Τίτλος:
Hidden Patterns of Anti-HLA Class I Alloreactivity Revealed Through
Machine Learning
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
Detection of alloreactive anti-HLA antibodies is a frequent and
mandatory test before and after organ transplantation to determine the
antigenic targets of the antibodies. Nowadays, this test involves the
measurement of fluorescent signals generated through antibody-antigen
reactions on multi-beads flow cytometers. In this study, in a cohort of
1,066 patients from one country, anti-HLA class I responses were
analyzed on a panel of 98 different antigens. Knowing that the immune
system responds typically to “shared” antigenic targets, we studied
the clustering patterns of antibody responses against HLA class I
antigens without any a priori hypothesis, applying two unsupervised
machine learning approaches. At first, the principal component analysis
(PCA) projections of intra-locus specific responses showed that
anti-HLA-A and anti-HLA-C were the most distantly projected responses in
the population with the anti-HLA-B responses to be projected between
them. When PCA was applied on the responses against antigens belonging
to a single locus, some already known groupings were confirmed while
several new cross-reactive patterns of alloreactivity were detected.
Anti-HLA-A responses projected through PCA suggested that three
cross-reactive groups accounted for about 70% of the variance observed
in the population, while anti-HLA-B responses were mainly characterized
by a distinction between previously described Bw4 and Bw6 cross-reactive
groups followed by several yet undocumented or poorly described ones.
Furthermore, anti-HLA-C responses could be explained by two major
cross-reactive groups completely overlapping with previously described
C1 and C2 allelic groups. A second feature-based analysis of all
antigenic specificities, projected as a dendrogram, generated a robust
measure of allelic antigenic distances depicting bead-array defined
cross reactive groups. Finally, amino acid combinations explaining major
population specific cross-reactive groups were described. The
interpretation of the results was based on the current knowledge of the
antigenic targets of the antibodies as they have been characterized
either experimentally or computationally and appear at the HLA epitope
registry.
Συγγραφείς:
Vittoraki, Angeliki G.
Fylaktou, Asimina
Tarassi, Katerina and
Tsinaris, Zafeiris
Siorenta, Alexandra
Petasis, George Ch and
Gerogiannis, Demetris
Lehmann, Claudia
Carmagnat, Maryvonnick
and Doxiadis, Ilias
Iniotaki, Aliki G.
Theodorou, Ioannis
Περιοδικό:
Frontiers in Immunology
Εκδότης:
Frontiers Media SA
Λέξεις-κλειδιά:
machine learning; antigenic epitopes; alloimmune response; translational
research; sensitization; bead array test; anti-HLA alloantibodies
DOI:
10.3389/fimmu.2021.670956
