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Tervi A, Junna N, Broberg M, Jones SE, Strausz S, Kreivi HR, Heckman CA, Ollila HM. Large registry-based analysis of genetic predisposition to tuberculosis identifies genetic risk factors at HLA. Hum Mol Genet 2023; 32:161-171. [PMID: 36018815 PMCID: PMC9838093 DOI: 10.1093/hmg/ddac212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 07/27/2022] [Accepted: 08/23/2022] [Indexed: 01/19/2023] Open
Abstract
Tuberculosis is a significant public health concern resulting in the death of over 1 million individuals each year worldwide. While treatment options and vaccines exist, a substantial number of infections still remain untreated or are caused by treatment resistant strains. Therefore, it is important to identify mechanisms that contribute to risk and prognosis of tuberculosis as this may provide tools to understand disease mechanisms and provide novel treatment options for those with severe infection. Our goal was to identify genetic risk factors that contribute to the risk of tuberculosis and to understand biological mechanisms and causality behind the risk of tuberculosis. A total of 1895 individuals in the FinnGen study had International Classification of Diseases-based tuberculosis diagnosis. Genome-wide association study analysis identified genetic variants with statistically significant association with tuberculosis at the human leukocyte antigen (HLA) region (P < 5e-8). Fine mapping of the HLA association provided evidence for one protective haplotype tagged by HLA DQB1*05:01 (P = 1.82E-06, OR = 0.81 [CI 95% 0.74-0.88]), and predisposing alleles tagged by HLA DRB1*13:02 (P = 0.00011, OR = 1.35 [CI 95% 1.16-1.57]). Furthermore, genetic correlation analysis showed association with earlier reported risk factors including smoking (P < 0.05). Mendelian randomization supported smoking as a risk factor for tuberculosis (inverse-variance weighted P < 0.05, OR = 1.83 [CI 95% 1.15-2.93]) with no significant evidence of pleiotropy. Our findings indicate that specific HLA alleles associate with the risk of tuberculosis. In addition, lifestyle risk factors such as smoking contribute to the risk of developing tuberculosis.
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Affiliation(s)
- Anniina Tervi
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Nella Junna
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Martin Broberg
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Samuel E Jones
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | | | - Satu Strausz
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Hanna-Riikka Kreivi
- Department of Pulmonology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Caroline A Heckman
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Hanna M Ollila
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.,Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.,Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
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Ma Y, Qiu F, Deng C, Li J, Huang Y, Wu Z, Zhou Y, Zhang Y, Xiong Y, Yao Y, Zhong Y, Qu J, Su J. Integrating single-cell sequencing data with GWAS summary statistics reveals CD16+monocytes and memory CD8+T cells involved in severe COVID-19. Genome Med 2022; 14:16. [PMID: 35172892 PMCID: PMC8851814 DOI: 10.1186/s13073-022-01021-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 02/06/2022] [Indexed: 02/08/2023] Open
Abstract
Background Understanding the host genetic architecture and viral immunity contributes to the development of effective vaccines and therapeutics for controlling the COVID-19 pandemic. Alterations of immune responses in peripheral blood mononuclear cells play a crucial role in the detrimental progression of COVID-19. However, the effects of host genetic factors on immune responses for severe COVID-19 remain largely unknown. Methods We constructed a computational framework to characterize the host genetics that influence immune cell subpopulations for severe COVID-19 by integrating GWAS summary statistics (N = 969,689 samples) with four independent scRNA-seq datasets containing healthy controls and patients with mild, moderate, and severe symptom (N = 606,534 cells). We collected 10 predefined gene sets including inflammatory and cytokine genes to calculate cell state score for evaluating the immunological features of individual immune cells. Results We found that 34 risk genes were significantly associated with severe COVID-19, and the number of highly expressed genes increased with the severity of COVID-19. Three cell subtypes that are CD16+monocytes, megakaryocytes, and memory CD8+T cells were significantly enriched by COVID-19-related genetic association signals. Notably, three causal risk genes of CCR1, CXCR6, and ABO were highly expressed in these three cell types, respectively. CCR1+CD16+monocytes and ABO+ megakaryocytes with significantly up-regulated genes, including S100A12, S100A8, S100A9, and IFITM1, confer higher risk to the dysregulated immune response among severe patients. CXCR6+ memory CD8+ T cells exhibit a notable polyfunctionality including elevation of proliferation, migration, and chemotaxis. Moreover, we observed an increase in cell-cell interactions of both CCR1+ CD16+monocytes and CXCR6+ memory CD8+T cells in severe patients compared to normal controls among both PBMCs and lung tissues. The enhanced interactions of CXCR6+ memory CD8+T cells with epithelial cells facilitate the recruitment of this specific population of T cells to airways, promoting CD8+T cell-mediated immunity against COVID-19 infection. Conclusions We uncover a major genetics-modulated immunological shift between mild and severe infection, including an elevated expression of genetics-risk genes, increase in inflammatory cytokines, and of functional immune cell subsets aggravating disease severity, which provides novel insights into parsing the host genetic determinants that influence peripheral immune cells in severe COVID-19. Supplementary Information The online version contains supplementary material available at 10.1186/s13073-022-01021-1.
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Affiliation(s)
- Yunlong Ma
- Institute of Biomedical Big Data, School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, China
| | - Fei Qiu
- Institute of Biomedical Big Data, School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, China
| | - Chunyu Deng
- Institute of Biomedical Big Data, School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, China
| | - Jingjing Li
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang University School of Medicine, Zhejiang, 310003, Hangzhou, China
| | - Yukuan Huang
- Institute of Biomedical Big Data, School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, China
| | - Zeyi Wu
- Institute of Biomedical Big Data, School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, China
| | - Yijun Zhou
- Institute of Biomedical Big Data, School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, China
| | - Yaru Zhang
- Institute of Biomedical Big Data, School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, China
| | - Yichun Xiong
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325011, China
| | - Yinghao Yao
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325011, China
| | - Yigang Zhong
- Department of Cardiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jia Qu
- Institute of Biomedical Big Data, School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, China
| | - Jianzhong Su
- Institute of Biomedical Big Data, School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, China. .,Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325011, China.
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Xiang B, Deng C, Qiu F, Li J, Li S, Zhang H, Lin X, Huang Y, Zhou Y, Su J, Lu M, Ma Y. Single cell sequencing analysis identifies genetics-modulated ORMDL3 + cholangiocytes having higher metabolic effects on primary biliary cholangitis. J Nanobiotechnology 2021; 19:406. [PMID: 34872583 PMCID: PMC8647381 DOI: 10.1186/s12951-021-01154-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Accepted: 11/21/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Primary biliary cholangitis (PBC) is a classical autoimmune disease, which is highly influenced by genetic determinants. Many genome-wide association studies (GWAS) have reported that numerous genetic loci were significantly associated with PBC susceptibility. However, the effects of genetic determinants on liver cells and its immune microenvironment for PBC remain unclear. RESULTS We constructed a powerful computational framework to integrate GWAS summary statistics with scRNA-seq data to uncover genetics-modulated liver cell subpopulations for PBC. Based on our multi-omics integrative analysis, 29 risk genes including ORMDL3, GSNK2B, and DDAH2 were significantly associated with PBC susceptibility. By combining GWAS summary statistics with scRNA-seq data, we found that cholangiocytes exhibited a notable enrichment by PBC-related genetic association signals (Permuted P < 0.05). The risk gene of ORMDL3 showed the highest expression proportion in cholangiocytes than other liver cells (22.38%). The ORMDL3+ cholangiocytes have prominently higher metabolism activity score than ORMDL3- cholangiocytes (P = 1.38 × 10-15). Compared with ORMDL3- cholangiocytes, there were 77 significantly differentially expressed genes among ORMDL3+ cholangiocytes (FDR < 0.05), and these significant genes were associated with autoimmune diseases-related functional terms or pathways. The ORMDL3+ cholangiocytes exhibited relatively high communications with macrophage and monocyte. Compared with ORMDL3- cholangiocytes, the VEGF signaling pathway is specific for ORMDL3+ cholangiocytes to interact with other cell populations. CONCLUSIONS To the best of our knowledge, this is the first study to integrate genetic information with single cell sequencing data for parsing genetics-influenced liver cells for PBC risk. We identified that ORMDL3+ cholangiocytes with higher metabolism activity play important immune-modulatory roles in the etiology of PBC.
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Affiliation(s)
- Bingyu Xiang
- Department of Infectious Diseases, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Chunyu Deng
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150080, China
| | - Fei Qiu
- Institute of Biomedical Big Data, School of Ophthalmology and Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Jingjing Li
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang University School of Medicine, Hangzhou, 310003, Zhejiang, China
| | - Shanshan Li
- Department of Infectious Diseases, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Huifang Zhang
- Department of Infectious Diseases, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Xiuli Lin
- Department of Infectious Diseases, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Yukuan Huang
- Institute of Biomedical Big Data, School of Ophthalmology and Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Yijun Zhou
- Institute of Biomedical Big Data, School of Ophthalmology and Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Jianzhong Su
- Institute of Biomedical Big Data, School of Ophthalmology and Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China.,Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325011, Zhejiang, China
| | - Mingqin Lu
- Department of Infectious Diseases, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
| | - Yunlong Ma
- Institute of Biomedical Big Data, School of Ophthalmology and Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China.
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