Nucleotide polymorphism in ARDS outcome: a whole exome sequencing association study

Jing-Yuan Xu, Ai-Ran Liu, Zong-Sheng Wu, Jian-Feng Xie, Xiao-Xiao Qu, Cai-Hua Li, Shan-Shan Meng, Song-Qiao Liu, Cong-Shan Yang, Ling Liu, Ying-Zi Huang, Feng-Mei Guo, Yi Yang, Hai-Bo Qiu, Jing-Yuan Xu, Ai-Ran Liu, Zong-Sheng Wu, Jian-Feng Xie, Xiao-Xiao Qu, Cai-Hua Li, Shan-Shan Meng, Song-Qiao Liu, Cong-Shan Yang, Ling Liu, Ying-Zi Huang, Feng-Mei Guo, Yi Yang, Hai-Bo Qiu

Abstract

Background: Genetic locus were identified associated with acute respiratory distress syndrome (ARDS). Our goal was to explore the associations between genetic variants and ARDS outcome, as well as subphenotypes.

Methods: This was a single-center, prospective observational trial enrolling adult ARDS patients. After baseline data were collected, blood samples were drawn to perform whole exome sequencing, single nucleotide polymorphism (SNP)/insertion-deletion to explore the quantitative and functional associations between genetic variants and ICU outcome, clinical subphenotypes. Then the lung injury burden (LIB), which was defined as the ratio of nonsynonymous SNP number per megabase of DNA, was used to evaluate its value in predicting ARDS outcome.

Results: A total of 105 ARDS patients were enrolled in the study, including 70 survivors and 35 nonsurvivors. Based on the analysis of a total of 65,542 nonsynonymous SNP, LIB in survivors was significantly higher than nonsurvivors [1,892 (1,848-1,942)/MB versus 1,864 (1,829-1,910)/MB, P=0.018], while GO analysis showed that 60 functions were correlated with ARDS outcome, KEGG enrichment analysis showed that SNP/InDels were enriched in 13 pathways. Several new SNPs were found potentially associated with ARDS outcome. Analysis of LIB was used to determine its outcome predicting ability, the area under the ROC curve of which was only 0.6103, and increase to 0.712 when combined with APACHE II score.

Conclusions: Genetic variants are associated with ARDS outcome and subphenotypes; however, their prognostic value still need to be verified by larger trials.

Trial registration: Clinicaltrials.gov NCT02644798. Registered 20 April 2015.

Keywords: Acute respiratory distress syndrome prognosis (ARDS prognosis); single nucleotide polymorphism (SNP); whole-exome sequencing.

Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/atm-20-5728). The authors have no conflicts of interest to declare.

2021 Annals of Translational Medicine. All rights reserved.

Figures

Figure 1
Figure 1
GO analysis showed that the top 30 of the 60 functions were correlated with ARDS outcome. ARDS, acute respiratory distress syndrome; BP, biological process; CC, cellular component; MF, molecular function.
Figure 2
Figure 2
Genome-wide association study showed that several SNPs potentially associated with ARDS outcome. (A) The QQ-plots of the results of whole exome sequencing of ARDS obtained in the analyses. (B) Corresponding Manhattan plots for the same analysis on the left panel. SNP, single nucleotide polymorphism; ARDS, acute respiratory distress syndrome.

References

    1. Fan E, Brodie D, Slutsky AS. Acute respiratory distress syndrome: advances in diagnosis and treatment. JAMA 2018;319:698-710. 10.1001/jama.2017.21907
    1. Bellani G, Laffey JG, Pham T, et al. Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries. JAMA 2016;315:788-800. 10.1001/jama.2016.0291
    1. Reilly JP, Christie JD. Linking genetics to ARDS pathogenesis: the role of the platelet. Chest 2015;147:585-6. 10.1378/chest.14-2701
    1. Reilly JP, Christie JD, Meyer NJ. Fifty years of research in ARDS. Genomic contributions and opportunities. Am J Respir Crit Care Med 2017;196:1113-21. 10.1164/rccm.201702-0405CP
    1. Grigoryev DN, Cheranova DI, Chaudhary S, et al. Identification of new biomarkers for Acute Respiratory Distress Syndrome by expression-based genome-wide association study. BMC Pulm Med 2015;15:95. 10.1186/s12890-015-0088-x
    1. Shortt K, Chaudhary S, Grigoryev D, et al. Identification of novel single nucleotide polymorphisms associated with acute respiratory distress syndrome by exome-seq. PLoS One 2014;9:e111953. 10.1371/journal.pone.0111953
    1. Morrell ED, O'Mahony DS, Glavan BJ, et al. Genetic variation in MAP3K1 associates with ventilator-free days in Acute Respiratory Distress Syndrome. Am J Respir Cell Mol Biol 2018;58:117-25. 10.1165/rcmb.2017-0030OC
    1. Wei Y, Tejera P, Wang Z, et al. A Missense genetic variant in LRRC16A/CARMIL1 improves Acute Respiratory Distress Syndrome survival by attenuating platelet count decline. Am J Respir Crit Care Med 2017;195:1353-61. 10.1164/rccm.201605-0946OC
    1. Dötsch A, Eisele L, Rabeling M, et al. Hypoxia Inducible Factor-2 Alpha and Prolinhydroxylase 2 Polymorphisms in patients with Acute Respiratory Distress Syndrome (ARDS). Int J Mol Sci 2017;18:1266. 10.3390/ijms18061266
    1. Sapru A, Liu KD, Wiemels J, et al. Association of common genetic variation in the protein C pathway genes with clinical outcomes in acute respiratory distress syndrome. Crit Care 2016;20:151. 10.1186/s13054-016-1330-5
    1. Kangelaris KN, Sapru A, Calfee CS, et al. The association between a Darc gene polymorphism and clinical outcomes in African American patients with acute lung injury. Chest 2012;141:1160-9. 10.1378/chest.11-1766
    1. Moss M, Mannino DM. Race and gender differences in acute respiratory distress syndrome deaths in the United States: an analysis of multiple-cause mortality data (1979-1996). Crit Care Med 2002;30:1679-85. 10.1097/00003246-200208000-00001
    1. Erickson SE, Shlipak MG, Martin GS, et al. Racial and ethnic disparities in mortality from acute lung injury. Crit Care Med 2009;37:1-6. 10.1097/CCM.0b013e31819292ea
    1. McKenna A, Hanna M, Banks E, et al. The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 2010;20:1297-303. 10.1101/gr.107524.110
    1. Koboldt D C, Zhang Q, Larson DE, et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res 2012;22:568-76. 10.1101/gr.129684.111
    1. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 2010;38:e164. 10.1093/nar/gkq603
    1. Lawrence MS, Stojanov P, Polak P, et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 2013;499:214-8. 10.1038/nature12213
    1. Matsushita H, Vesely MD, Koboldt DC, et al. Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting. Nature 2012;482:400-4. 10.1038/nature10755
    1. Maleki Vareki S. High and low mutational burden tumors versus immunologically hot and cold tumors and response to immune checkpoint inhibitors. J Immunother Cancer 2018;6:157. 10.1186/s40425-018-0479-7
    1. Yarchoan M, Hopkins A, Jaffee EM. Tumor mutational burden and response rate to PD-1 inhibition. N Engl J Med 2017;377:2500-1. 10.1056/NEJMc1713444
    1. Cristescu R, Mogg R, Ayers M, et al. Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy. Science 2018;362:eaar3593. 10.1126/science.aar3593
    1. Chan TA, Yarchoan M, Jaffee E, et al. Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Ann Oncol 2019;30:44-56. 10.1093/annonc/mdy495
    1. Devarakonda S, Rotolo F, Tsao MS, et al. Tumor mutation burden as a biomarker in resected non-small-cell lung cancer. J Clin Oncol 2018;36:2995-3006. 10.1200/JCO.2018.78.1963

Source: PubMed

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