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Whole Exome Sequencing in Acquired Angioedema by Angiotensin-Converting Enzyme Inhibitors: A Pilot Study in Five Patients

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04 December 2024

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05 December 2024

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Abstract
Background/Objectives One of the most common causes of bradykinergic angioedema (Bk-AE) is related to reduced Bk breakdown after the use of certain medications. This is the case of forms of Acquired Angioedema (AAE) due to the use of angiotensin-converting enzyme inhibitors (ACEi), which are used for the treatment of cardiovascular conditions. The AE causes is not clear in these patients. Given the limited number of AAE-ACEi genetic loci identified by genome-wide association studies, here we opted for assessing the utility of NGS of a panel of relevant genes to aim to identify candidate genetic risk factors in severely affected patients. Methods Five Hypertensive patients from unrelated families with clinical AAE-ACEi were included in the study. Whole-exome sequencing, variant calling, and annotation techniques were used. ANNOVAR was used to annotate the variant calls . These variant were processed for each patient by Hereditary Angioedema Database Annotation tool and Franklin genomic platform for variant prioritization and clinical impact interpretation. Results The genetic variant rs6025 in F5 gene was identified in all recruited samples, which has been associated with an increase in blood clotting in AAE-ACEi patients. In two patients, a common synonymous genetic variant of ACE gene was found (rs4343). Finally, we identified the ACE genetic variant rs142947404 in only one patient. This variant has not been assessed in AAE-ACEi. Conclusions More studies will be needed to clarify the genetics involved in acquired forms of Bk-AE. In this way, we will be able to try to predict future episodes of angioedema due to use of ACEi.
Keywords: 
acquired angioedema
;  
bradykinin
;  
ACEi
;  
NGS
;  
exome
Subject: 
Medicine and Pharmacology  -   Immunology and Allergy

Introduction

Bradykinin (Bk)-induced angioedema (Bk-AE) is a medical condition often associated with reduced Bk degradation following the use of specific medications. A prominent example is acquired angioedema (AAE) triggered by angiotensin-converting enzyme inhibitors (ACEi) used as a treatment for cardiovascular conditions, referred to as AAE-ACEi. These medications interfere with the function of angiotensin-converting enzyme, leading to an excessive accumulation of Bk in some patients.[1] AAE-ACEi symptoms can appear at any point during treatment, even after years of otherwise safe use.
Genetic testing in Bk-AE mostly focuses on rare hereditary forms, relying on conventional methods such as Sanger sequencing, Multiplex Ligation-dependent Probe Amplification (MLPA), and PCR. However, these genetic tests are rapidly evolving towards the use of Next-Generation Sequencing (NGS), enabling highly efficient and holistic high-throughput genetic assessments. This transformation has paved the way for the identification of disease-causing variants, even in conditions with small genetic contributions. We have exposed the nuances of Bk-AE, shedding light on the acquired forms, their connection to medication use, and the evolving methods for genetic testing, including Whole Exome Sequencing (WES), as a powerful transformative tool to better understand and precisely manage this multifaceted condition in the patients.[1] Given the limited number of AAE-ACEi genetic loci identified by genome-wide association studies, here we opted for assessing the utility of NGS of a panel of relevant genes to aim to identify candidate genetic risk factors in severely affected patients.

Materials and Methods

  • Patient description
Five Hypertensive patients from unrelated families with clinical AAE-ACEi suspicion residing in the Canary Islands were included in the study (Table 1). The most commonly used drug was enalapril. These patients maintained treatment with ACEi for more than 15 months. The patients had, at least, one life-threatening episode of angioedema. The oropharynx was the most common location. All patients required assistance in the critical care unit. Complement studies were normal in all patients.
  • Whole-exome sequencing, variant calling, and annotation
DNA was extracted from 4 mL of peripheral blood with IllustraTM blood genomicPrep kit (GE Healthcare; Chicago, IL). DNA concentration was evaluated using the dsDNA BroadRange Assay Kit for the Qubit® 3.0 Fluorometer (Termo Fisher Scientific, Waltham, MA). Libraries were prepared using the DNA Prep with Exome 2.0 Plus Enrichment Kit (Illumina, San Francisco, CA), with fragment sizes and concentrations assessed on a TapeStation 4200 (Agilent Technologies, Santa Clara, CA) and sequencing obtained with a NovaSeq 6000 Sequencing System (Illumina, San Francisco, CA) with paired-end 101-base reads. PhiX was loaded and sequenced at 1% as an internal control of the experiments.
Sequencing reads were preprocessed with bcl2fastq v2.18 and mapped to hg19/GRCh37 reference genome with Burrows-Wheeler Aligner v0.7.15, [2] and BAM files were processed with Qualimap v2.2.1, [3] SAMtools v1.3, [4] BEDTools, [5] and Picard v2.10.10 (http://broadinstitute.github.io/picard) for quality control steps. Variant calling of small germline variants was performed using the Genome Analysis Toolkit (GATK) v.3.8 for the detection of nucleotide substitutions (SNVs) and small indels (<50 bp) following the best practices. [6] The pipeline description is publicly available (https:// github.com/genomicsITER/benchmarking/tree/master/WES).
The identified genetic variation was filtered by means of SAMtools and VCFtools based on “PASS” filter, depth of coverage per position (≥ 20×), genotype quality (≥ 100), and mapping quality (≥ 50).
ANNOVAR [7] was used to annotate the variant calls by including the allele frequency in reference populations, gene location, known functional consequences, links with disease based on ClinVar [8] and The Human Gene Mutation Database, [9] and several pathogenicity scores including the Combined Annotation-Dependent Depletion (CADD) in the context of the Mutation Significance Cutoff (MSC), among others. The classification of pathogenic potential of variants was obtained and annotated using InterVar software following the American College of Medical Genetics and Genomics (ACMG) guidelines. [10]
The analysis was carried out at the Teide-HPC Supercomputing facility (http://teidehpc.iter.es/en).
  • Prioritization of potential deleterious variants
The annotated variant calls were processed for each patient by Hereditary Angioedema (HAE) Database Annotation tool (HADA, http://hada.hpc.iter.es/), to rule out undiagnosed HAE. This tool facilitates the identification of the variants affecting function as well as other accompanying information from the literature. [11]
We used the Franklin genomic platform for variant prioritization and clinical impact interpretation as a second tier for identifying genetic factors that could be responsible of AAE-ACEi symptoms. For this approach, we extracted from scientific literature the most common affected genes in AAE-ACEi and designed a virtual gene panel composed by ACE, BDKRB2, XPEPNP2, MME, F5, ETV6, DENND1B, and CRB1. [12] This assessment was done combining the search with a list of phenotypic abnormalities related with Bk-AE (HP:0100665, HP:0025018, HP:0100540, HP:0002098, HP:0040315, HP:0031244, HP:0010783, HP:0002781, HP:0012027, HP:0011855).
  • Patient population and sequencing summary
WES of the five patients yielded an average of 6.58 Gb sequence, with an average of 100% of on-target reads and a median depth of 139.8×, and a transition/transversion ratio in the range of the expected (3.1 to 3.3).

Results and Discussion

HADA did not reveal previously reported HAE causal variants present in these patients, thus reducing the possibility that the symptoms could be due to an undiagnosed HAE. Franklin was able to prioritize interesting likely deleterious variants (Table 2).
Table 2 Prioritized genetic variants in patients with AAE-ACEi.
The genetic variant rs6025 in F5 gene was identified in all recruited samples, which has been associated with an increase in blood clotting in AAE-ACEi patients. [13] Franklin also prioritized the intronic variant rs2786098 in CRB1 gene, likely due to the association between the increased risk of angioedema attacks and ACEi intake in the ONTARGET dataset [12].
In two patients, AM_2498 and AM_2712, a common synonymous genetic variant of ACE gene was found (rs4343). This variant was previously assessed to determine the association with AAE-ACEi, specifically with captopril. [14] In addition, ACE gene is one of the mainly components that inactivate the Bk activity. However, this variant has not been associated with AAE-ACEi, and the different pathogenic scores and allele frequency support it as benign.
Finally, we identified the ACE genetic variant rs142947404 in AM_2450. This variant has not been assessed in AAE-ACEi despite it is included in ClinVar as variant of uncertain significance. [15] Some of the prediction scores such as SIFT and PROVEAN suggest a deleterious effect. The allele frequency in European populations is <0.1%.
More studies will be needed to clarify the genetics involved in acquired forms of Bk-AE. In this way, we will be able to try to predict future episodes of angioedema due to use of ACEi.

Author Contributions

AMA, IMR, and ACa wrote the first draft of the manuscript and designed the table. AMA and IMR performed the statistical analysis. JAMT, EPR, JBR, and ACa contributed to the sample and clinical data collection. JMLS, ACo, and RGM performed the experiments. CF, ACa, and JCGR designed the project. CF, ACa, and IMR obtained funding. All the authors revised and approved the final version of the manuscript.

Funding

This work was supported by the Ministerio de Ciencia e Innovación (RTC-2017-6471-1; AEI/FEDER, UE), co-financed by the European Regional Development Funds ‘A way of making Europe’ from the European Union; Cabildo Insular de Tenerife (CGIEU0000219140); Agreements OA17/008 and OA23/043 with the Instituto Tecnológico y de Energías Renovables (ITER); Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC19/48); and SEAIC Foundation (18_A01). The content of this publication is solely the responsibility of the authors and does not necessarily reflect the views or policies of the funding agencies.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Marcelino I, Callero A, Mendoza-Alvarez A, et al. Bradykinin-mediated angioedema: an update of the genetic causes and the impact of genomics. Front Genet. 2019;10:900. [CrossRef]
  2. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754-1760. https://doi.org/10.1093/bioinformatics/btp324. [CrossRef]
  3. García-Alcalde F, Okonechnikov K, Carbonell J, et al. Qualimap: evaluating next-generation sequencing alignment data. Bioinformatics. 2012;28(20):2678-2679. [CrossRef]
  4. Li H, Handsaker B, Wysoker A, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25(16):2078-2079. [CrossRef]
  5. Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26(6):841-842. [CrossRef]
  6. 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(9):1297-1303. [CrossRef]
  7. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38(16):e164. [CrossRef]
  8. Landrum MJ, Lee JM, Riley GR, et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 2014;42(Database issue):D980-5. [CrossRef]
  9. Stenson PD, Mort M, Ball EV, et al. The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies. Hum Genet. 2017;136(6):665-677. [CrossRef]
  10. Li Q, Wang K. InterVar: Clinical Interpretation of Genetic Variants by the 2015 ACMG-AMP Guidelines. Am J Hum Genet. 2017;100(2):267-280. [CrossRef]
  11. Mendoza-Alvarez A, Muñoz-Barrera A, Rubio-Rodríguez LA, et al. Interactive Web-Based Resource for Annotation of Genetic Variants Causing Hereditary Angioedema (HADA): Database Development, Implementation, and Validation. J Med Internet Res. 2020;22(10):e19040. [CrossRef]
  12. Pare G, Kubo M, Byrd JB, et al. Genetic variants associated with angiotensin-converting enzyme inhibitor-associated angioedema. Pharmacogenet Genomics. 2013;23(9):470-478. [CrossRef]
  13. Maroteau C, Kalhan Siddiqui M, Veluchamy A, et al. Exome sequencing reveals common and rare variants in F5 associated with ACE inhibitor and ARB induced angioedema. Clin Pharmacol Ther. Published online June 4, 2020. [CrossRef]
  14. Ghafil FA, Mohammad BI, Al-Janabi HS, Hadi NR, Al-Aubaidy HA. Genetic polymorphism of angiotensin converting enzyme and angiotensin II type 1 receptors and their impact on the outcome of acute coronary syndrome. Genomics. 2020;112(1):867-872. [CrossRef]
  15. National Center for Biotechnology Information. ClinVar; [VCV000324414.10]. https://www.ncbi.nlm.nih.gov/clinvar/variation/VCV000324414.10 (accessed Aug. 29, 2023).
Table 1. Patients data collection.
Table 1. Patients data collection.
ID Sex Age C4 mg/dl C1q mg/dl Drug HTA Diagnosis Onset of ACEI Onset of AE (months) Localization
AM_2450 Male 64 25,5 18 Enalapril 2014 2014 47 Oropharynx
AM_2419 Male 43 26,1 19 Enalapril 2016 2016 27 Oropharynx
AM_2498 Male 62 24,3 21 Enalapril 2010 2010 82 Oropharynx, lips
AM_2569 Male 49 28 17 Ramipril 2019 2020 16 Oropharynx, lips
AM_2712 Female 90 27,2 ND Perindopril 2010 2012 78 Oropharynx
ID: Identification number; ND: No data; ACEI: Angiotensin-converting enzyme inhibitors; AE: Angioedema.
Table 2. Prioritized genetic variants in patients with AAE-ACEi.
Table 2. Prioritized genetic variants in patients with AAE-ACEi.
Individual ID Gene RS ID Chr Position start-end Reference allele Alternative allele Total depth (ref/alt) HGVS coding HGVS protein ACMG class ACMG criteria GnomAD CADD Phred score MSC 95% HGMD Predicted effect*
AM_2419 F5 rs6025 1 169,519,049 - 169,519,049 C T 156 (0 / 156) c.1601C>T p.Gln534Arg Likely pathogenic PM1 (moderate), PM5 (moderate), PM2 (supporting) Absent 0.255 12.286 Increased blood clotting13
AM_2450 124 (0 / 124)
AM_2498 145 (75 / 70)
AM_2569 121 (0 / 121)
AM_2712 125 (0 / 125)
AM_2419 CRB1 rs2786098 1 197,325,908 - 197,325,908 T G 101 (0 / 101) c.989-53T>G none (intronic) Benign BA1 (stand alone), BP4 (strong), BP6 (moderate) 0.7967 0.446 11.653 Higher risk of ACEi induced angioedema12
AM_2450 117 (0 / 117)
AM_2498 96 (1 / 95)
AM_2569 87 (47 / 40)
AM_2712 102 (0 / 102)
AM_2498 ACE rs4343 17 61,566,031 - 61,566,031 G A 213 (138 / 75) c.2328G>A p.Thr776= Benign BA1 (stand alone), BP6 (very strong), BP4 (strong), BP7 (supporting) 0.4727 0.083 0.177 Increased ACE activity14
AM_2712 206 (136 / 70)
AM_2450 ACE rs142947404 17 61,570,992 - 61,570,992 C A 88 (39 / 49) c.3108C>A p.Asn1036Lys Uncertain significance BP4 (strong), BP1 (supporting), PM2 (supporting) 0.0009597 22.7 0.177 Not reported in the scientific literature15
ACMG: American College of Medical Genetics and Genomics; CADD: Combined Annotation Dependent Depletion; GnomAD: Genome Aggregation Database; HGDM: Human Gene Mutation Database; HGVS: Human Genome Variation Society; MSC: Mutation Significance Cutoff.
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