Cotugno, Nicola, Santilli, Veronica, Pascucci, Giuseppe Rubens, Manno, Emma Concetta, De Armas, Lesley, Pallikkuth, Suresh, Deodati, Annalisa, Amodio, Donato, Zangari, Paola, Zicari, Sonia et al (show 7 more authors)
(2020)
Artificial Intelligence Applied to<i>in vitro</i>Gene Expression Testing (IVIGET) to Predict Trivalent Inactivated Influenza Vaccine Immunogenicity in HIV Infected Children.
FRONTIERS IN IMMUNOLOGY, 11.
559590-.
|
Text
Artificial Intelligence Applied to iin vitroi Gene Expression Testing (IVIGET) to Predict Trivalent Inactivated Influenza Va.pdf - Open Access published version Download (2MB) | Preview |
Abstract
The number of patients affected by chronic diseases with special vaccination needs is burgeoning. In this scenario, predictive markers of immunogenicity, as well as signatures of immune responses are typically missing even though it would especially improve the identification of personalized immunization practices in these populations. We aimed to develop a predictive score of immunogenicity to Influenza Trivalent Inactivated Vaccination (TIV) by applying deep machine learning algorithms using transcriptional data from sort-purified lymphocyte subsets after <i>in vitro</i> stimulation. Peripheral blood mononuclear cells (PBMCs) collected before TIV from 23 vertically HIV infected children under ART and virally controlled were stimulated <i>in vitro</i> with p09/H1N1 peptides (stim) or left unstimulated (med). A multiplexed-qPCR for 96 genes was made on fixed numbers of 3 B cell subsets, 3 T cell subsets and total PBMCs. The ability to respond to TIV was assessed through hemagglutination Inhibition Assay (HIV) and ELIspot and patients were classified as Responders (R) and Non Responders (NR). A predictive modeling framework was applied to the data set in order to define genes and conditions with the higher predicted probability able to inform the final score. Twelve NR and 11 R were analyzed for gene expression differences in all subsets and 3 conditions [med, stim or Δ (stim-med)]. Differentially expressed genes between R and NR were selected and tested with the Adaptive Boosting Model to build a prediction score. The score obtained from subsets revealed the best prediction score from 46 genes from 5 different subsets and conditions. Calculating a combined score based on these 5 categories, we achieved a model accuracy of 95.6% and only one misclassified patient. These data show how a predictive bioinformatic model applied to transcriptional analysis deriving from <i>in-vitro</i> stimulated lymphocytes subsets may predict poor or protective vaccination immune response in vulnerable populations, such as HIV-infected individuals. Future studies on larger cohorts are needed to validate such strategy in the context of vaccination trials.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | gene expression, predictive biomarkers, artificial intelligence, deep learning, influenza vaccine, HIV, vaccinomics |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 09 Oct 2023 09:32 |
| Last Modified: | 30 Oct 2023 03:08 |
| DOI: | 10.3389/fimmu.2020.559590 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3173540 |
Dimensions
Dimensions
