A patient with the rare alpha-1-antitrypsin variant (Z)bristol in compound heterozygosity with the Z mutation

Distribution of alpha1 antitrypsin rare alleles in six countries: Results from the Progenika diagnostic network

BACKGROUND

Knowledge of the frequency of rare SERPINA1 mutations could help in the management of alpha1 antitrypsin deficiency (AATD). The present study aims to assess the frequencies of rare and null alleles and their respiratory and hepatic pathogenicity.

METHODS

This is a secondary analysis of a study that evaluated the viability of the Progenika diagnostic genotyping system in six different countries by analyzing 30,827 samples from cases of suspected AATD. Allele-specific genotyping was carried out with the Progenika A1AT Genotyping Test which analyses 14 mutations in buccal swabs or dried blood spots samples. SERPINA1 gene sequencing was performed for serum AAT-genotype discrepancies or by request of the clinician. Only cases with rare mutations were included in this analysis.

RESULTS

There were 818 cases (2.6%) carrying a rare allele, excluding newly identified mutations. All were heterozygous except for 20 that were homozygous. The most frequent alleles were the M-like alleles, PI*M_malton and PI*M_heerlen. Of the 14 mutations included in the Progenika panel, there were no cases detected of PI*S_iiyama, PI*Q0_{granite falls} and PI*Q0_west. Other alleles not included in the 14-mutation panel and identified by gene sequencing included PI*M_würzburg, PI*Z_bristol, and PI*Z_wrexham, and the null alleles PI*Q0_porto, PI*Q0_madrid, PI*Q0_brescia, and PI*Q0_kayseri.

CONCLUSIONS

The Progenika diagnostic network has allowed the identification of several rare alleles, some unexpected and not included in the initial diagnostic panel. This establishes a new perspective on the distribution of these alleles in different countries. These findings may help prioritize allele selection for routine testing and highlights the need for further research into their pathogenetic role.

Leveraging Population Genomics for Individualized Correction of the Hallmarks of Alpha-1 Antitrypsin Deficiency

Deep medicine is rapidly moving towards a high-definition approach for therapeutic management of the patient as an individual given the rapid progress of genome sequencing technologies and machine learning algorithms. While considered a monogenic disease, alpha-1 antitrypsin (AAT) deficiency (AATD) patients present with complex and variable phenotypes we refer to as the "hallmarks of AATD" that involve distinct molecular mechanisms in the liver, plasma and lung tissues, likely due to both coding and non-coding variation as well as genetic and environmental modifiers in different individuals. Herein, we briefly review the current therapeutic strategies for the management of AATD. To embrace genetic diversity in the management of AATD, we provide an overview of the disease phenotypes of AATD patients harboring different AAT variants. Linking genotypic diversity to phenotypic diversity illustrates the potential for sequence-specific regions of AAT protein fold design to play very different roles during nascent synthesis in the liver and/or function in post-liver plasma and lung environments. We illustrate how to manage diversity with recently developed machine learning (ML) approaches that bridge sequence-to-function-to-structure knowledge gaps based on the principle of spatial covariance (SCV). SCV relationships provide a deep understanding of the genotype to phenotype transformation initiated by AAT variation in the population to address the role of genetic and environmental modifiers in the individual. Embracing the complexity of AATD in the population is critical for risk management and therapeutic intervention to generate a high definition medicine approach for the patient.