; 33:579-585. [PMID: 34410228 DOI: 10.1097/bor.0000000000000833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE OF REVIEW To summarize recent studies stratifying SLE patients into subgroups based on gene expression profiling and suggest future improvements for employing transcriptomic data to foster precision medicine. RECENT FINDINGS Bioinformatic & machine learning pipelines have been employed to dissect the transcriptomic heterogeneity of lupus patients and identify more homogenous subgroups. Some examples include the use of unsupervised random forest and k-means clustering to separate adult SLE patients into seven clusters and hierarchical clustering of single-cell RNA-sequencing (scRNA-seq) of immune cells yielding four clusters in a cohort of adult SLE and pediatric SLE participants. Random forest classification of bulk RNA-seq data from sorted blood cells enabled prediction of high or low disease activity in European and Asian SLE patients. Inferred transcription factor activity stratified adult and pediatric SLE into two subgroups. SUMMARY Several different endotypes of SLE patients with differing molecular profiles have been reported but a global consensus of clinically actionable groups has not been reached. Moreover, heterogeneity between datasets, reproducibility of predictions as well as the most effective classification approach have not been resolved. Nevertheless, gene expression-based precision medicine remains an attractive option to subset lupus patients.
Collapse
|