Τίτλος:
Patterns of 1,748 Unique Human Alloimmune Responses Seen by Simple
Machine Learning Algorithms
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
Allele specific antibody response against the polymorphic system of HLA
is the allogeneic response marker determining the immunological risk for
graft acceptance before and after organ transplantation and therefore
routinely studied during the patient’s workup. Experimentally, bead
bound antigen- antibody reactions are detected using a special
multicolor flow cytometer (Luminex). Routinely for each sample, antibody
responses against 96 different HLA antigen groups are measured
simultaneously and a 96-dimensional immune response vector is created.
Under a common experimental protocol, using unsupervised clustering
algorithms, we analyzed these immune intensity vectors of anti HLA class
II responses from a dataset of 1,748 patients before or after renal
transplantation residing in a single country. Each patient contributes
only one serum sample in the analysis. A population view of linear
correlations of hierarchically ordered fluorescence intensities reveals
patterns in human immune responses with striking similarities with the
previously described CREGs but also brings new information on the
antigenic properties of class II HLA molecules. The same analysis
affirms that “public” anti-DP antigenic responses are not correlated
to anti DR and anti DQ responses which tend to cluster together.
Principal Component Analysis (PCA) projections also demonstrate ordering
patterns clearly differentiating anti DP responses from anti DR and DQ
on several orthogonal planes. We conclude that a computer vision of
human alloresponse by use of several dimensionality reduction algorithms
rediscovers proven patterns of immune reactivity without any a priori
assumption and might prove helpful for a more accurate definition of
public immunogenic antigenic structures of HLA molecules. Furthermore,
the use of Eigen decomposition on the Immune Response generates new
hypotheses that may guide the design of more effective patient
monitoring tests.
Συγγραφείς:
Vittoraki, Angeliki G.
Fylaktou, Asimina
Tarassi, Katerina and
Tsinaris, Zafeiris
Petasis, George Ch
Gerogiannis, Demetris and
Kheav, Vissal-David
Carmagnat, Maryvonnick
Lehmann, Claudia and
Doxiadis, Ilias
Iniotaki, Aliki G.
Theodorou, Ioannis
Περιοδικό:
Frontiers in Immunology
Εκδότης:
Frontiers Media SA
Λέξεις-κλειδιά:
HLA; patterns detection; allorecognition; transplantation; monitoring;
PCA; descriptive statistics; machine learning
DOI:
10.3389/fimmu.2020.01667
